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Wednesday, January 29
 

9:00am IST

Registration with Networking Tea / Coffee & Cookies
Wednesday January 29, 2025 9:00am - 9:30am IST
Wednesday January 29, 2025 9:00am - 9:30am IST
Magnolia Hotel Crowne Plaza, Pune, India

9:30am IST

Opening Ceremony – Lighting the Lamp
Wednesday January 29, 2025 9:30am - 9:35am IST
Wednesday January 29, 2025 9:30am - 9:35am IST
Magnolia Hotel Crowne Plaza, Pune, India

9:35am IST

Welcome Remarks By
Wednesday January 29, 2025 9:35am - 9:45am IST
Invited Guest/Session Chair
avatar for Dr. Amit Joshi

Dr. Amit Joshi

Conference Chair, SmartCom 2025, Director, Global Knowledge Research Foundation, India
Wednesday January 29, 2025 9:35am - 9:45am IST
Magnolia Hotel Crowne Plaza, Pune, India

9:45am IST

Address By Special Guest
Wednesday January 29, 2025 9:45am - 9:55am IST
Invited Guest/Session Chair
avatar for Dr. Parikshit N. Mahalle

Dr. Parikshit N. Mahalle

Professor and Head, Vishwakarma Institute of Information Technology, Pune, India
Wednesday January 29, 2025 9:45am - 9:55am IST
Magnolia Hotel Crowne Plaza, Pune, India

9:55am IST

Address By Special Guest and Speaker
Wednesday January 29, 2025 9:55am - 10:05am IST
Invited Guest/Session Chair
avatar for Dr. Tarun Kumar Sharma

Dr. Tarun Kumar Sharma

Professor & Research Mentor - CSE, School of Engineering & Technology, Shobhit University Gangoh, India
Wednesday January 29, 2025 9:55am - 10:05am IST
Magnolia Hotel Crowne Plaza, Pune, India

10:05am IST

Address By Special Guest and Speaker
Wednesday January 29, 2025 10:05am - 10:15am IST
Invited Guest/Session Chair
avatar for Dr. Nilanjan Dey

Dr. Nilanjan Dey

Professor, Techno International New Town, India
Wednesday January 29, 2025 10:05am - 10:15am IST
Magnolia Hotel Crowne Plaza, Pune, India

10:15am IST

Address By Special Guest and Speaker
Wednesday January 29, 2025 10:15am - 10:25am IST
Invited Guest/Session Chair
avatar for Dr. Jagdish C. Bansal

Dr. Jagdish C. Bansal

Associate Professor, South Asian University, India
Wednesday January 29, 2025 10:15am - 10:25am IST
Magnolia Hotel Crowne Plaza, Pune, India

10:25am IST

Address By Special Invited Guest
Wednesday January 29, 2025 10:25am - 10:35am IST
Invited Guest/Session Chair
avatar for Dr. Ketan Kotecha

Dr. Ketan Kotecha

Dean, Symbiosis International University, India
Wednesday January 29, 2025 10:25am - 10:35am IST
Magnolia Hotel Crowne Plaza, Pune, India

10:35am IST

Address By Special Invited Guest
Wednesday January 29, 2025 10:35am - 10:45am IST
Invited Guest/Session Chair
avatar for Dr. A. V. Deshpande

Dr. A. V. Deshpande

Principal, Smt Kashibai Navale Sinhgad College of Engineering (SKNCOE), India
Wednesday January 29, 2025 10:35am - 10:45am IST
Magnolia Hotel Crowne Plaza, Pune, India

10:45am IST

Address By Special Invited Guest
Wednesday January 29, 2025 10:45am - 10:55am IST
Invited Guest/Session Chair
avatar for Mr. Aninda Bose

Mr. Aninda Bose

Executive Editor, Springer Nature Group, United Kingdom
Wednesday January 29, 2025 10:45am - 10:55am IST
Magnolia Hotel Crowne Plaza, Pune, India

10:55am IST

Address By Special Invited Guest
Wednesday January 29, 2025 10:55am - 11:05am IST
Invited Guest/Session Chair
avatar for Dr. Rajesh Jalnekar

Dr. Rajesh Jalnekar

Director, Vishwakarma Institute of Technology, India
Wednesday January 29, 2025 10:55am - 11:05am IST
Magnolia Hotel Crowne Plaza, Pune, India

11:05am IST

Address By Guest of Honour and Keynote Speaker
Wednesday January 29, 2025 11:05am - 11:20am IST
Invited Guest/Session Chair
avatar for Dr. Milan Tuba

Dr. Milan Tuba

Head - Artificial Intelligence Project, Singidunum University & Vice-Rector of Research at Sinergija University, Serbia
Wednesday January 29, 2025 11:05am - 11:20am IST
Magnolia Hotel Crowne Plaza, Pune, India

11:20am IST

Vote of Appreciation
Wednesday January 29, 2025 11:20am - 11:25am IST
Wednesday January 29, 2025 11:20am - 11:25am IST
Magnolia Hotel Crowne Plaza, Pune, India

11:25am IST

Felicitation & Conference Group Photograph
Wednesday January 29, 2025 11:25am - 11:30am IST
Wednesday January 29, 2025 11:25am - 11:30am IST
Magnolia Hotel Crowne Plaza, Pune, India

12:00pm IST

Review on recent trends and technologies of Precision Agriculture
Wednesday January 29, 2025 12:00pm - 12:15pm IST
Authors - Vandana R. Babrekar, Sandeep V. Gaikwad
Abstract - This paper presents a literature review of current state of knowledge and innovation related to precision agriculture (PA) technologies leveraging Internet of Things (IoT), Artificial intelligence (AI), Computer vision (CV), Machine learning (ML), and Deep learning (DL). The review provides a thorough understanding of the various developments made in the above-mentioned technologies in precision farming. It also states several challenges after reviewing research publications, and it also recommends thrust areas for future studies.
Paper Presenter
Wednesday January 29, 2025 12:00pm - 12:15pm IST
Magnolia Hotel Crowne Plaza, Pune, India

12:00pm IST

Enhancing Software Robustness: A Comprehensive Guide to Quality Attributes and Optimization Tools
Wednesday January 29, 2025 12:00pm - 12:15pm IST
Authors - Bimal Patel, Jalpesh Vasa, Ravi Patel, Martin Parmar, Krunal Maheriya
Abstract - Software robustness, a vital component of software quality, encompasses key attributes such as reliability, usability, efficiency, maintainability, and portability. This paper offers a comprehensive overview of these attributes and examines the role of optimization tools in enhancing software robustness. Reliability, which ensures a system’s consistent dependability, is achieved through techniques like redundancy, error handling, and extensive testing. Usability, focusing on the user experience, is improved through user-centered design, usability testing, and heuristic evaluation. Efficiency targets the optimal use of system resources such as CPU and memory, with performance profiling, load testing, and code optimization techniques helping identify and resolve bottlenecks. Maintainability, ensuring that systems can be easily modified or updated, is enhanced through modularity, code readability, and design patterns that simplify future changes. Portability, which allows software to operate across diverse platforms, is achieved through cross-platform frameworks and containerization technologies such as Docker and Kubernetes. Optimization tools, including profilers, load testing tools, static code analyzers, and dependency management tools, play a critical role in maintaining software robustness. These tools help identify performance issues, ensure resource efficiency, and improve code quality. By leveraging these tools, developers and project managers can build more reliable, efficient, and maintainable software systems. This paper serves as a valuable resource for improving the overall quality, resilience, and portability of software products.
Paper Presenter
Wednesday January 29, 2025 12:00pm - 12:15pm IST
Maple Hotel Crowne Plaza, Pune, India

12:00pm IST

Exploring the Determinants of NFT usage intention Among Gen Z and Millennials: A UTAUT2 Model Approach
Wednesday January 29, 2025 12:00pm - 12:15pm IST
Authors - Sehaj Preet Kaur, Rahul Chaudhary, Rashmy Moray, Shikha Jain, Sridevi Chennamsetti, Harsha Thorve
Abstract - This study aims to investigate the factors affecting the usage intent of Non-Fungible Tokens (NFTs) and its adoption among Gen Z and Millennials. Purposive sampling technique was used Using structured questionnaire, primary data was gathered and statistical tool SEM using SmartPLS was employed to assess the influence of factors under UTAUT2 model on usage intention. The findings reveal that users are inclined to adopt NFTs when perceived as easy-to-use and hassle-free. Likelihood of adopting is further facilitated with some conditions like adequate resources and support. Interestingly, habituality toward traditional digital assets had diverted effort from the steepness toward NFTs while hedonic motivation shows a lack of inclination for novelty over utility. Besides, performance expectancy and social influence play a big role, while the perceived cost acts as a showstopper. The study contributes to the existing body of knowledge and the stakeholder to estimates NFTs intent use.
Paper Presenter
Wednesday January 29, 2025 12:00pm - 12:15pm IST
Tulip Hotel Crowne Plaza, Pune, India

12:15pm IST

Miniaturized UWB MIMO Antenna with Improved Isolation for Wireless Communications
Wednesday January 29, 2025 12:15pm - 12:30pm IST
Authors - Killol Pandya, Trushit Upadhyaya, Aneri Pandya, Upesh Patel, Poonam Thanki, Kanwarpreet Kaur, Jinesh Varma
Abstract - The 2x2 Ultra Wide Band(UWB) MIMO antenna model with 31 x 26 x 1.56 mm3 are designed and presented. The proposed antenna system consists of two primary radiators over the surface layer and positioned with sufficient space between each other to receive adequate response. The conducting patches include U shaped and inverse U shaped slots to increase the current traveling surface. The bottom corners of the patches are having fractional geometry and supporting arms associated with microstrip line to offer resonances at desired frequencies. The Ultra Wide band characteristics are reported due to partial ground plane geometry. The additional strip with slots are deployed with ground plane to receive the satisfactory isolation as the radiators create mutual effect on the other ones which turns into performance degradation. For UWB, the structure exhibits gain of around 2 dB and around a bandwidth of 85% which shows the applicability of discussed research. The other diversity parameters such as Mean Effective Gain(MEG), Gain diversity, Channel Capacity Loss and Envelope Correlation parameter are analyzed and presented. The proposed antenna is well appropriate for WiMAX, WLAN and C band applications.
Paper Presenter
Wednesday January 29, 2025 12:15pm - 12:30pm IST
Magnolia Hotel Crowne Plaza, Pune, India

12:15pm IST

Digitization of Product Manual Using Augmented Reality
Wednesday January 29, 2025 12:15pm - 12:30pm IST
Authors - Siddhesh Joshi, Manoj Naidu, Athrva Kulkarni, Sahil Kadam, Nilesh P. Sable, Pranjal Pandit
Abstract - Artificial Intelligence (AI) is a new type of experience that uses computer-generated content to augment the Real World (RW). An emerging type of experience known as augmented reality (AR) involves adding computer generated content to the real world (RW) that is connected to certain places and/or activities. AR is starting to show up in audio-visual media (news, entertainment, sports, etc.) and is starting to make a real and exciting appearance in other areas of lives (e-commerce, travel, marketing, etc.) [2]. This paper proposes the development of a marker-based AR system that overlays interactive 3D models of industrial machinery onto real-world views. The proposed project will be based on Unity software, which will make it feasible to present complex industrial equipment and move away from typical product manuals that are only text-laden. Users can intuitively see and interact with different parts of their machinery during maintenance, troubleshooting, or training through the AR-based system. This will probably ensure more user interaction with less learning time for a technical operation and is, therefore, most beneficial for the industries that heavily depend on machineries' setups and maintenance. This paper describes methodology, and the probable effects of the marker-based AR digitizing product manuals with first-hand observation in the future of digital documentation across industrial life.
Paper Presenter
Wednesday January 29, 2025 12:15pm - 12:30pm IST
Maple Hotel Crowne Plaza, Pune, India

12:15pm IST

Advancing the Medical Image Registration Pipeline: Challenges and Future Directions
Wednesday January 29, 2025 12:15pm - 12:30pm IST
Authors - Alka Beniwal, Trishna Paul, Mukesh Kumar Rohil
Abstract - In the rapidly changing landscape of daily life, medical imaging stands out as a significant and novel component, significantly impacting healthcare practices. The efficiency of medical imaging processes is pivotal, and within this realm, accurate image registration emerges as a key contributor. Despite its recognized importance, existing pipelines lack a definitive and well-defined structure tailored to the specific requirements of medical imaging. This paper exclusively directs its focus toward addressing this gap by thoroughly examining and redefining the image registration pipeline within the context of medical imaging. The objective is to enhance the efficiency of medical imaging procedures by establishing a tailored and comprehensive pipeline that aligns seamlessly with the unique demands of this critical domain. We also analyzed the different application areas of image registration in medical imaging with their benefits, challenges, and future directions.
Paper Presenter
Wednesday January 29, 2025 12:15pm - 12:30pm IST
Tulip Hotel Crowne Plaza, Pune, India

12:30pm IST

Advancements in Object Attributes Recognition Using Deep Learning
Wednesday January 29, 2025 12:30pm - 12:45pm IST
Authors - V V N Sai Rajeshwar, Gangalam Sumanth
Abstract - Attributes like size, shape, color, quantity, quality, etc. are very hard to find in an image of a particular object. There are various ways to process an image and derive attributes namely – Faster R – CNN, SSD (Single Shot Detector), SPP (Spatial Pyramid Pooling) - Net. Many modern technologies use YOLOv8, the 8th version of YOLO (You Only Look Once), which is quite accurate and detects real - time objects. To enhance the proficiency and scalability of the model Deep Convolutional Neural Networks (DCNN) were used. This paper highlights a very unique strategy to enhance the abilities of YOLO with the help of dynamic masking and also advances the searching and findings of attributes in a unique way. The use of Compute Unified Device Architecture (CUDA) which uses GPUs rather than CPU, made it convenient to run on a higher loads of data. After masking an image or stream of images, detecting colors is easier, and expressing dominant colors is the motive of this research. Availing Coordinates of specified images in a particular image or stream of images is very helpful in various surveillance-related actions like military, and navy, and also useful in domestic purposes.
Paper Presenter
Wednesday January 29, 2025 12:30pm - 12:45pm IST
Magnolia Hotel Crowne Plaza, Pune, India

12:30pm IST

Mitigating Algorithmic Bias in Facial Recognition Technology: A Deep Learning Approach to Fairness in AI
Wednesday January 29, 2025 12:30pm - 12:45pm IST
Authors - Ashita C. Kolla, Dattatray G. Takale, Parikshit N. Mahalle, Bipin Sule, Gopal Deshmukh
Abstract - The research paper mainly focuses on algorithmic bias in facial recognition technology using parameters like race and hairstyle. It involves a CNN model following the pre-processing step of the data and custom annotation. It further talks about advanced methods for dataset balancing, such as normalization and sampling, along with detailed annotations involving characteristics of different races and hairstyles. Compared to other models, the CNN model contains powerful feature extraction methods and other bias mitigation methods such as adversarial training and annotation to enhance the chance of predictions. The results reveal that the model has made significant progress with good performance and lesser bias. This study helps the industry develop more reliable FRT systems with effective strategies for reducing bias and maintaining accuracy. These advancements are important for applications in various industries, where unbiased facial recognition is important for fairness and effectiveness.
Paper Presenter
Wednesday January 29, 2025 12:30pm - 12:45pm IST
Maple Hotel Crowne Plaza, Pune, India

12:30pm IST

Surface Electromyography-Based Prosthetic Hand Control: A Comprehensive Review of Signal Detection and Analysis Techniques
Wednesday January 29, 2025 12:30pm - 12:45pm IST
Authors - Shashwat Avhad, Nikhil Chavan, Lalit Patil
Abstract - An extensive overview of the surface electromyography (sEMG) methods for signal processing and their application to prosthetic hand control is given in this paper's abstract. Techniques for analyzing muscle activity to enable natural and accurate movement of the hands in prosthetic have advanced immensely as a result of growing interest in sEMG-based devices. This article discusses various techniques, such as wavelet transformation, machine learning-based algorithms, and time- and frequency-domain approaches, for feature extraction and classification from sEMG data. It also looks at how deep learning models have recently been included, and how it has helped to increase the precision and stability of sEMG signal classification. In addition, hybrid models that combine traditional statistical techniques with neural networks are investigated for their potential to improve prosthetic control precision and adaptability. The study tackles typical real-time signal recognition problems, like noise reduction and multi-degree freedom movement control management. The review's conclusion highlights the need for more study on multi-modal systems that use machine learning and sophisticated signal processing in order to enhance the usability and reliability of prosthetic devices.
Paper Presenter
Wednesday January 29, 2025 12:30pm - 12:45pm IST
Tulip Hotel Crowne Plaza, Pune, India

12:45pm IST

Multiparty Computation for Privacy-preserving Communication in the Smart Grid
Wednesday January 29, 2025 12:45pm - 1:00pm IST
Authors - Priya Deokar, Sandhya Arora
Abstract - Multiparty Computation (MPC) allows users to use their private inputs to compute a function without revealing anything about the inputs. This feature is useful in computing the aggregation of smart meter readings. The smart grid provides bi-directional communication between the smart meter and the utility supplier. Periodic sharing of fine-grained information by a smart meter presents a greater danger to privacy. Secure multiparty computing is one of the most efficient ways to preserve privacy among users. This paper briefly overviews MPC techniques and their usage in Smart Grid communication.
Paper Presenter
Wednesday January 29, 2025 12:45pm - 1:00pm IST
Magnolia Hotel Crowne Plaza, Pune, India

12:45pm IST

Agile Transformation in the Gaming Sector: The Role of Riot Games Case Study in Shaping Industry Competitiveness
Wednesday January 29, 2025 12:45pm - 1:00pm IST
Authors - Shrivardhansinh Jadeja, Bimal Patel, Jalpesh Vasa
Abstract - The evolution of software development methodologies has profoundly influenced the gaming industry, marking a transition from traditional approaches to agile frameworks that emphasize flexibility and responsiveness. Traditional methodologies often struggled to meet the rapidly changing demands of game design and player expectations, leading to extended development cycles and reduced competitiveness. In contrast, agile methodologies have emerged as a viable solution, focusing on iterative development, continuous feedback, and collaboration among cross-functional teams. This paradigm shift has enabled companies like Riot Games to significantly enhance operational efficiency and swiftly respond to market dynamics. This research paper investigates Riot Games' agile transformation as a case study, illustrating how the adoption of agile practices has fortified its competitive advantage over alternative players in the gaming sector. By examining Riot's innovative team structures, leadership models, and iterative development processes, this study elucidates the critical role of agility in fostering creativity, improving product quality, and accelerating time-to-market. The findings highlight the necessity of embracing agile methodologies not only for individual organizations but also for the broader gaming industry seeking sustainability and growth in an increasingly competitive landscape. Ultimately, this paper offers valuable insights into how agile transformation can act as a catalyst for success in game development, providing a framework for other companies aiming to enhance their competitive positioning.
Paper Presenter
Wednesday January 29, 2025 12:45pm - 1:00pm IST
Maple Hotel Crowne Plaza, Pune, India

12:45pm IST

Chip Implementation of Low Power Encryption using RSA Algorithm
Wednesday January 29, 2025 12:45pm - 1:00pm IST
Authors - Pranav Indurkar, Mansi Dangade, Apoorva Kumar, Harsh Thakar
Abstract - This paper explores the chip implementation of a low-power RSA encryption system, optimizing resource and Power usage while maintaining the security. The RSA Algorithm modeled using Verilog, implemented on the XILINX SPARTAN-7 FPGA (XC7S50-CSGA324-2) with a comparative analysis of 4- bit to 8-bit algorithmic parameters (such as p, q, e, d, M, C, n, phi_n). Two approaches are studied: one with uniform bit sizes of algorithmic parameters and another with smaller p & q bit sizes. Results show that the second approach yields better efficiency. Future work with CADENCE CIC tools will further optimize power consumption. This work offers insights into designing low-power RSA encryption chip for modern digital systems.
Paper Presenter
Wednesday January 29, 2025 12:45pm - 1:00pm IST
Tulip Hotel Crowne Plaza, Pune, India

1:00pm IST

Harnessing Agile Methodologies: A Case Study of GitHub's Collaborative Development and Its Competitive Edge Over Alternative Platforms
Wednesday January 29, 2025 1:00pm - 1:15pm IST
Authors - Tamanna Kalariya, Bimal Patel, Martin Parmer, Mrugendra Rahevar
Abstract - The Agile model has demonstrated clear superiority over traditional software process models by offering enhanced flexibility and adaptability to evolving project requirements. Unlike rigid, linear methodologies, Agile promotes iterative development, allowing teams to quickly incorporate feedback and adapt to changing needs. This iterative approach is particularly advantageous for software testers, enabling continuous testing throughout the development lifecycle, which improves code quality and reduces defects. GitHub, as a collaborative development platform, amplifies these Agile benefits by providing robust version control and seamless integration with Agile workflows. For testers, GitHub facilitates real-time collaboration, enabling immediate feedback and shared responsibility for quality assurance. Features such as pull requests, issue tracking, and continuous integration streamline the testing process and enhance communication between developers and testers. Additionally, GitHub’s Agile-driven workflow provides a competitive advantage over other code-sharing tools by optimizing development cycles and fostering a strong collaborative culture. The platform's rich ecosystem of tools, combined with extensive community support, further boosts productivity and innovation. This paper examines how Agile methodologies within GitHub enhance both the development and testing processes, positioning GitHub as a leader in the collaborative development landscape and driving greater efficiency in modern software engineering practices.
Paper Presenter
Wednesday January 29, 2025 1:00pm - 1:15pm IST
Magnolia Hotel Crowne Plaza, Pune, India

1:00pm IST

Algorithmic Trading: A Prospect Theory Perspective
Wednesday January 29, 2025 1:00pm - 1:15pm IST
Authors - Rashmy Moray, Sejal Vaishav, Sangam Dey, Sridevi Chennamsetti, Harsha Thorve
Abstract - This paper investigates the impact of behavioural biases, specifically Loss Aversion, Regret Aversion, Reference Dependence, and Risk Perception on algorithmic trading using the framework of Prospect Theory. Using structured questionnaire, the Primary data was collected from the traders who use algorithm. Statistical tool, SmartPLS was employed to assess the endogenous factors and the behavioural biases that influence the intention to trade using algorithms. The findings indicate that Risk Perception and Reference Dependence significantly impact trading intent, whereas Loss Aversion and Regret Aversion do not show a significant influence on trading intent. This advocates that the systematic and emotion-free nature of algorithmic trading minimizes the effects of certain emotional biases. The study contributes profound understanding of behavioural biases of traders adopting algorithm offer distinctive path for future scope of research.
Paper Presenter
Wednesday January 29, 2025 1:00pm - 1:15pm IST
Maple Hotel Crowne Plaza, Pune, India

1:00pm IST

AI-Powered Deep Packet Analyzer
Wednesday January 29, 2025 1:00pm - 1:15pm IST
Authors - Aditi Vanikar, Rana Vanikar, Mihir Sardesai, Rupesh Jaiswal
Abstract - Preserving the security and performance of a network in today's networked environment depends upon monitoring and analyzing network traffic. The paper discusses our system overview that analyzes live network traffic from a mirrored port by means of DPI. It includes the provision for gaining insight into the usage of networked applications by sorting packets into particular categories, such as HTTP, video streaming, or other protocols. The technology employs state-of-the-art machine-learning algorithms and categorization in order to identify and alert potentially damaging packets in a timely manner. Upon detecting any threats, the system alerts the user so that he or she can take appropriate preventive measures. The hybrid strategy ensures improved visibility and security of network environments through concurrent, real-time threat detection and traffic classification.
Paper Presenter
Wednesday January 29, 2025 1:00pm - 1:15pm IST
Tulip Hotel Crowne Plaza, Pune, India

1:15pm IST

Pioneering Innovation: Tesla's Agile Methodology in Revolutionizing the Automotive Industry
Wednesday January 29, 2025 1:15pm - 1:30pm IST
Authors - Nishit Patel, Bimal Patel, Mansi Patel, Parth Shah, Nishat Shaikh
Abstract - Agile model has demonstrated clear superiority over traditional software process models by offering enhanced flexibility and responsiveness to changing requirements, particularly essential in the fast-paced automotive industry. Prior to adopting Agile, Tesla encountered significant challenges, including prolonged development cycles and difficulties in scaling manufacturing processes to meet increasing demand. These constraints limited Tesla’s ability to innovate rapidly and deliver advanced features such as Full Self-Driving (FSD) capabilities. By implementing Agile methodologies, Tesla transformed its software development and manufacturing ramp-ups, enabling iterative development with continuous testing and integration. This shift significantly accelerated FSD development, allowing for faster adaptation to technological changes. The adoption of Agile led to measurable improvements, including reduced time-to-market for new features, improved cross-functional collaboration, and enhanced product quality. Compared to other automotive giants, Tesla’s Agile integration has provided a distinct competitive advantage in terms of operational efficiency and innovation. While competitors often rely on established, rigid processes, Tesla’s Agile approach enables rapid adaptation to market demands and continuous consumer feedback. This paper highlights the transformative impact of Agile methodologies on Tesla’s product development and operational strategies, demonstrating how Agile has positioned Tesla as a leader in automotive innovation, driving significant advancements in technology and customer satisfaction.
Paper Presenter
Wednesday January 29, 2025 1:15pm - 1:30pm IST
Magnolia Hotel Crowne Plaza, Pune, India

1:15pm IST

Design and Implementation of a Lightweight Smart Garage System: A Cost-Effective Approach
Wednesday January 29, 2025 1:15pm - 1:30pm IST
Authors - Padmanabh khunt, Martin Parmar, Het Khatusuriya, Mrugendra Rahevar, Bimal Patel, Krunal Maheriya
Abstract - The smart garage system presented in this paper incorporates advanced security and remote-control functionalities to enhance the user experience and ensure secure access. The implementation of a One-Time Password (OTP) authentication mechanism provides an additional layer of security, effectively preventing unauthorized access to the garage. Central to the system are ESP32 microcontrollers, which facilitate reliable and efficient communication between the keypad, relay module, and the mobile application. Utilizing LoRa communication, the system achieves long-range wireless connectivity, enabling seamless interaction between ESP32 microcontrollers even in areas with limited network coverage. The mobile application, developed using React Native, offers a user-friendly interface for homeowners, featuring login/signup options, direct garage door control, and OTP generation for secure access. A robust server backend, built with Node.js and supported by a MongoDB database, ensures efficient management of user data, including login credentials and generated OTPs. Furthermore, an admin panel is integrated to enhance user administration and access control capabilities. This comprehensive smart garage system not only improves security but also provides convenience and reliability for modern homeowners.
Paper Presenter
Wednesday January 29, 2025 1:15pm - 1:30pm IST
Maple Hotel Crowne Plaza, Pune, India

1:15pm IST

Development of Rapid and Reliable Technique for Milk Spoilage Detection
Wednesday January 29, 2025 1:15pm - 1:30pm IST
Authors - Ritika Patki, Srushti Jamewar, Tanika Mathur, Mridula Korde
Abstract - High temperatures accelerate the spoilage of dairy products, particularly milk, posing significant challenges for food safety and waste management. This study presents a novel sensor-based detection system designed to monitor milk spoilage by measuring real-time changes in pH levels and carbon monoxide (CO) concentrations, which are key indicators of microbial activity and biochemical shifts in milk. The system utilizes an Arduino UNO microcontroller integrated with a pH sensor and an MQ-7 gas sensor to assess milk freshness through a combination of pH and CO data. Results are displayed on an LCD screen with intuitive indicators of freshness status—categorized as "fresh," "not so fresh," or "spoiled"—ensuring ease of interpretation for users. Experimental validation across various milk samples demonstrates the system’s effectiveness in early spoilage detection. Future developments may focus on non-invasive methods and IoT-based miniaturization, incorporating machine learning algorithms for residual life prediction through blockchain integration. This approach promises to reduce food wastage and enhance food security, offering an accessible and sustain-able solution for milk spoilage detection.
Paper Presenter
Wednesday January 29, 2025 1:15pm - 1:30pm IST
Tulip Hotel Crowne Plaza, Pune, India

1:30pm IST

Traffic Sign Detection
Wednesday January 29, 2025 1:30pm - 1:45pm IST
Authors - Aarti Agarkar, Ayush Sasane, Amankumar Kumare, Aditya Kadlag, Gaurang Khanderay
Abstract - This paper presents a highly accurate intelligent traffic sign recognition system, which has potential to greatly improve safety on roads and make autonomous driving possible. It uses Convolutional Neural Networks (CNN) to detect and classify traffic signs, accurately robustly in real-time systems. The process involves pre-processing a large number of images, training the model and performing real-time detection with OpenCV in Python. The Node MCU Microcontroller is integrated to make the communication and responses more stable and can be set automatically after recognizing traffic signs. The findings show improved precision and stability of the properties that are essential for consideration in autonomous vehicle systems, where harmonious driving is needed to upgrade latest architectures.
Paper Presenter
Wednesday January 29, 2025 1:30pm - 1:45pm IST
Magnolia Hotel Crowne Plaza, Pune, India

1:30pm IST

Generative AI Powered Tool For Automated Video Summarization.
Wednesday January 29, 2025 1:30pm - 1:45pm IST
Authors - Varalatchoumy M, Syed Hayath, Dinesh D, Dhanush C P, Manu R, V Sadhana
Abstract - This paper presents an advanced Generative AI-powered system for video-to-text summarization, leveraging state-of-the-art Computer Vision (CV) technologies and Natural Language Processing (NLP) techniques. The developed system addresses the growing need to extract key information efficiently from lengthy videos across diverse domains such as education, entertainment, sports, and instructional content. By integrating visual and textual data, it pinpoints essential moments and generates concise summaries that capture the core message of the video, reducing the time users spend understanding extensive media. At the heart of this system lies a robust, open-source large language model (LLM), finetuned to produce human-like summaries from video transcripts. The system processes visual cues using advanced CV techniques—such as keyframe extraction and scene segmentation—and textual cues via Automatic Speech Recognition (ASR), which converts audio into text. This dual approach facilitates a deep understanding of spoken and visual content, ensuring that summaries are precise, relevant, and contextually accurate. The system has been evaluated on a diverse dataset, comprising videos of various genres, qualities, and lengths, demonstrating its capability to generalize effectively across a wide spectrum of content. Applications of this video summarization tool include content management, video indexing, educational platforms, and beyond, offering significant time-saving benefits to users and organizations. By incorporating real-time feedback, the system continuously refines its summarization techniques, enhancing accuracy and ensuring that users quickly access the most relevant information, thereby promoting greater accessibility and usability of video content.
Paper Presenter
avatar for Dinesh D
Wednesday January 29, 2025 1:30pm - 1:45pm IST
Maple Hotel Crowne Plaza, Pune, India

1:30pm IST

Kidney Disease Detection using Machine Learning
Wednesday January 29, 2025 1:30pm - 1:45pm IST
Authors - Rajlaxmi Sunil Sangve, Riya Jha, Bhagyashri Narale, Sakshi Hosamani
Abstract - Kidney disease is an asymptomatic disease, which leads to severe complications or even mortality if not diagnosed early. Routine diagnostic methods, such as serum-based tests and biopsies, are either less effective in the early stages of the disease. This paper proposes an automatic detection of kidney disease using CNNs applied to medical imaging data. Our model is designed to analyze computed tomography (CT) images for the identification of kidney disease, classifying normal and tumors. The proposed CNN architecture leverages deep learning techniques to extract features from these images and classify them with high accuracy. This paper aims to build a system for detection of kidney disease using CNN, based on a public dataset sourced from Kaggle. The paper involves several key stages, initiated from raw data preprocessing and feature selection, followed by training and evaluating machine learning model using CNN. Our proposed model demonstrated superior performance in kidney disease detection, achieving an accuracy of 95%.
Paper Presenter
Wednesday January 29, 2025 1:30pm - 1:45pm IST
Tulip Hotel Crowne Plaza, Pune, India

1:45pm IST

Empowering Vision: A Survey on Image Captioning Assistive Technologies for the Visually Impaired
Wednesday January 29, 2025 1:45pm - 2:00pm IST
Authors - Vidisha Deshpande, Gauri Shelke, Bhakti Kadam
Abstract - Advancements in deep learning are fundamentally transforming assistive technologies, providing visually impaired users with unprecedented access to information and enhanced interaction with their surroundings. This paper comprehensively surveys traditional and emerging assistive technologies, focusing on real-time image caption generation systems. The modern advancements that bridge sensory limitations and digital interaction by covering a range of technologies such as Optical Character Recognition (OCR)-based text readers, object detection systems, image captioning systems, and intelligent haptic feedback devices are highlighted. In particular, the critical role of vision-language models and multimodal systems, which enable real-time auditory descriptions of visual scenes is studied. The survey also identifies significant gaps in real-world applications, particularly in terms of adaptability, cost, and inclusivity. These findings emphasize the need for more accessible, affordable, and real-time solutions that cater to the diverse needs of visually impaired individuals.
Paper Presenter
Wednesday January 29, 2025 1:45pm - 2:00pm IST
Magnolia Hotel Crowne Plaza, Pune, India

1:45pm IST

PhishSecure: Enhancing Web Safety with ML
Wednesday January 29, 2025 1:45pm - 2:00pm IST
Authors - Chetana Shravage, Shubhangi Vairagar, Priya Metri, Shreya C. Jaygude, Pradnya P. Sonawane, Pradnya D. Kudwe, Siddheshwari S. Patil
Abstract - This project presents an innovative phishing detection system that addresses the limitations of traditional methods by combining URL-based and content- based features to accurately identify fraudulent websites. Unlike conventional approaches that rely heavily on blacklisting and heuristics, which struggle with zero-day attacks and frequent updates, this system employs machine learning algorithms to automatically extract and analyze critical features from URLs and webpage content. By leveraging a comprehensive dataset that consists of fraudulent (phishing) websites along with legitimate websites, the system aims to improve detection rates to optimize performance based on evaluation metrics like accuracy, precision, F-1 score, recall, and false-positive rates. The system makes use of selective machine learning models like Random Forest, Decision Tree, and Support Vector Machine (SVM), which provide the benefit of increased scalability, robustness and improved effectiveness in phishing detection. Ultimately, this project aims to deliver a scalable, real-time detection solution that effectively mitigates phishing threats in a rapidly evolving landscape.
Paper Presenter
Wednesday January 29, 2025 1:45pm - 2:00pm IST
Maple Hotel Crowne Plaza, Pune, India

1:45pm IST

A Comprehensive Review of Deep Q-Learning for Network Intrusion Detection: Limitations and Enhancements
Wednesday January 29, 2025 1:45pm - 2:00pm IST
Authors - Aman Bhimrao Kamble, Shafi Pathan
Abstract - Deep Q-Learning (DQL) has emerged as a promising method for enhancing Network Intrusion Detection Systems (NIDS) by enabling dynamic and adaptive detection of evolving network threats. This review examines the strengths, limitations, and potential enhancements of using DQL in NIDS. Even while DQL increases the accuracy of anomaly detection and manages massive amounts of network data, it has drawbacks such slow convergence, high processing costs, and vulnerability to adversarial attacks. This study proposes improvements to overcome these problems, including efficient reward systems, hybrid architectures that combine DQL with other machine learning models, and continuous learning to adapt to changing threats. Recommendations for further research to enhance DQL's efficacy in real-time intrusion detection are included in the study's conclusion.
Paper Presenter
Wednesday January 29, 2025 1:45pm - 2:00pm IST
Tulip Hotel Crowne Plaza, Pune, India

2:00pm IST

The Virtual Healing Garden
Wednesday January 29, 2025 2:00pm - 2:15pm IST
Authors - Renuka Sandeep Gound, Farhan Mujawar, Niraj Dhakulkar, Payal Rathod, Saish Bhise, Kavita Moholkar
Abstract - With the growing interest in naturopathy and holistic health, it is more important to make information about medicinal plants easily accessible. The Virtual Healing Garden is designed to do just that, offering a dynamic platform where AYUSH students, professors, and plant enthusiasts can explore a variety of medicinal plants in a hands-on, engaging way. By blending traditional healing knowledge with modern scientific research, the platform brings these plants to life through interactive 3D models. Using cutting-edge algorithms like photogrammetry and 3D modeling, the garden creates realistic representations of the plants, giving users a detailed and immersive experience. The Virtual Healing Garden will feature a rich database of 3D plant models, each paired with detailed information about their medicinal properties. This will provide users with a visually immersive and informative resource, making it easy to explore and learn about various plants in a more engaging way, whether for education or research. This virtual garden not only raises awareness of the health benefits of medicinal plants but also makes learning about them interactive and fun.
Paper Presenter
Wednesday January 29, 2025 2:00pm - 2:15pm IST
Magnolia Hotel Crowne Plaza, Pune, India

2:00pm IST

Cardio Rhythm Analysis using Machine Learning Techniques : Review Paper
Wednesday January 29, 2025 2:00pm - 2:15pm IST
Authors - Chetana Shravage, Shubhangi Vairagar, Priya Metri, Rohit Rajendra Kalaburgi, Harsh Anil Shah, Abhinandan Vaibhav Sharma, Shubham Shatrughun Godge
Abstract - Heart sound categorization is critical for the early identification and detection of cardiovascular illness. Recently deep learning methods have resulted in promising improvements in the correctness of heart sound classification systems. This work introduces a unique transformer-based model for heart sound classification that uses powerful attention mechanism to capture both local as well as global dependencies in heart sound data. Transformers, in contrast to traditional models that rely on handmade features or recurrent networks, can dynamically focus on the most important characteristics in time-series data, making them perfect for dealing with the complexity and variability of phonocardiogram (PCG) signals
Paper Presenter
Wednesday January 29, 2025 2:00pm - 2:15pm IST
Maple Hotel Crowne Plaza, Pune, India

2:00pm IST

Understanding Investors' Intention to Use P2P Lending Platforms: An ISS …. DeLone And Mclean Approach
Wednesday January 29, 2025 2:00pm - 2:15pm IST
Authors - Shannon D’Souza, Rashmy Moray, Sridevi Chenammasetti, Shikha Jain
Abstract - This study explores factors affecting investors' intentions to utilize Peer-to-Peer (P2P) lending platforms using the DeLone and McLean Information Systems Success (ISS) model and the Technology Acceptance Model (TAM). Data was collected through an online survey, yielding 283 valid responses from 350 distributed questionnaires, using snowball and convenience sampling. Structural Equation Modelling (SEM) with SmartPLS validated relationships between system quality, information quality, service quality, user satisfaction, social influence, and continuous intention to use. Findings indicate that system quality and service quality have a significant positive predictive effect on user satisfaction, while social influence positively affects ongoing usage intentions. Improved system performance, information accuracy, and service responsiveness can foster investor trust, platform adoption, and retention, guiding stakeholders and regulators toward an environment of stable and successful P2P lending.
Paper Presenter
Wednesday January 29, 2025 2:00pm - 2:15pm IST
Tulip Hotel Crowne Plaza, Pune, India

2:15pm IST

Mitigating Scams, Phishing, and Malicious Attacks: Strategies for Enhancing Cybersecurity and Personal Protection
Wednesday January 29, 2025 2:15pm - 2:30pm IST
Authors - Pankaj Chandre, Palash Sontakke, Rajkumar Patil, Bhagyashree D Shendkar, Viresh Vanarote, Dhanraj Dhotre
Abstract - In today’s digital landscape, the prevalence of scams, phishing, and malicious attacks poses significant risks to both individuals and organizations. Mitigating these threats requires a comprehensive cybersecurity strategy that begins with user awareness and extends to robust protective measures and incident response protocols. By integrating education, proactive defenses, and responsive actions, personal and organizational cybersecurity can be greatly enhanced. Mitigating scams, phishing, and malicious attacks requires a comprehensive approach to cybersecurity and personal protection. This strategy begins with the User Environment, where devices connected to the internet become vulnerable to threats. Education and Awareness play a crucial role, providing training on recognizing phishing attempts and setting up reporting mechanisms to flag suspicious activities. Building on this, Protective Measures such as strong passwords, multi-factor authentication, regular software updates, and the use of security tools strengthen the defenses against cyber threats. Should an attack occur, Incident Response protocols are activated, including the detection and investigation of incidents, followed by recovery actions to restore security and prevent future attacks. By integrating these layers of defense, individuals and organizations can significantly reduce the risks of cyberattacks and safeguard sensitive information.
Paper Presenter
Wednesday January 29, 2025 2:15pm - 2:30pm IST
Magnolia Hotel Crowne Plaza, Pune, India

2:15pm IST

Transforming Financial Statement Analysis with Large Language Models: A Survey of Approaches and Challenges
Wednesday January 29, 2025 2:15pm - 2:30pm IST
Authors - Shubhangi Vairagar, Chetana Shravage, Priya Merti, Nikhil M. Ingale, Sakshi N. Gaikwad, Dhruv G. Yaranalkar, Atharva R. Pimple
Abstract - Artificial Intelligence, specifically large language models and generative AI, has dramatically changed the finance industry. According to a few research studies, the current article discusses some of those findings that provided insight into different applications of artificial intelligence in the sector of financial operations and improved predictive analytics, operational efficiency, and quality in decision-making processes. Most critical findings point towards the efficiency of LLMs, where it has achieved automation, financial analysis improvements, and does comply with enforcement standards in their usage of such technologies as towards most security concerns, privacy, and ethical perspectives. Specifically, the long-term implications for financial decision-making and the potential consequences arising from the use of such technologies in an ethical view stand out starkly as red flags of concern. Thus, the review brings new knowledge in the sphere of AI in finance and grounds further justification to be done with proper research motives toward complete responsible development of AI.
Paper Presenter
Wednesday January 29, 2025 2:15pm - 2:30pm IST
Maple Hotel Crowne Plaza, Pune, India

2:15pm IST

A Comprehensive Solution for Locating and Accessing Ayurveda, Yoga, and Naturopathy Hospitals
Wednesday January 29, 2025 2:15pm - 2:30pm IST
Authors - Kumbhar Sanjivanee Rajan, Kulkarni Prachi Prashant, Pawar Prachi Baghwan, Patara Diya Milan, Abira Banik
Abstract - “Holistic Heal” is an app developed using Flutter, tailored to address the growing interest in alternative healthcare options such as Ayurveda, Yoga, and Naturopathy. The app simplifies the process of discovering and accessing these specialized hospitals and wellness centers, making holistic healthcare more accessible to users. Harnessing the capabilities of geolocation services enables users to find the nearest facilities, offering detailed information for each. The app also ensures a user-friendly experience. Built with a focus on promoting holistic well-being, “Holistic Heal” showcases the potential of technology to enhance traditional healing practices and empower individuals in their journey towards a healthier, more balanced lifestyle. Depending on their demands, the patient can search the hospital. Upon the patient’s request, this application offers the hospital and physician details that are currently accessible. A suggested application has been designed to find the closest hospital with the requested medical specialty. Hospitals’ nearest locations are identified using the Global Positioning System (GPS), which provides real-time geographic data by triangulating signals from satellites. This data, integrated into smartphones, is combined with Google Maps Application Programming Interfaces (APIs) to determine optimal routes from the user’s current location to hospitals, accounting for road networks, traffic, and travel modes. A patient can use this application to discover the closest hospital based on the availability of expert consultants. This application that was built is easy to use and effectively gives patients the necessary information.
Paper Presenter
Wednesday January 29, 2025 2:15pm - 2:30pm IST
Tulip Hotel Crowne Plaza, Pune, India

2:30pm IST

A Multimodal Approach for Detection and Prediction of Diabetic Retinopathy using Machine Learning and Deep Learning Techniques
Wednesday January 29, 2025 2:30pm - 2:45pm IST
Authors - Swati Kiran Rajput, Sunil Gupta
Abstract - Diabetes mellitus is the root cause of a disease known as diabetic retinopathy, which is a disorder that affects the retina. In every region of the globe, it is the leading cause of blindness. Early detection and treatment are very necessary in order to delay or avoid the deterioration of vision and the loss of eyesight. The scientific community has proposed a number of artificial intelligence algorithms for the aim of identifying and classifying diabetic retinopathy in fundus retina pictures due to the fact that this is the intended objective. Utilizing a Convolutional Neural Network (CNN), we suggested a method for the identification and early prediction of diabetic retinopathy in this particular piece of research. Using a wide variety of hyper parameters, such as epoch size, batch size, optimized, and so on, the Deep CNN has been used for both training and testing purposes. Examples of normal and abnormal retinal pictures have been included in the MRI dataset. When the findings of the experimental investigation were evaluated using machine learning and deep learning algorithms like SVM, ANN, and CNN, the results were shown to be accurate. In conclusion, the CNN achieves a detection and prediction accuracy of 96.60%, which is superior than that of the SVM and other artificial neural networks.
Paper Presenter
Wednesday January 29, 2025 2:30pm - 2:45pm IST
Magnolia Hotel Crowne Plaza, Pune, India

2:30pm IST

Survey of Deep Learning Models for Disease Detection in Various Fruit Species
Wednesday January 29, 2025 2:30pm - 2:45pm IST
Authors - Ashutosh Patil, Gayatri Bhangle, Sejal Kadam, Poonam Vetal, Prajakta Shinde
Abstract - Advances in deep learning have enabled effective applications in agriculture, including fruit disease detection. Accurate identification of diseases in fruits such as Annonaceae and Rutaceae families is crucial for yield and quality improvements. Many studies employ deep learning models like CNN, ResNet, VGG, and DenseNet for disease detection across fruits such as apple, orange, guava, and grapes. This article reviews recent research on deep learning for fruit disease detection and classification, focusing on model performance, data utilization, and visualization techniques. We analyze existing studies to identify optimal strategies for fruit species and other underrepresented crops, outlining challenges and areas for future research on various types of fruit species and their family.
Paper Presenter
Wednesday January 29, 2025 2:30pm - 2:45pm IST
Maple Hotel Crowne Plaza, Pune, India

2:30pm IST

Real Time Football Match Analysis and Substitution Recommendations using Machine Learning and API Integration
Wednesday January 29, 2025 2:30pm - 2:45pm IST
Authors - Malay Shah, Sayal Goyal, Rashmi Rane, Ruhi Patankar, Sarika Bobde, Arnav Jain
Abstract - This study investigates the impact of risk-taking on football match outcomes, focusing on player substitutions. The analysis reveals that risk-taking propensity peaks when a team is trailing by 2-3 goals and diminishes when leading by the same margin. Younger managers outperform middle-aged ones in risky decisions, while older managers excel in later substitutions. Additionally, a manager's tenure with the team increases the effectiveness of risk-taking, particularly in earlier substitutions and stronger teams. This study also emphasizes the importance of mental state in player performance, proposing a framework combining Match Score Analysis (Kaplan-Meier Fitter) and Score Analysis to evaluate players' mental stability and survival rates during the game. By integrating these models, teams can make better-informed decisions regarding substitutions, considering both past performance and mental health, ultimately enhancing match outcomes. This research underscores the synergistic potential of combining black-box causal machine learning with interpretable models, offering valuable insights for football management and beyond.
Paper Presenter
Wednesday January 29, 2025 2:30pm - 2:45pm IST
Tulip Hotel Crowne Plaza, Pune, India

2:45pm IST

Elevate Customer Engagement with WhatsApp Chat Analysis
Wednesday January 29, 2025 2:45pm - 3:00pm IST
Authors - Nilesh Deotale, Nafiz Shaikh, Ashwin Katela
Abstract - WhatsApp chats consist of various kinds of conversation held among two people or a group of people. This chat consists of various topics. This information can provide a lot of data for the latest technologies such as Machine Learning. The most important things for Machine Learning models are to provide the right learning experience which is indirectly affected by the data we provide to the model. This tool aims to provide in depth analysis of the data which is provided by WhatsApp. Irrespective of whichever topic the conversation is based on, our developed code can be applied to obtain a better understanding of the data. The advantage of this tool is that it is implemented using simple python modules such as pandas, matplotlib, seaborn, streamlit, NumPy, re, emojis and a technique sentiment analysis which are used to create data frames and plot different graphs, where then it is displayed in the streamlit web application which is efficient and less resources consuming algorithms, therefore it can be easily applied to larger dataset. The Accuracy of this project is 75%. In summary, this project makes use of state-of-the-art data analysis technologies such as scikit-learn, Topic Modeling, Named Entity Recognition, Clustering, Word Embeddings, Natural Language Toolkit, and more. Sequence-to-Sequence Models, Text Classification and so forth. Users can better understand how others communicate by using Language Model Fine-Tuning to extract relevant information from WhatsApp discussions.
Paper Presenter
Wednesday January 29, 2025 2:45pm - 3:00pm IST
Magnolia Hotel Crowne Plaza, Pune, India

2:45pm IST

Enhanced Pathological Tissue Image Categorization Using a Bag-of-Features Approach with Roulette Wheel Whale Optimization
Wednesday January 29, 2025 2:45pm - 3:00pm IST
Authors - Susheela Vishnoi, Monika Roopak, Prashant Vats
Abstract - Pathological tissue image categorization is essential in medical diagnostics, offering insights into disease types, progression, and treatment alternatives. The significant variability in tissue morphology and the overlapping visual patterns across different classes complicate accurate categorization. This study introduces an improved categorization model utilizing a bag-of-features (BoF) methodology integrated with the Roulette Wheel Whale Optimization Algorithm (RWWOA) to enhance classification accuracy and optimize feature selection efficiency. The proposed model utilizes the Bag of Features (BoF) technique to extract discriminative features from tissue images, thereby generating a feature-rich dictionary that represents various pathological structures. The RWWOA is employed to optimize feature selection, thereby reducing dimensionality and concentrating on the most pertinent features for precise categorization. Our method integrates the exploration capabilities of the Whale Optimization Algorithm (WOA) with the probabilistic selection mechanism of the roulette wheel, thereby dynamically balancing exploitation and exploration, which enhances convergence speed and categorization accuracy. Experimental results indicate that the RWWOA-BoF method outperforms traditional methods across various datasets, showing enhancements in classification precision, recall, and F1-score. This method offers a reliable resource for aiding pathologists in diagnostic imaging, which may expedite diagnostic processes and improve consistency in clinical practice.
Paper Presenter
Wednesday January 29, 2025 2:45pm - 3:00pm IST
Maple Hotel Crowne Plaza, Pune, India

2:45pm IST

Performance Analysis of Handwritten Digit Recognition: Integrating Machine Learning and Deep Learning in Mobile Applications
Wednesday January 29, 2025 2:45pm - 3:00pm IST
Authors - Sanjana, Sukanya Sharma, Dipty Tripathi
Abstract - Handwritten digit recognition is a key application in image processing and pattern recognition, with wide usage in areas such as postal services, banking, and mobile applications. This research paper presents a performance comparison between traditional machine learning models and deep learning models for accurate handwritten digit classification. The study focuses on developing a mobile application using Flutter integrated with TensorFlow Lite and Firebase to deliver real-time predictions. The app performs preprocessing on input images and employs model inference for efficient and accurate digit recognition. The objective is to determine the most effective model in terms of speed and accuracy for on-device predictions, emphasizing usability and real-time response
Paper Presenter
avatar for Sanjana

Sanjana

India
Wednesday January 29, 2025 2:45pm - 3:00pm IST
Tulip Hotel Crowne Plaza, Pune, India

3:00pm IST

Random Sampling of Social Media Interaction Data
Wednesday January 29, 2025 3:00pm - 3:15pm IST
Authors - Yogini Prasanna Paturkar, Amol P. Bhagat, Priti A. Khodke
Abstract - Social media platforms generate vast amounts of interaction data, offering valuable insights into user behavior, preferences, and trends. However, the sheer volume and velocity of this data pose significant challenges for real-time analysis and computational efficiency. This paper proposes a framework for random sampling of social media interaction data to address these challenges. By employing probabilistic sampling methods, we aim to reduce data volume while preserving key statistical properties and minimizing information loss. The proposed methodology leverages stratified and weighted random sampling techniques to ensure the representation of diverse user groups and interaction types. Applications of this approach include sentiment analysis, trend detection, and user behavior modeling. Preliminary experiments demonstrate that random sampling can achieve a significant reduction in computational overhead while maintaining analytical accuracy within an acceptable margin of error. This framework has the potential to enhance data processing pipelines in fields such as marketing, public opinion analysis, and event monitoring, enabling timely and resource-efficient decision-making.
Paper Presenter
Wednesday January 29, 2025 3:00pm - 3:15pm IST
Magnolia Hotel Crowne Plaza, Pune, India

3:00pm IST

Exploring the Role of Trust and Perception in AI Adoption: A South African Perspective
Wednesday January 29, 2025 3:00pm - 3:15pm IST
Authors - Rowan Cowper, Grant Oosterwyk, Jean-Paul Van Belle
Abstract - The rapid and widespread growth of AI use has brought about a number of important areas for research. This paper aims to examine the human factors that impact AI use: whether demographic attributes, trust in AI, and perceptions about AI influence whether someone will use AI or not. A survey among South African industry and academic respondents was used. The key findings of this study include that age and education have a significant impact on trust in AI. Domain knowledge and education levels were significant indicators of perception of AI, with higher levels of domain knowledge and education leading to lower perceptions of AI. Both AI trust and perception were found to have a significant positive impact on whether someone made use of AI or not. These findings may inform decision makers on targeted interventions, such as education, to increase the use of AI in industry and academic contexts. Hopefully further academic research will also validate our findings in other research contexts, such as India and/or different population segments.
Paper Presenter
avatar for Jean-Paul Van Belle
Wednesday January 29, 2025 3:00pm - 3:15pm IST
Maple Hotel Crowne Plaza, Pune, India

3:00pm IST

SO-SVM: Self Organizing Support Vector Machine
Wednesday January 29, 2025 3:00pm - 3:15pm IST
Authors - Rupan Panja, Rajani K. Mudi, Nikhil R. Pal
Abstract - The Support Vector Machine (SVM) is a popular classification algorithm, however, it suffers from the drawback that the classification time for an unknown data point is proportional to the number of support vectors (SVs). Thus, its application for real-time decision-making, especially for complex class boundaries becomes problematic / impossible. In this article, we propose an algorithm, Self Organizing Support Vector Machine (SO-SVM), which can decrease the number of SVs without compromising the accuracy. In this algorithm, we first cluster the data points using self-organizing map to find potential class boundary points, which are crucial for determining the separating hyperplane in an SVM. The separating hyperplane is then learned from the selected boundary points, leading to a reduction in the number of SVs. Learning the SVM also becomes efficient because of the reduced number of data points. The proposed algorithm has been tested on a number of benchmark datasets and found to decrease the number of SVs without deteriorating the classification accuracy. The SO-SVM algorithm thus can be an efficient alternative to the SVM algorithm and thus can be applied instead of normal SVM without making a noticeable compromise with the performance.
Paper Presenter
Wednesday January 29, 2025 3:00pm - 3:15pm IST
Tulip Hotel Crowne Plaza, Pune, India

3:15pm IST

Understanding the Drivers of usage Intention of Chatbots by Bank customers…. A UTAUT Approach
Wednesday January 29, 2025 3:15pm - 3:30pm IST
Authors - Riya Reddy, Ayoush Singh, Keshav Tanwar, Rashmy Moray, Shikha Jain, Sridevi Chennamsetti
Abstract - This study examines the underlying drivers of usage intention of chatbots by bank customers using UTAUT model. This model assesses four key factors determining the usage intent of chatbots: performance expectancy, effort expectancy, social influence, and facilitating conditions. Primary data was obtained through structured questionnaire from retail banking customers. Structural Equation Modelling method is used and statistical tool SmartPLS 4.0 was employed to analyze complex associations between variables. The results show that performance expectancy, facilitating conditions, and motivation have a significant association with the usage intention of chatbots; however, effort expectancy and social influence have no connection. In addition, the results show that perceived security and trust are essential criteria for adopting a chatbot in banking. The study contributes value to the existing body of knowledge by proving the factor influencing the intent usage of chatbots in the banking industry. It also highlights future work directions in the form of long-term impacts and insights that can guide banks in designing customer-centric AI systems and improving chatbot services.
Paper Presenter
Wednesday January 29, 2025 3:15pm - 3:30pm IST
Magnolia Hotel Crowne Plaza, Pune, India

3:15pm IST

AI Skills and Technologies for Future-Proofing Businesses in the Manufacturing Industry
Wednesday January 29, 2025 3:15pm - 3:30pm IST
Authors - Mihlali Mqoqi, Marita Turpin, Jean-Paul Van Belle
Abstract - The manufacturing industry is undergoing significant changes due to the emergence of artificial intelligence (AI) technologies. This transition has implications, particularly with regards to the skills necessary to adopt and leverage these technologies effectively. This paper addresses the issue of the expanding gap between the skill set in the current workforce and those required for the future of work. A systematic literature review was conducted to investigate these skills, which resulted in the selection of 28 studies out of 216 initially identified that offered insights into the benefits and applications of AI in manufacturing. The findings show how AI has altered manufacturing processes through predictive maintenance, quality assurance, and product design. Further, there is a need for targeted upskilling and reskilling programs to bridge the current skill gap and equip the workforce to meet the changing demands of the industry. Initiatives that could be implemented for successful skills development are also discussed.
Paper Presenter
avatar for Marita Turpin

Marita Turpin

South Africa
Wednesday January 29, 2025 3:15pm - 3:30pm IST
Maple Hotel Crowne Plaza, Pune, India

3:15pm IST

EfficientApneaNet: Detection of Apnea via ECG Signal
Wednesday January 29, 2025 3:15pm - 3:30pm IST
Authors - Nitesh Pradhan, Aryan Baghla
Abstract - Electrocardiogram (ECG) signals are effective indicators for detecting obstructive sleep apnea (OSA) due to their ability to reflect physiological changes associated with apnea events. EfficientApneaNet is a deep learning-based model for the detection of OSA from a single-lead ECG. In general, traditional approaches are reliable but exhaustive and costly; therefore, ECG-based methods are being sought. Earlier machine-learning methods, including Support Vector Machines and Random Forests, are prone to real data noise. Based on these, EfficientApneaNet joins previous advances in deep learning for further improvement in accuracy and robustness. The proposed architecture is powered by three novelties, namely: the ENBlocks, inspired by EfficientNet, Squeeze-and-Excitation blocks, and attention. ENBlocks make use of depthwise separable convolutions that reduce the computational complexity and amplify the efficiency of spatial feature extraction. The Squeeze and Excitation blocks carry out channel recalibration to focus on relevant patterns, while the attention mechanism underlines the critical temporal events within the ECG sequences. On the Apnea-ECG dataset, EfficientApneaNet realizes state-of-the-art performance with 91.47% accuracy, 85.92% sensitivity, and 94.91% specificity outperforming those of leading existing CNN-LSTM hybrids. It adopts Adamax optimization for stability while implementing the technique of cosine annealing for LR scheduling. The residual connections avoid gradient vanishing and explosion. Through ablation studies, it is confirmed that SE blocks and the attention mechanism are both essential to achieving high sensitivity and high specificity. In this respect, EfficientApneaNet would be considered a significant improvement in OSA detection, as it has successfully handled spatial and temporal complexities in ECG data.
Paper Presenter
Wednesday January 29, 2025 3:15pm - 3:30pm IST
Tulip Hotel Crowne Plaza, Pune, India

3:30pm IST

Dyslexia Friendly Summarized Document Enhancement for Improved Readability
Wednesday January 29, 2025 3:30pm - 3:45pm IST
Authors - Soham Kulkarni, Suhani Thakur, Twsha Vyass, V. Yasaswini, Pooja Kamat
Abstract - Dyslexia is a learning disorder that causes difficulty in reading and identifying relations between words and letters.[1] To improve reading accessibility within dyslexic patients, this study aimed to develop models for summarization of documents as well as provide a streamlined method to convert documents into dyslexia-friendly versions. Within this study, several summarization models were tested to generate effective text summaries, while defining Python functions to convert regular text into dyslexia friendly text. Models attempted with are: Term Frequency- Inverse Document Frequency Summarizer, Term Frequency-Inverse Document Frequency Summarizer with Support Vector Machine, and BART Transformer. After analysing the results, the BART Trained Summarization Model results are fruitful having a ROUGE R-1 F1 Score of 0.4510, a R-2 F1 Score of 0.2571 and a R-L F1 Score of 0.4177, ultimately successfully generating dyslexia-friendly summarized documents.
Paper Presenter
Wednesday January 29, 2025 3:30pm - 3:45pm IST
Magnolia Hotel Crowne Plaza, Pune, India

3:30pm IST

Examining the factors influencing the intent usage of Digital Currencies by Gen Z and Millennials: A UTAUT Model Approach
Wednesday January 29, 2025 3:30pm - 3:45pm IST
Authors - Anupama Pandey, Anubha Dwivedi, Rashmy Moray, Vivek Divekar, Shikha Jain, Sridevi Chennamsetti
Abstract - The objective of the research is to identify the factors that affects the adoption of digital currencies by the Generation Z and the millennials, utilizing UTAUT model. It highlights the role of performance expectancy, effort expectancy, social influence, and facilitating conditions on the use of digital currency. A designed questionnaire was used to collect the primary data from Gen Z and millennials. Statistical techniques SmartPLS was used to analyse the correlation and impact of the various UTAUT constructs on the intent to use digital currency. Results highlight that performance expectancy and user influence have the significant influence on the adoption of innovative solutions. The paper also describes issues concerning the digital infrastructure and various regulations. Prospective strategies to increase the acceptability of the digital currency with the young people are advanced to articulate the perceived usefulness and to make the use of digital currencies easier.
Paper Presenter
Wednesday January 29, 2025 3:30pm - 3:45pm IST
Maple Hotel Crowne Plaza, Pune, India

3:30pm IST

Prediction of Chronic kidney Disease (CKD) using Hybrid Machine Learning Model
Wednesday January 29, 2025 3:30pm - 3:45pm IST
Authors - Arun Kumar S, Niveditha S, Vikas K B, Varshini C, Hemalatha H U
Abstract - Improved patient outcomes and effective management of Chronic Kidney Disease (CKD) depend on early detection, making it a major worldwide health concern. This study presents a hybrid machine learning model that combines Random Forest and LightGBM classifiers in order to accurately predict CKD stages. The dataset from Kaggle was used to build the model, and SMOTE was used to handle class imbalances in addition to feature engineering and data preprocessing. With regard to test data, the model's accuracy was 97.7%. Enhancing its actual clinical utility, a web-based tool was also developed to enable real-time forecasts of CKD stage based on patient inputs. Using a mixed-method approach that included more than 1600 participants' medical examinations and surveys With the integration of real-time clinical data and strong predictive models among its potential enhancements, the proposed approach provides a robust and easily utilized tool for clinical applications.
Paper Presenter
Wednesday January 29, 2025 3:30pm - 3:45pm IST
Tulip Hotel Crowne Plaza, Pune, India

3:45pm IST

Navigating the Future of Document Handling: A Comprehensive Survey on Intelligent Document Processing
Wednesday January 29, 2025 3:45pm - 4:00pm IST
Authors - Mihir Deshpande, Hrishikesh Patkar, Madhuri Wakode
Abstract - Intelligent Document Processing (IDP) automates extraction and categorization of information from unstructured and semi-structured documents. While standard Optical Character Recognition (OCR) computerizes the parsing of printed and scanned documents, so-called parsing tools like PyPDF2 and React-PDF take on the extraction of text from digital files. Emerging developments in IDP apply Large Language Models (LLMs) to enhance Natural Language Processing (NLP) to ensure the efficient interpretation, classification, and analysis of the extracted data. This paper provides a survey of IDP technologies with possible applications in financial and retail sectors-invoice processing, purchase order matching, and fraud detection. It also focuses on accuracy, scalability, and outlook of LLM-based IDP on the cloud, towards next-generation automation in more detail.
Paper Presenter
Wednesday January 29, 2025 3:45pm - 4:00pm IST
Magnolia Hotel Crowne Plaza, Pune, India

3:45pm IST

A study on Abnormality Detection and Classification of diseases on X-Ray images of Animals
Wednesday January 29, 2025 3:45pm - 4:00pm IST
Authors - G.G. Rajput, Sumitra M Mudda
Abstract - This paper presents abnormality detection from segmentation techniques for leg fracture segmentation from animal x-ray images. Gaussian filtering is used to remove the noise from the x-ray images. fracture is segmented from x-ray image by performing thresholding segmentation operations. Experiments are performed on clinical data set to present the severity of the fracture in images for threshold segmentation methods studied. Extract the Features from segmented images using GLCM techniques. SVM algorithm is use for classify the given animal x-ray image is fractured or not. Using thresholding segmentation techniques, fractures are separated from x-ray images. Utilizing GLCM techniques, extract the features from segmented images. The SVM method is used to determine whether or not the provided animal x-ray image is broken.
Paper Presenter
Wednesday January 29, 2025 3:45pm - 4:00pm IST
Maple Hotel Crowne Plaza, Pune, India

3:45pm IST

AI TRAVEL BUDDY: EMOTION DETECTION AND CONVERSATIONAL AI FOR ENHANCING SOLO TRAVEL EXPERIENCES
Wednesday January 29, 2025 3:45pm - 4:00pm IST
Authors - Nilesh Korade, Swaraj Waykar, Archit Waghmode, Shweta Tate
Abstract - Though solo travel can be an enriching and adventurous experience, staying alone (without social interactions) can sometimes enhance feelings of loneliness and this can be detrimental to a traveler's mental health. In this paper, we present an AI Travel Buddy. This intelligent system uses real-time facial expression recognition to determine the emotion of travelers at the moment and provides personalized conversational topics based on the detected emotion using the emotion-aware voice assistant. Using the latest CNN architecture for emotion detection, and Conversational model for dynamic answers, the AI Travel Buddy is a companion to travelers, delivering emotional support to users on solo trips. The paper describes the technologies used, the system architecture, and its methodology, showing substantial improvement in user engagement emotionally alongside providing overall higher travel satisfaction.
Paper Presenter
Wednesday January 29, 2025 3:45pm - 4:00pm IST
Tulip Hotel Crowne Plaza, Pune, India

4:00pm IST

Predictive Analysis of Cyber Attack Patterns and Consequent System Outages in Industrial IoT Systems
Wednesday January 29, 2025 4:00pm - 4:15pm IST
Authors - Sahana Balajee, Sakhi Saswat Panda, Anushruth Gowda, Animesh Giri
Abstract - The extension of conventional Internet of Things (IoT) technologies from basic applications to advanced industrial processes has revolutionised operational efficiency, giving rise to a new class of IoTs called the Industrial Internet of Things (IIoT) systems. Despite these advancements, critical IIoT infrastructures remain exposed to a growing range of cybersecurity threats due to open vulnerabilities, stemming from the fact that these IIoT networks try to interconnect physical machinery with digital systems. Cyber attacks targeting IIoT systems attempt to exploit these vulnerabilities in order to cause severe operational disruptions and costly outages. These intrusions pose significant risks to data integrity, operational continuity, and total production, highlighting the need for effective cybersecurity measures in the industrial setting. To help prevent such intrusions this work proposes a unified predictive framework combining cyber attack detection with outage prediction to enhance resilience in IIoT environments. By leveraging machine learning algorithms, this framework analyses two types of data - network traffic for cyber threats and historical sensor data for forecasting the Remaining Useful Life (RUL) of critical components. This approach aims to identify risks and send alerts to minimise operational downtime preemptively, integrating predictive techniques for both cybersecurity and system reliability to strengthen IIoT systems, thereby helping industries maintain continuous, secure, and safe operations.
Paper Presenter
Wednesday January 29, 2025 4:00pm - 4:15pm IST
Magnolia Hotel Crowne Plaza, Pune, India

4:00pm IST

AURACARE- A VIRTUAL MENTAL HEALTH ASSISTANT
Wednesday January 29, 2025 4:00pm - 4:15pm IST
Authors - Rakhi Bharadwaj, Nikhil Patil, Riddhi Patel, Raj Pagar, Suchita Padhye
Abstract - Integrated AI within the context of mental health has continued to be an emergent area of interest as more and more mental health applications in mobile devices, include chatbots. Such chatbots are the focus of this paper, and their design based on NLP, machine learning, and sentiment analysis is illustrated in order to help people who suffer from anxiety and depression providing them with individual therapeutic support. Despite these applications equip users with means of tracking moods, sharing concerns and getting ideas about mental health, they are not a license to practice. The study also responds to fundamental questions about their usability, usefulness, privacy, security, and protection of data. Because the need for mental health services continues to increase in parallel with technological progress, one has to maintain the harmony between innovation and ethics. Discussing the current mental health platforms, their technological model, and case studies of their usage, this paper also emphasizes the related risks – the misuse of AI in mental health treatment. Leaving aside the beautiful language and the fascinating discussions with the machines, the goal of the study is to provide theoretical perspectives as well as pragmatic suggestions for way AI can be used to improve mental health care responsibly.
Paper Presenter
Wednesday January 29, 2025 4:00pm - 4:15pm IST
Maple Hotel Crowne Plaza, Pune, India

4:00pm IST

Recipal : An AI Based Multi-modal Recipe Generator
Wednesday January 29, 2025 4:00pm - 4:15pm IST
Authors - Sunil Sangve, Rutuparn Kakade, Saamya Gupta, Sahil Akalwadi, Soham Pawar
Abstract - The paper introduces a novel and detailed approach for automated generation of fully customizable and user-centric food recipes. The system utilizes fine-tuned GPT2 and fine-tuned YOLOv9 to work efficiently. The user can input the available ingredients by uploading an image which is then processed by the fine-tuned YOLOv9 model to extract the ingredients present in the image. Alternatively, the user can also input ingredients manually. The user input also contains macro and micro nutrient levels, caloric values, cholesterol levels, taste and texture. Utilizing fine-tuned GPT2 LLM, the system then generates a unique recipe that follows the input. Web application developed by Next JS 14 with backend support from Flask, helps greatly in enhancing User experience. The system also utilizes Stable Diffusion to generate colorful recipe images for user reference of how the final recipe should look like.
Paper Presenter
Wednesday January 29, 2025 4:00pm - 4:15pm IST
Tulip Hotel Crowne Plaza, Pune, India

4:15pm IST

Leveraging Conversational AI to Streamline Troubleshooting and Predictive Maintenance in the Aviation Industry
Wednesday January 29, 2025 4:15pm - 4:30pm IST
Authors - Sakshi Prakash Masand, Sadhana Shashidhar, Animesh Giri
Abstract - High-stakes industries like the aviation industry demand minimal downtime requiring unified solutions that address all maintenance and troubleshooting needs. Existing solutions often function in isolation, requiring multiple systems for predictive analytics and manual-based repairs. To bridge this gap, this article presents a novel integration of machine learning based predictive maintenance and conversational AI for routine servicing and troubleshooting operations in industrial IoT systems, tailored to critical sectors such as aviation. A robust predictive maintenance model is built to predict the Remaining Useful Life (RUL) for aircraft components. Multiple traditional machine learning models like Random Forest, Support Vector Regression (SVR), XGBoost, and deep learning techniques like Gated Recurrent Unit (GRU) and Long Short Term Memory (LSTM) are compared for performance and accuracy, ultimately focusing on a refined time series specific approach. When the predicted RUL of a component falls below a predefined threshold, operators are automatically alerted to schedule maintenance. For interactive support, Rasa, a customisable conversational AI framework and a fine tuned LLaMA model provide instant and enterprise specific guidance, offering step by step instructions and reducing reliance on lengthy manuals. This solution combines predictive maintenance with dynamic assistance, saving valuable time and resources for the industry.
Paper Presenter
Wednesday January 29, 2025 4:15pm - 4:30pm IST
Magnolia Hotel Crowne Plaza, Pune, India

4:15pm IST

Leveraging Explainable AI in Healthcare using Precision Medicine for Treatment of Multiple Diseases
Wednesday January 29, 2025 4:15pm - 4:30pm IST
Authors - Anushka Ashok Pote, Laukik Nitin Marathe, Suvarna Abhijit Patil, Sneha Kanawade, Deepali Samir Hajare, Varsha Pandagre, Arti Singh, Rasika Kachore
Abstract - Due to the increase in vulnerability of different types of diseases, the use of artificial intelligence is seen to be rapidly increasing in the healthcare industry for creating systems that will provide diagnosis, treatment, and patient care. One of the major challenges that is faced in recent days is that most of the traditional healthcare systems are not transparent and comprehensible. This review explores the importance of Explainable Artificial Intelligence in order to make advancements in precision medicine, focusing on personalized treatment and disease prediction. Despite being powerful, traditional AI models function as "black boxes," which do not offer any insights into how decisions are made. This limits their application in critical sectors like healthcare where trust and accountability are crucial. Explainable AI makes systems more transparent and interpretable allowing healthcare professionals to understand and trust AI-driven insights. It exhibits significant enhancements in diagnostic accuracy and treatment personalization across various areas like oncology, cardiovascular disease, neurology, etc. The review performs comparisons between explainability driven models and traditional models. It reveals that XAI-based models offer better accuracy along with precision. It provides interpretable decision-making which makes them more suitable for clinical applications. Even though these systems exhibit certain challenges like computational complexity and need for standardized evaluation metrics. This paper highlights transformative potential of XAI in healthcare industry by fostering more ethical, transparent and patient-centered solutions. It is poised to revolutionize precision medicine by improving patient outcomes and exhibiting significant contributions in the healthcare industry.
Paper Presenter
Wednesday January 29, 2025 4:15pm - 4:30pm IST
Maple Hotel Crowne Plaza, Pune, India

4:15pm IST

AI-ENHANCED GLASSES FOR THE VISUALLY IMPAIRED
Wednesday January 29, 2025 4:15pm - 4:30pm IST
Authors - Austine S Manuel, Alana Ance John, Hamna Rafeeq, Thalhah Anas, Manoj V. Thomas
Abstract - Smart assistive technologies improve the independence and access to transportation of the vision-impaired people [1]. This paper presents AI-enhanced smart glasses designed to empower visually impaired by transforming their interaction with the environment. The system combines cutting-edge technologies to offer a comprehensive assistive solution. A path navigation algorithm, powered by YOLOv8, ensures safe mobility by detecting obstacles and guiding users with audio feedback. Integrated OCR capabilities enable real-time text reading, while an image captioning module provides detailed scene descriptions for enhanced situational awareness. The glasses incorporate a camera and an ultrasonic sensor, delivering robust performance in diverse scenarios, including detecting objects at close proximity where traditional algorithms fall short. With seamless audio output for user interaction, the proposed solution bridges the gap between technology and accessibility, promising independence and confidence for visually impaired individuals. This innovative fusion of AI and sensory inputs defines a new benchmark for assistive devices.
Paper Presenter
Wednesday January 29, 2025 4:15pm - 4:30pm IST
Tulip Hotel Crowne Plaza, Pune, India

4:30pm IST

Exploring the Emotional Impact of Layoffs: A Twitter- Based Sentiment Analysis with NLP Techniques.
Wednesday January 29, 2025 4:30pm - 4:45pm IST
Authors - P C Gita, Arthi R, R Geetha, Syed Hayath
Abstract - Employee layoffs have significant emotional and psychological impacts, often reflected in public discussions on social media platforms like Twitter. Employee layoffs not only affect the employee at stake but also impacts the brand image of the company. This study explores the sentiments and emotions surrounding layoffs through a comparative analysis using three natural language processing (NLP) tools: TextBlob, VADER, and the NRC Emotion Lexicon. A dataset of layoff-related tweets was collected over six months, pre-processed, and analyzed for sentiment polarity and emotional tone. The analysis revealed predominantly negative sentiments, with emotions like anger, sadness, and anticipation being prevalent. While TextBlob and VADER effectively gauged sentiment, VADER performed better in handling informal language, and the NRC Lexicon provided a more nuanced emotional profile. The study highlights the psychological toll of layoffs and the importance of employer transparency in mitigating anxiety. Future research should consider advanced NLP models like BERT for improved sentiment detection and track the evolution of layoff-related sentiments over time..
Paper Presenter
Wednesday January 29, 2025 4:30pm - 4:45pm IST
Magnolia Hotel Crowne Plaza, Pune, India

4:30pm IST

Voltage Visions: Contactless Voltage Detector
Wednesday January 29, 2025 4:30pm - 4:45pm IST
Authors - Pinakci Kathotia, Ridhima Rathore, Kush Gupta, Eshita Vijay, Ujwala Kshirsagar
Abstract - The developing subject of contactless voltage sensing and its revolutionary effects on the electrical sector are explored here. The basics of contactless voltage detection are first described, along with how it differs from conventional techniques. It then goes into detail on the different uses for this technology, such as non-invasive electrical testing and its employment in high-voltage environments. This paper also discusses the advantages of contactless voltage detection, such as improved efficiency and safety, as well as the difficulties that must yet be overcome for this technology to reach its full potential. Examples from everyday life are utilized throughout the paper to highlight the useful uses for contactless voltage detection.
Paper Presenter
Wednesday January 29, 2025 4:30pm - 4:45pm IST
Maple Hotel Crowne Plaza, Pune, India

4:30pm IST

Artificial Intelligence and Robotics in Mental Health Care
Wednesday January 29, 2025 4:30pm - 4:45pm IST
Authors - Rohith Aryan TG, Kashish Sharma, Sunil Kumar, Abhay Sharma
Abstract - A Revolution in the Diagnosis and Treatment Landscape. This paper brings forward how AI technologies can be utilized in earlier detection of mental illnesses, and how robotics provide therapeutic support to patients. Examining the various models, results and challenges on these technologies is aimed at putting attention on their perceived benefits and limitations in augmenting the health care of the mind. Comparative studies that include traditional treatment modalities will also be featured, along with recommendations for future research directions.
Paper Presenter
Wednesday January 29, 2025 4:30pm - 4:45pm IST
Tulip Hotel Crowne Plaza, Pune, India

4:45pm IST

Breast Cancer Detection Using Graphical Neural Networks on Histopathological Images
Wednesday January 29, 2025 4:45pm - 5:00pm IST
Authors - Aarv Mankodi, Sanya Jain, Vedant Mundada, Dinesh Kumar Saini
Abstract - Breast Cancer is a malignant tumor that occurs in the breast. It is a very serious threat to the health and well-being of women. Over the years the study of detection of this cancer using histopathology image recognition has become quite popular. Most of the methods, however, focus on creating new deep learning models or improving existing models like VGG or AlexNet. While these convolutional neural networks have been very successful in their implementation, it does not mean they do not have challenges, namely the problem of imbalance in datasets. It is for this reason that this paper instead tries to use graphs to solve the problem of breast cancer detection. For this paper, we use a graphical neural network that is trained to detect breast cancer in histopathology images. This is because the challenges faced by the deep learning models namely overcoming the imbalance in datasets may not be present in this graphical approach. It is also to find out if there are some previously unknown improvements in using a graph for detection instead of deep learning models.
Paper Presenter
Wednesday January 29, 2025 4:45pm - 5:00pm IST
Magnolia Hotel Crowne Plaza, Pune, India

4:45pm IST

Unmasking Reality: A Deepfake Detection Odyssey using Machine Learning Model
Wednesday January 29, 2025 4:45pm - 5:00pm IST
Authors - Vishalsinh Bais, Mansi Pagdhune, Wamik Khan, Gaurav Maske, Aditya Umredkar, Amol P. Bhagat
Abstract - The rise of deepfakes has raised questions about the veracity of digital content. This has led to a lot of research into reliable detection techniques. In this study, we introduce a new deepfakes detection approach based on the mesoNet architecture and the use of convolutional neural networks (CNNs). The proposed model has a multi-layer structure that includes convolutional layers, pooling layers, and dropout techniques to effectively extract and discriminate features. Training on a dataset that includes our own forged images and deepfakes, this model shows promising results in identifying manipulated content. With the activation function of leaky ReLU, our mesoNet model shows great promise in accurately distinguishing deepfakes from real images. Our experimental results demonstrate its effectiveness in distinguishing between forged and real images, demonstrating its value as a powerful tool in the fight against digitally manipulated content.
Paper Presenter
Wednesday January 29, 2025 4:45pm - 5:00pm IST
Maple Hotel Crowne Plaza, Pune, India

4:45pm IST

KrishiMitra: Facilitating Direct Market Access to Farmers
Wednesday January 29, 2025 4:45pm - 5:00pm IST
Authors - Vinayak Kanhegaonkar, Ayush Mahant, Ali Sayyed, Abha Shah, Aparna Junnarkar
Abstract - India’s farmers battle many challenges before they are able to sell their produce to the consumer, one such challenge is related to middlemen and market access. The current scenario is such that middlemen hold significant influence over the market. Middlemen are the intermediaries that farmers sell their produce to because of reasons such as lack of infrastructure, price fluctuations, limited storage facilities, lack of information and more. This causes farmers to lose much of their profit and overall reduces their income. Middlemen take substantial profits from their work; they buy for less from farmers and sell for more to consumers. Through our research we aim to create an app, KrishiMitra, for farmers to facilitate direct access to the market. KrishiMitra will be able to reflect real-time market prices, provide multilingual support, and have a bidding system. With this app we hope to provide farmers a platform to get fair prices, some bargaining power, proper and complete information about market prices and current trends. In the future, the application can be integrated with more services that address other challenges faced by farmers such as limited storage space for produce and climate change impacts on produce.
Paper Presenter
avatar for Abha Shah
Wednesday January 29, 2025 4:45pm - 5:00pm IST
Tulip Hotel Crowne Plaza, Pune, India

5:00pm IST

Innovative Crop Diseases Database Design for ASR-Based Prediction
Wednesday January 29, 2025 5:00pm - 5:15pm IST
Authors - Abhijeet G. Dhepe, Ashish R. Lahase, Shalini V. Sathe, Sagar Chitte, Pooja B. Abhang, Bharati W. Gawali, Sunil S. Nimbhore
Abstract - This study introduces a novel crop diseases database design for Automatic Speech Recognition (ASR) systems. It aimed to predict crop diseases, addressing the critical role of speech technology in agriculture. The research bridges the gap between farmers and advanced diagnostic tools by creating a specialized voice corpus focused on agricultural terminology and disease names. The dataset, which includes various phrases related to crop health management, was collected in naturally noisy environments from farmers in the Marathwada region. Recordings captured the authentic speech patterns of both male and female participants, encompassing various dialects and accents. This approach ensures real-world applicability, enhancing the reliability and relevance of ASR systems. By including native and non-native speakers, the study promotes linguistic inclusivity. It aims to empower farmers with accessible, speech-based disease prediction tools, ultimately fostering greater efficiency and resilience in crop management.
Paper Presenter
Wednesday January 29, 2025 5:00pm - 5:15pm IST
Magnolia Hotel Crowne Plaza, Pune, India

5:00pm IST

BERT-Based Educational Chatbots: Integrating NLU and Pragmatic Analysis for Improved Learning Outcomes
Wednesday January 29, 2025 5:00pm - 5:15pm IST
Authors - Shruti Gandhi, Charmy Patel
Abstract - This research presents an innovative framework for developing educational chatbots that redefine student support by integrating advanced natural language understanding (NLU), intent recognition, and pragmatic analysis. Leveraging machine learning techniques, including pre-trained models like BERT, the chatbot achieves state-of-the-art performance in recognizing user intents and delivering contextually relevant responses. By addressing the limitations of traditional systems, such as poor personalization and difficulty in handling nuanced queries, this framework enables dynamic, adaptive, and engaging interactions. The chatbot transcends conventional query handling through pragmatic analysis, allowing it to interpret subtle nuances, emotional states, and real-world contexts. This ensures personalized responses that align with individual learning needs, fostering deeper student engagement and comprehension. Finetuned with diverse datasets and instructional materials, the system is robust and scalable, making it suitable for a wide range of educational applications. This approach also emphasizes human-like interaction, combining emotional intelligence with context-aware capabilities to create a supportive learning environment. By enhancing response accuracy, adaptability, and user engagement, the chatbot sets a new benchmark in educational technology. Ultimately, this research demonstrates transformative potential in creating intelligent, scalable, and highly effective tools for modern education, paving the way for a more personalized and interactive learning experience.
Paper Presenter
Wednesday January 29, 2025 5:00pm - 5:15pm IST
Maple Hotel Crowne Plaza, Pune, India

5:00pm IST

Multi-label Classification of Chest X-ray Images: A Privacy Preserving Method with Federated Learning
Wednesday January 29, 2025 5:00pm - 5:15pm IST
Authors - Madhuri Wakode, Geetanjali Kale
Abstract - Deep learning has many applications in healthcare especially for disease prediction from complex images. One such application is to predict diseases from chest X-rays (CXR). These models need huge amounts of data available for training. A single healthcare facility may struggle to collect sufficient data to build robust and efficient models. The apparent solution is collaboration among multiple healthcare facilities to use their data to build efficient models together. However, facilities may not want to share the patient’s sensitive data with other facilities or with the central server. Federated Learning (FL) allows multiple parties to build models without sharing their data with each other. FL allows parties to train the model locally on their private data and only share the trained model parameters to the server. Server averages the model parameters sent by all the parties to build a robust global model. Server sends this updated model to each party who then again trains the model locally. This process continues till the model convergence. We propose using federated learning average on a large CXR dataset for multi-label classification. Our results show that federated learning achieves the accuracy of ~82% as compared to ~91% of that of traditional centralized training method. FL with more robust algorithms and larger datasets, can achieve performance comparable to centralized approach with an added advantage of collaborative learning with privacy preservation.
Paper Presenter
Wednesday January 29, 2025 5:00pm - 5:15pm IST
Tulip Hotel Crowne Plaza, Pune, India

5:15pm IST

An Optimized and Secure Data Sharing Framework Using DCT Compression and CP-ABE Access Control
Wednesday January 29, 2025 5:15pm - 5:30pm IST
Authors - Namrata Naikwade, Shafi Pathan
Abstract - This paper presents a cost efficient and secure framework for data storage in cloud environments, It uses Discrete Cosine Transform (DCT) for data compression and Ciphertext-Policy Attribute-Based Encryption (CP-ABE) for fine-grained access control. The framework focuses on some of the main challenges in cloud storage, such as storage costs minimization, data privacy and security along with swift access revocation. Framework significantly reduces storage overhead by integrating DCT which compresses image data effectively while maintaining data quality. This storage optimization strategy enhances the cost-effectiveness of cloud storage making it suitable for large scale applications. CP-ABE provides a secure data access management by enforcing attribute based encryption which enables flexible and precise control over who can access the data ensuring privacy and security even in untrusted cloud environments. It also provides a rapid access revocation to safeguard data from risks associated with unauthorized access which is a critical risk in dynamic cloud environments. Experimental evaluations shows that the framework minimizes storage costs by up to 50% while significantly improving data security and efficiency demonstrating that proposed framework is a practical and scalable solution for secure and cost-efficient data management, addressing the needs of modern cloud-based systems.
Paper Presenter
Wednesday January 29, 2025 5:15pm - 5:30pm IST
Magnolia Hotel Crowne Plaza, Pune, India

5:15pm IST

MorseMate: Portable Morse Code to Text Converter
Wednesday January 29, 2025 5:15pm - 5:30pm IST
Authors - Komal V. Papanwar
Abstract - MorseMate is a user-friendly Morse code converter designed to simplify the process of converting Morse code to alphanumerical values by allowing input through three buttons for three separate functions involved in the transmission of the code. This approach eliminates the need for timing-based input, making Morse code more accessible and easier to use. The device, powered by an ESP8266 microcontroller and featuring a 128x64 Organic Light Emitting Diode (OLED) display, converts Morse code into readable text in real-time. Users can input their sequences with the press of a button, and a long press of the submit button clears the display, allowing for continuous use. Custom I2C configuration provides flexibility in hardware setup, while the compact design ensures portability. This paper elaborates on how the system combines simplicity, efficiency, and practicality, to make Morse code more accessible to a wider audience.
Paper Presenter
Wednesday January 29, 2025 5:15pm - 5:30pm IST
Maple Hotel Crowne Plaza, Pune, India

5:15pm IST

Design of Resource,Delay & Power Efficient Single Cycle Signed Multiplier
Wednesday January 29, 2025 5:15pm - 5:30pm IST
Authors - Hepin Gondaliya, Parth Monpara, Dhaval Shah
Abstract - Multiplier is a crucial factor influencing processor performance, making its optimization vital for efficient computing. This paper introduces a multiplier design that achieves remarkable improvements, reducing resource utilization by more than half, logic delay to nearly a quarter and total power consumption to less than a quarter of compared to conventional radix-2 booth’s multiplied The proposed design was synthesized and implemented using Xilinx Vivado on a Zedboard FPGA, demonstrating its effectiveness and scalability for modern FPGA-based systems.. . .
Paper Presenter
Wednesday January 29, 2025 5:15pm - 5:30pm IST
Tulip Hotel Crowne Plaza, Pune, India

5:30pm IST

Self-Supervised Learning in Image Classification
Wednesday January 29, 2025 5:30pm - 5:45pm IST
Authors - Deep Dey, Hrushik Edher, Lalit M. Rao, Dinesh Kumar Saini
Abstract - SSL is an important technique in machine learning which has high value for image classification tasks because acquiring labeled data is expensive. SSL trains on the labeled data and then we apply it predict the unlabeled dataset. Once it is trained, it can be further fine-tuned with the smaller labeled subsets for specific task. Recently, SimCLR, BYOL, and MoCo have shown impressive performances. SimCLR uses contrastive learning to train models by maximizing similarity between pairs classified under the same class and minimizing it between pairs belonging to different classes. BYOL, on the other hand, does not rely on negative pairs but implements a two-network architecture that eases training. MoCo develops a dynamic dictionary through which scaling SSL becomes efficient on massive data and performs well even with the smallest of batches. We can evaluate this models on CIFAR-10 and ImageNet, SSL approach is used in the areas where the labeled training data is scarce. In some research it is shown that SSL is competitive with SL when the amount of labeled training samples are small.
Paper Presenter
Wednesday January 29, 2025 5:30pm - 5:45pm IST
Magnolia Hotel Crowne Plaza, Pune, India

5:30pm IST

Survey on Emotion Recognition in humans using Speech as a modality
Wednesday January 29, 2025 5:30pm - 5:45pm IST
Authors - Tanuja Zende, Ramachandra. Pujeri, Suvarna Pawar
Abstract - Human Emotion Recognition (HER) has gained considerable attention in recent years, driven by advances in machine learning, acoustic signal analysis and natural language processing. The advancements in HER using speech as a principal modality is addressed in this survey systematically. The importance of emotional intelligence in HCI, social robotics and mental health assessment in this review comprehends a complete analysis of approaches including feature extraction techniques, classification algorithms and data representation used in the field of speech emotion recognition. Furthermore, in this survey, existing methods into traditional rule-based systems, machine learning algorithms and state-of-the-art deep learning frameworks, stressing on the strengths and limitations are discussed. Additionally, thoughtful challenges such as the density of human emotions, the influence of contextual factors, and the need of annotated datasets to train robust emotion recognition systems find their involvement in this work. Current trends in multimodal emotion recognition (MER) and the incorporation of speech with other modalities are also discussed to provide a complete view. This amalgamation of existing literature aims to notify future research directions in emotion recognition systems, enhancing their pertinency across varied fields.
Paper Presenter
Wednesday January 29, 2025 5:30pm - 5:45pm IST
Maple Hotel Crowne Plaza, Pune, India

5:30pm IST

Integration of Quantum Supervised Learning for Intrusion Detection System: A Review
Wednesday January 29, 2025 5:30pm - 5:45pm IST
Authors - R. R. Bhoge, Ranjit R. Keole, Pravin P. Karde
Abstract - In the rapidly evolving landscape of network security, Intrusion Detection Systems (IDS) play an indispensable role in distinguishing normal network traffic from anomalies. While traditional machine learning models have achieved notable success, the introduction of quantum-assisted techniques opens new avenues for improving accuracy and reliability. This review delves into the application of quantum supervised learning for Intrusion Detection, emphasizing recent breakthroughs and comparing the advantages of quantum methods to classical counterparts. As digital networks become increasingly interconnected, the demand for robust Intrusion Detection Systems has grown exponentially. This paper investigates quantum-enhanced cybersecurity solutions, particularly the incorporation of quantum supervised learning, to improve the detection and classification of network intrusions. The review discusses the theoretical principles of quantum supervised learning, its unique attributes, and the advancement of quantum-enabled IDS. This paper provides a detailed overview of how quantum computing is revolutionizing machine learning for cybersecurity, with an emphasis on the enhanced capabilities of IDS through quantum-assisted methodologies.
Paper Presenter
Wednesday January 29, 2025 5:30pm - 5:45pm IST
Tulip Hotel Crowne Plaza, Pune, India

5:45pm IST

Challenges and Future Directions in Fishing Area Identification: Balancing Ecosystem Preservation with Industry Sustainability
Wednesday January 29, 2025 5:45pm - 6:00pm IST
Authors - Prateeksha Chouksey, Tanvi Rainak, Piyush Shastri, Jayshree Karve, Ritesen Dhar
Abstract - Identifying fishing spots is vital for sustainable fisheries management, conservation, and optimizing fishing activities. This paper provides a comprehensive review of Artificial Intelligence, Machine Learning, Big Data, and Data Analytics in enhancing fishing spot detection, surpassing the limitations of traditional methods such as fisher experience and historical catch data. These advanced approaches improve accuracy in locating productive fishing areas by integrating remote sensing, Geographic Information Systems, and real-time data from sonar and satellite imagery. The study emphasizes the role of environmental factors—such as water temperature, salinity, and ocean currents—in influencing fish distribution and explores the impact of technological advancements on eco-friendly fishing practices. Current challenges include data availability, environmental variability, and the need for interdisciplinary collaboration. Additionally, the paper outlines the transformative potential of these technologies to optimize resource utilization and preserve marine ecosystems, suggesting future research directions to address existing gaps. As these methods continue to evolve, their wider adoption is expected to support the sustainability of fisheries and environmental conservation significantly.
Paper Presenter
Wednesday January 29, 2025 5:45pm - 6:00pm IST
Magnolia Hotel Crowne Plaza, Pune, India

5:45pm IST

Effective Segmentation of Grape Leaves using Segment Anything Model - 2
Wednesday January 29, 2025 5:45pm - 6:00pm IST
Authors - Akshat Vashisht, Ishika Tekade, Juhi Shah, Aniket Sawarn, Deependra Singh Yadav, Palash Sontakke, Rajkumar Patil
Abstract - Segmenting grape leaf stress condition accurately is a critical step in precision agriculture, as it enables early detection and treatment to mitigate crop losses. In this research, we propose a novel approach leveraging the Segment Anything Model 2 (SAM-2) for precise segmentation of stress condition regions on grape leaves. SAM-2 is a foundation model for promptable visual segmentation in images and videos. This model can generate high-quality masks with minimal user input, which makes it an ideal tool for such tasks. The SAM-2 model was tested on field images and we achieved an accuracy of nearly 70% without fine-tuning. Experimental results demonstrate that SAM-2 outperforms traditional segmentation models like U2Net and V-net. Data augmentation can improve the performance of SAM-2, especially in challenging tasks such as detecting early-stage leaf spots or stress condition symptoms in overlapping leaves. Techniques such as rotation, adjusting brightness, and scaling, can simulate different conditions, balance the dataset and improve generalization. This helps SAM-2 to adapt to different scenarios and improve its ability to detect complex patterns. This shows the potential of SAM-2 in agricultural applications and provides a framework that can be integrated into advanced plant monitoring systems. By automating the segmentation process with minimal user intervention, SAM-2 significantly reduces the labour-intensive task of manual stress condition detection, thus saving time and resources in agricultural operations. Integrating SAM-2 into the grape leaf stress condition detection pipeline enhances the precision and reliability of stress condition identification systems. Furthermore, its adaptability across different segmentation tasks provides a foundation for scalable and automated plant health monitoring systems. This study establishes SAM-2 as a transformative tool for advancing sustainable farming practices and precision agriculture.
Paper Presenter
Wednesday January 29, 2025 5:45pm - 6:00pm IST
Maple Hotel Crowne Plaza, Pune, India

5:45pm IST

Machine Learning in Swing Trading: The Application of K Nearest Neighbors for Stock Prediction
Wednesday January 29, 2025 5:45pm - 6:00pm IST
Authors - Prasanna Tupe, Parikshit N. Mahalle
Abstract - This study presents a machine learning-based method for using the K-Nearest Neighbors (KNN) algorithm to optimize swing trading strategies. The algorithm forecasts stock price fluctuations over the next seven days using historical stock market data and offers various levels of nuanced trading signals. By empowering traders to make more accurate and knowledgeable judgments, this method outperforms conventional binary buy-and-sell recommendations. The KNN technique was selected due to its instance-based learning, non-parametric nature, and simplicity, which make it interpretable and computationally efficient. The model's accuracy rate demonstrated how well it could forecast changes in stock prices. This study provides a workable approach for swing traders looking to optimize their profits while highlighting the important role that machine learning plays in tackling the difficulties associated with stock market prediction. This study lays the groundwork for using machine learning to enhance trading tactics and financial market decision-making.
Paper Presenter
Wednesday January 29, 2025 5:45pm - 6:00pm IST
Tulip Hotel Crowne Plaza, Pune, India

6:00pm IST

Human Breast Cancer Detection using MATLAB
Wednesday January 29, 2025 6:00pm - 6:15pm IST
Authors - Arpita Mahakalkar, Karan Deshmukh, Kruthika Agarwal, Sudhanshu Maurya, Firdous Sadaf M. Ismail, Rachit Garg
Abstract - Breast cancer remains a driving cause of mortality among ladies around the world, emphasizing the requirement for progressed location strategies. This considers creating a novel Computer-Aided Conclusion (CAD) framework leveraging MATLAB to progress the precision and proficiency of breast cancer location. The framework utilizes mammographic pictures and applies progressed measurable extraction procedures to analyze key characteristics, such as mass shape and edges. These highlights assist in classifying utilizing machine learning calculations, counting neural systems, and back vector machines. A one of a kind integration of wavelet change and multilayer perceptron strategies illustrated critical enhancements in recognizing Incendiary Breast Cancer (IBC) from non-IBC cases. The proposed approach beats conventional strategies, advertising improved symptomatic unwavering quality, decreased execution time, and tall exactness in cancer classification. This work underscores the potential of joining progressed machine learning procedures and picture-preparing apparatuses within the early and exact location of breast cancer, eventually supporting radiologists and decreasing demonstrative challenges.
Paper Presenter
Wednesday January 29, 2025 6:00pm - 6:15pm IST
Magnolia Hotel Crowne Plaza, Pune, India

6:00pm IST

Conversational AI-Driven DNN Model for Cyberattack Detection and Mitigation in Industrial IoT Networks
Wednesday January 29, 2025 6:00pm - 6:15pm IST
Authors - Safwan Ahmed, Rojas Binny, S N Velukutty, Animesh Giri
Abstract - Industrial Internet of Things (IIoT) networks have started to play a crucial role in revolutionizing manufacturing and Industry 4.0. However, the distributed nature of IIoT and its relative infancy make it a prime target for cyberattacks. This paper proposes a new approach to address the threats faced by industries using a conversational Artificial Intelligence (AI)-interfaced deep neural network model for detecting attacks on an IIoT network. The proposed approach extends to threat mitigation and has been evaluated using an extensive IIoT network traffic data set, demonstrating 93% accuracy in detecting 14 of the most common threats plaguing the industry. This model introduces the integration of conversational AI with deep learning, offering a user-friendly interface for naive users and accurate threat detection. The broader impact of this work lies in its potential to significantly enhance access to a robust and accurate real-time cyber-threat detection and mitigation system, thus contributing to a more secure and resilient industrial landscape.
Paper Presenter
Wednesday January 29, 2025 6:00pm - 6:15pm IST
Maple Hotel Crowne Plaza, Pune, India

6:00pm IST

Innovative Offline Learning: AI-Driven Transcription, Real-Time Communication, and Learning Management Systems for Remote Education
Wednesday January 29, 2025 6:00pm - 6:15pm IST
Authors - Atharva Madhukar Nimbalkar, Madhukar Nimbalkar, Madhura Gondhalekar, Yashvendra Singh Dhandal, Parikshit Mahalle, Pankaj Chandre
Abstract - The digital divide continues to pose significant challenges to delivering quality education, particularly in remote and underserved regions with limited or no internet connectivity. This paper explores the innovative integration of three advanced technological solutions within the Spacelink: Educational System, designed to bridge this gap. The system incorporates an AI-driven transcription and translation model using Whisper, a web-based Learning Management System (LMS), and a real-time communication platform utilizing WebRTC. These tools are tailored for offline functionality, operating effectively through LAN-based communication to ensure uninterrupted educational services. The AI transcription system enhances accessibility by converting spoken content into text and supporting multiple languages, addressing linguistic diversity and inclusivity. The LMS provides a robust offline platform for content delivery, course management, and student assessment, synchronizing data seamlessly when connectivity is restored. Meanwhile, the WebRTC-based communication tool facilitates real-time audio and video interactions optimized for low-bandwidth environments, promoting interactive learning experiences. Together, these technologies offer a holistic solution to overcome educational barriers, enabling equitable access to high-quality learning resources and fostering collaborative education in disconnected settings.
Paper Presenter
Wednesday January 29, 2025 6:00pm - 6:15pm IST
Tulip Hotel Crowne Plaza, Pune, India
 
Thursday, January 30
 

9:30am IST

Opening Remarks
Thursday January 30, 2025 9:30am - 9:35am IST
Moderator
Thursday January 30, 2025 9:30am - 9:35am IST
Virtual Room A Pune, India

9:30am IST

Opening Remarks
Thursday January 30, 2025 9:30am - 9:35am IST
Moderator
Thursday January 30, 2025 9:30am - 9:35am IST
Virtual Room B Pune, India

9:30am IST

Opening Remarks
Thursday January 30, 2025 9:30am - 9:35am IST
Moderator
Thursday January 30, 2025 9:30am - 9:35am IST
Virtual Room C Pune, India

9:30am IST

Opening Remarks
Thursday January 30, 2025 9:30am - 9:35am IST
Moderator
Thursday January 30, 2025 9:30am - 9:35am IST
Virtual Room D Pune, India

9:30am IST

Opening Remarks
Thursday January 30, 2025 9:30am - 9:35am IST
Moderator
Thursday January 30, 2025 9:30am - 9:35am IST
Virtual Room E Pune, India

9:30am IST

Aligning Graduate Attributes with Industrial Revolution 4.0 – Recommendations for Indian Technical Institutes
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - Hemlata V. Gaikwad, Sushma S. Kulkarni
Abstract - The present article explores the dynamic changes in required graduate attributes to develop industrial revolution 4.0 (IR4) ready. The assurance of graduate attributes assumes more importance given the fact that only 20% of the engineers are employable for any job in the knowledge economy (NER, 2019). Indian Engineering institutes across India are facing pressure and striving hard to equip the graduates with the right set of attributes to enhance their employability. The authors examine the shifts of graduate attributes from industrial revolution I to IV to perceive the most important ones from employment perspective. Second, we recommend the strategies at multiple levels to develop the identified attributes by mapping them with program educational objectives and finally we argue that all these strategies must be aligned to strengthen outcomes based education and as a result prepare employable and environmentally sensitive graduates responsible for a sustainable future..
Paper Presenter
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room A Pune, India

9:30am IST

Analyzing the vulnerabilities and security risks associated with IOT devices and networks
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - Payal Khode, Shailesh Gahane, Arya Kapse, Pankajkumar Anawade, Deepak Sharma
Abstract - IoT has brought significant changes in different aspect of the society, social-relational and economic life and how society interacts with environment and physical space. Starting from light switches, locks that open doors, self-driving cars, various bracelets that track a person’s health state, sensors in industries that control machines, the Internet of things brought us to the world of increased connectivity that is filled with various conveniences, effectiveness, and automation. However, this link has given a new level of added insecurity since this makes it easier for cybercriminals to get into systems, manipulate important services, and steal important information. Consequently, this research paper provides the analysis of the complex nature of IoT security and identifies the primary threats inherent in IoT devices and networks. Comparing these models, the outcome is also presented, according to which these vulnerabilities can lead to an organized attack on data and personal accounts that can encompass the theft of personal information, attack on organizational accounts, and disruption of infrastructure and utilities. Moreover, the paper presents material considering IoT security issues as complex would be an understatement, one having to do with its technical, non-standards, and dynamic nature. Analyzing such vulnerabilities and risks, this paper intends to clarify the significance of sound security measures and protective actions that must be put in place to reduce possible threats in the IoT sphere.
Paper Presenter
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room A Pune, India

9:30am IST

Enhancing Image Compression via Interval Arithmetic Vector Quantization and CNN
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - Rohini Hongal, Rahil Sanadi, Supriya Katwe, Rajeshwari .M
Abstract - Image compression involves reducing digital image file sizes while keeping their quality intact. Interval arithmetic, a mathematical technique, deals with ranges of values rather than precise numbers, allowing for robust computations across various applications. Vector quantization is a data compression method that groups similar data points to represent data efficiently. This study attempts to create an advanced image reduction method by integrating Convolutional Neural Networks (CNNs) and Interval-Arithmetic Vector Quantization (IAVQ). The study also examines and validates the practical relevance of attribute preservation. In the compression stage, the trained CNN is employed to extract features from input images, and the interval-arithmetic-based quantization maps these features to the predefined quantization intervals, considering attributes like sum, difference, and product. The proposed framework involves two main stages: training and compression. During the training stage, a CNN is trained to learn feature representations that encapsulate important image characteristics such as contrast and luminance intensity. This project thoroughly examines Normal Compression and Interval Arithmetic Compression, with the latter displaying promising results. Notably, Interval Arithmetic Compression consistently yields superior outcomes in pixels per second, compression file size, and PSNR compared to normal compression techniques. The IAVQ method achieves nearly 20 per cent higher compression quality, reduces pixel count by 0.5 to 0.9 per second, lowers PSNR ratio by 0.7 to 2.3 dB, and saves 11 to 19 KB of storage compared to the standard method across all image types.”
Paper Presenter
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room A Pune, India

9:30am IST

Enhancing Law Enforcement with Open-Source Intelligence (OSINT): Strategies, Tools, and Ethical Implications
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - Harsh Murjani, Kabir Mota, Nishat Shaikh
Abstract - This research paper explores the critical importance of Open Source Intelligence (OSINT) in modern law enforcement practices. The study aims to elucidate the essential role of OSINT tools and methodologies in enhancing the capabilities of law enforcement agencies to gather, analyze, and utilize intelligence from publicly available sources. The findings underscore the significant impact of OSINT on various aspects of law enforcement operations, including proactive threat detection, investigative support, and strategic decision-making. Moreover, the study identifies key benefits of OSINT, such as its cost-effectiveness, scalability, and ability to provide timely and actionable intelligence.
Paper Presenter
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room A Pune, India

9:30am IST

Malware Attack Detection using Network flows with Machine Learning
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - Abdul Razzak R Yergatti, Prajwal Shiggavi, Mohammed Azharuddin, Suneeta V Boodihal
Abstract - Traditional defense solutions like intrusion detection and thorough packet inspection are not so accurate. These techniques include signature-based detection, which uses known patterns, and heuristic or behavioral analysis, which evaluates program behavior to detect suspect activities. The demand for more advanced and continuously innovative methods to combat malware, botnets, and other malicious activities is urgent. Machine Learning (ML) emerged as a promising approach due to increasing computing power and reduced costs, offering potential as either an alternative or complementary defense mechanism to enhance detection accuracy by learning from large datasets of known malware behaviors. This investigation delves into the capability of Machine Learning in detecting malicious malwares within a network. Initially, a thorough analysis of the Netflow datasets is conducted, resulting in the extraction of 22 distinct characteristics. Subsequently, a feature selection procedure is employed to compare all these characteristics against each other. Following this, five machine learning algorithms are assessed using a NetFlow dataset that encompasses typical botnets. The outcomes reveal that the Random Forest Classifier successfully identifies over 95% of the botnets in 8 out of the 13 scenarios, with detection rates exceeding 55% in the most challenging datasets.
Paper Presenter
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room A Pune, India

9:30am IST

Managing Artificial Intelligence Technology Oriented Tools for Assessment and Evaluation in Education System
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - Shailesh Gahane, Payal Khode, Arya Kapse, Deepak Sharma, Pankajkumar Anawade
Abstract - An essential concern of our community is enhancing education. Everyone would wish to see a smaller class and school, but technology cannot materialize it physically. For the instructor, though, technology may function as a "force multiplier." For the guidance of researchers, a comprehensive questionnaire is formulated for pertinent data from the primary source, and a survey is conducted in the targeted area for determining influence. Using the questionnaire, in-depth conversations were carried out with certain main data sources toward gaining an understanding of the perspectives, mindsets, and behaviors. This would give the researchers any kind of recommendation they may deem necessary and helpful. Statistical tools such as tabulations, grouping, percentages, averages, hypothesis testing, etc. are applied to process the questionnaire. The following are considered when it comes to streams: arts, science, commerce, engineering, medicine. Because technology is always changing, even though we update these pages often, we cannot promise that all the information will remain current. Please visit our technology-focused top page to view the most recent and pertinent tech headlines. In the information age, we can now communicate with one another in ways that were before unthinkable. Educators and administrators are facing a new problem as they try to figure out. Technology has advantages. Using web conferencing or other tools, parents and teachers may be able to work together virtually. This holds true whether they use the internet for virtual communication with professionals or fellow students, or for research purposes. These programs also impart to students the technology skills needed to succeed in the modern workforce.
Paper Presenter
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room A Pune, India

9:30am IST

Realtime Human Action Recognition using Machine Learning Prediction Model and Media pipe
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - More Swami Das, N.N.S.S.S.Adithya, Gunupudi Rajesh Kumar, R. P. Ram Kumar
Abstract - In image processing and computer vision, human activity detection is a significant activity. There are various techniques and approaches for key point detection that identify the external Skelton key points. Some methods will detect key points and recognise the human pose. The proposed work aims to utilise the Random Forest (RF) approach and classify the human activity into 15 classes using media pipes. The library trained with 30,000 samples. The objective of this paper is to capture the human face, like the angles of limbs and key points, and train the machine learning model to recognize the human action using media pipe. In the future, we can extend this work to capture real-time video poses using intelligent methods for key points to identify the actions of human facial expressions.
Paper Presenter
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room A Pune, India

9:30am IST

Revolutionizing Bone Fracture Diagnosis through Deep Learning
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - Muskan Dave, Mrugendrasinh Rahevar, Arpita Shah
Abstract - These days, bone breaks are an all-over issue that can be invited on by a couple of special events, similar to lamentable lifestyle decisions and car collisions. The human body's ability to move and expect different shapes depends upon bones. This can cause serious misery, developing, and inconvenience moving the influenced appendage. X-radiates are a useful and sensible technique for finding breaks. For the patient, missing a break has serious outcomes. The revelation of bone breaks using CNNs ought to be conceivable using a couple of unmistakable estimations. This paper proposes a couple of regularly used computations incorporate move learning estimation using a pre-arranged CNN model, as VGG. The proposed method includes optimized time, resources and different CNN models. Multi-view CNN estimation uses various X-pillar viewpoints on comparative bone, similar to front back and equal points of view, to chip away at the precision of break revelation. Hybrid CNN computation joins various CNN models, similar to a 2D CNN and 3D CNN, to deal with the precision of break disclosure. Existing systems are being investigated and developed ceaselessly, and it similarly depends upon the dataset open, the kind of imaging and the essential of the use case. Significant learning uses additional mystery layers of the ANN that rely upon mind associations. This paper summarise significant learning approaches for separating bone breaks.
Paper Presenter
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room A Pune, India

9:30am IST

Secured Cloud-based Applications and its Effective Utilization
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - More Swami Das, Gunupudi Rajesh Kumar, R. P. Ram Kumar
Abstract - Cloud-Computing enables ubiquitous on demand, convenient network access to a shared pool of computing resources. Cloud services are provided by organizations that manage huge data. The problem is to provide cloud security and availability of services to all authenticated users. In this work, We use Cloud Security Model (i.e. Encryption and description of cloud data) to enhance security and also increase availability through the use of virtualization technologies like Hyper-V and efficient utilization of cloud services. In the future, we can extend this architecture to prevent hacking and trusting models.
Paper Presenter
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room A Pune, India

9:30am IST

Technology-Driven Cycle Time Reduction: The Impact of Advanced Cutting Tools on Production Performance
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - Vanishree Pabalkar, Ruby Chanda, Debjyoti
Abstract - When it comes to n recent manufacturing, lines of Production and the Work that is in process are considered as the crucial aspects that determine the Production Performance. TAKT is Takzeit, which means Rhythm of Music. TAKT is a defined as the tool that measures the methods of Production. TAKT times is nothing but the complete time within the defined range. The current study explains the way in which technologically advanced cutting tools can be used for cycle time reduction. The impact of these advanced cutting tools on Production Performance is studied here. The objective is to increase productivity of a particular aspect. This is done by assessing the challenges that occur in the Production process. The ways to do away with the challenges that create obstacles are also identified. The Action taken to enhance Production is discussed. The existing mapping that is termed as the value stream mapping has been considered to explain the current situation of Production and provide suitable solutions. Technologically advanced cutting tools reduce the cycle time and reduce the cost in the Production process.
Paper Presenter
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room A Pune, India

9:30am IST

A Comprehensive analysis of transforming Fashion using Generative AI
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - Ketaki Bhoyar, Suvarna Patil
Abstract - Fashion is the canvas of our identity. Fashion can be so inclusive, expressive and sustainable. In the growing landscape of fashion, every individual face an overwhelming array of choices. It becomes difficult to discover a personalized wardrobe that reflects their preferences, taste and needs. Traditional Fashion Recommendation Systems (FRSs) limits their ability to scale and adapt the ever-growing styles as they heavily rely on manual design. Around the world, a large number of users buy cloths online through the e-commerce websites. These websites primarily use recommender systems. Appropriate recommendations given by FRS helps to enhance user satisfaction and makes it more enjoyable and accessible. Artificial Intelligence (AI) tools have revolutionized FRS enabling them to consume beyond conventional methods by taking in contextual data, user preferences and visual content for recommendations with a more individualized suggestion. Recently, Generative Adversarial Networks (GANs) have emerged as a potent technique to enhance these systems by generating diverse fashion designs with high fidelity. In this paper, a systematic review of parameters used to evaluate FRS using Generative Algorithms is discussed. Various parameters to evaluate system performance and the recommendation quality are analyzed. Detailed analysis of the input parameters, to be considered to design the efficient AI based FRS (AI-FRS) is also presented. Along with this, research gaps are explored by surveying numerous review papers. This review will help in deciding the evaluation parameters to develop and examine more efficient AI based FRS.
Paper Presenter
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room B Pune, India

9:30am IST

An Online Platform for Defence Aspirants: SSB MOCKS
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - Shobha K, Rajashekhara S
Abstract - The Service Selection Board (SSB) evaluates candidates for admission to military services like the Indian Army, Navy, and Air Force through a rigorous five- to six-day selection process. This process assesses a candidate’s psychological and physical fitness, communication skills, and leadership qualities. Despite its importance, the low selection rate highlights a lack of preparation platforms for aspirants. Many candidates cannot afford offline coaching, and no comprehensive online platforms exist to simulate SSB tests. The proposed solution is an interactive online platform offering real-time test simulations, feedback, and guidance, replicating the SSB interview experience to enhance aspirants’ chances of success.
Paper Presenter
avatar for Shobha K
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room B Pune, India

9:30am IST

Cybersecurity risks and challenges in transition to remote work during the COVID-19 Pandemic: A focus on employee behavior and organizational vulnerabilities
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - Payal Khode, Shailesh Gahane, Arya Kapse, Pankajkumar Anawade, Deepak Sharma
Abstract - The COVID-19 pandemic has led to a widespread trend toward remote work, drastically altering the nature of the traditional workplace. While there are many advantages to working remotely, such as flexibility and less time spent traveling, there are also major cybersecurity risks. The inherent vulnerabilities in technologies used for remote work pose a persistent threat to cybersecurity. But social distancing measures imposed by the pandemic have made workers work from home, which has increased internet usage. These widespread modifications have been used by malicious hackers to launch extensive phone scams, phishing attacks, and other computer-based exploits. Organization have quickly embraced remote work without fully understanding the impact on cybersecurity. Because remote work policies have been widely adopted without first consulting cybersecurity experts or implementing comprehensive security measures, there are now more vulnerabilities. This study focuses on people because they are the weakest link in cybersecurity. It highlights how important it is to protect business and personal information when working from a distance. The study looks at the cybersecurity risks associated with changing employee behaviors during the transition to remote work in light of the COVID-19 pandemic. The aim of this research is to investigate the cybersecurity risks and challenges that companies and organizations encounter when workers change their work habits to work remotely during the COVID-19 pandemic.
Paper Presenter
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room B Pune, India

9:30am IST

E-commerce Platform for Plant Nursery with Virtual Green Space Customizer
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - Ananya Solanki, Leander Braganza, Aarol D’Souza, Sana Shaikh
Abstract - For plant lovers, there has always been a barrier to accessing a wider variety of plants. This restriction is due to the absence of a dedicated marketplace to buy and sell plants. This platform includes an interactive Augmented Reality (AR) feature that enables users to visualize the plants they select in their selected environment. Furthermore, it utilizes location-based Air Quality Index (AQI) and recommends plants according to the user’s location. This Platform will educate the users about the plants by providing plant care tips.
Paper Presenter
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room B Pune, India

9:30am IST

Emotion Detection for Adaptive Experiences
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - Prajkta Dandavate, Ameya Badge, Mohit Badgujar, Aditi Badkas, Rutuja Badgujar, Orison Bachute, Vedant Badve
Abstract - This paper presents a cutting-edge combination system designed to integrate personalized music recommendations with real-time face-based emotion recognition by using adaptive emotion-driven user interaction. The approach demonstrates how, given a continuously streamed video coming from a PC camera, advantage is taken to analyze emotions as the CNN feeds in user-defined emotions in the emotion categorization task and indicates that such categories of emotions have been quite accurately identified or classified up to around 65% into defined categories, say for example sadness, happiness, and many more. It detects emotions within a room in real time while online building up a playlist of music. The system remains smooth and adaptive, constantly readjusting the emotional responsiveness of the interaction, supported by a multi-threaded architecture. In addition to entertainment, the paper explores other applications in home automation, healthcare, and mental health as well as opportunities for emotion-driven content and advertisements that match the real-time emotional states of users. It brings to the foreground the prospects of machine learning and the possibility of real-time processing in creating deeply personalized, emotionally driven user experiences across diverse settings.
Paper Presenter
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room B Pune, India

9:30am IST

Integrating Artificial Intelligence and Blockchain for Enhancing Occupational Health and Safety Management in the Construction Sector of Maputo, Mozambique
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - Shailesh Gahane, Payal Khode, Arya Kapse, Deepak Sharma, Pankajkumar Anawade
Abstract - In Mozambique, in recent years, the construction sector has seen a lot of loss of life caused by accidents at work, mainly due to the lack of control of international standards for OHS, the production process and employee orientation. Risk analysis and management advocates that risks can be characterized by being partially known, changing over time and being managed in the sense that human action can be applied to change their form and/or the magnitude of their effect. The field of artificial intelligence (AI) is experiencing rapid growth and is increasingly integrating into various sectors, including healthcare, industry, education, and the workplace. Its overall objective is to develop an environmental, health, and safety management system integrating artificial intelligence (AI) and blockchain to prevent accidents, facilitate decision-making, and comply with international construction regulations at sites in Maputo, Mozambique. To achieve this goal, the system will focus on administration and legal compliance, education and training, safety and emergency
Paper Presenter
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room B Pune, India

9:30am IST

Integrating Deep Learning with Dynamic Sharding to Enhance Blockchain Performance: Case Study for Educational Record Management
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - Swapnil M Maladkar, Praveen M Dhulavvagol, S G Totad
Abstract - Blockchain technology has emerged as a powerful tool for secure, decentralized data management across various industries, but it faces significant scalability challenges due to the limitations of existing sharding methods. Traditional static sharding approaches often result in inefficient resource allocation, while adaptive sharding techniques can lead to increased complexity and delayed adjustments, hampering overall system performance. This paper proposes an innovative blockchain network management approach by integrating Long Short-Term Memory (LSTM) models with dynamic sharding. This system leverages predictive analytics to optimize real-time sharding adjustments, significantly enhancing blockchain performance. By addressing the shortcomings of both static and adaptive sharding methods, the proposed approach avoids the extra infrastructure and delays associated with Layer 2 solutions. Future research will focus on advancing LSTM techniques, integrating them with other optimization strategies, and testing in real-world scenarios to further enhance scalability and efficiency. This LSTM-integrated dynamic sharding method represents a significant step forward in blockchain network optimization, offering a more efficient and adaptable solution for contemporary blockchain applications. Experimental results reveal a 22% increase in transaction throughput and a 25% reduction in latency compared to conventional static sharding.
Paper Presenter
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room B Pune, India

9:30am IST

Medicard: Advanced Healthcare Application for Digital Transformation
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - Prema Sahane, Anand Dhadiwal, Devvrath Datkhile, Harshal Deore, Atharva Shinde, Amruta Hingmire
Abstract - The paper provides information about different healthcare applications that are built to develop the healthcare sector digitally with the help of modern technologies. It describes the need for making the particular application with its advantages and disadvantages. Though there are many health record management systems existing for electronic health record management, the accuracy and efficiency are not up to the level that society need. People find a lot of time wastage in maintaining the records manually. Also, Patients find it difficult to track their previous records. So, our system “Medicard” is an application for interaction between doctors, patients, and pharmacists. It is a multi-tasking application for all healthcare tasks like Centralized Storage of patient health records, Drug Analysis, Allergy Analysis, Online receipt generation, Community creation, Booking Doctor’s Appointments and Online Payment. It has three different interfaces for doctors, patients, and pharmacists.
Paper Presenter
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room B Pune, India

9:30am IST

Revolutionizing Academic Management: A Digital Solution for School or University Lecture and Laboratory Oversight - Smart QR Attendance Application with Location-Based Features for Schools and Universities
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - Anant Chovatiya, Priyanka Patel
Abstract - Attendance management holds significant importance for all organizations, serving as a determining factor in their success, whether they operate in educational institutions or the public and private sectors. Efficiently tracking individuals within the organization, including employees and students, is crucial for optimizing their performance. Managing employee attendance during lecture periods has become a challenging endeavor. The task of computing attendance percentages poses a significant challenge as manual calculations often result in errors and consume excessive time, leading to inefficiencies and time wastage. In response to the challenges posed by traditional paper-based practices in educational institutions, this paper introduces a digital solution for managing university lecture slots and attendance. The proposed system, named the "Speed Check system," aims to streamline faculty and student attendance processes through a mobile application, eliminating the need for manual recording and reducing paper consumption. Leveraging a cloud-based NoSQL database, real-time data synchronization ensures seam-less communication across users. The system offers distinct functionalities for Time Table Coordinators and Attendance Coordinators, facilitating efficient slot scheduling, modification, and attendance marking. Utilizing Flutter SDK and Firebase technology, the application provides a user-friendly inter-face and robust data protection. Future enhancements include role-based access control and advanced analytics for informed decision-making. Overall, this digital solution presents a significant stride towards optimizing academic administration and enhancing the effectiveness of attendance management in educational institutions.
Paper Presenter
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room B Pune, India

9:30am IST

Towards Contextual Search Optimization: A Unified Ranking Approach for Relevance Prioritization
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - Nirali Arora, Harsh Mathur, Vishal Ratansing patil
Abstract - Achieving relevance in search results is difficult in today's complex information environment, particularly when single-algorithm ranking models find it difficult to account for a variety of user circumstances. In order to improve search relevancy in a variety of circumstances, this study presents a unified ranking strategy that integrates many algorithms. Hybrid system adapts dynamically to user intent and situational details by combining conventional models like BM25 and PageRank with cutting-edge neural techniques like BERT-based transformers and learning-to-rank algorithms. A key component of this strategy is a context recognition mechanism that continuously evaluates user history, query type, and behavioural patterns to fine-tune relevance score according to the particular requirements of every search context. This method, called Contextual Rank, combines algorithmic scores to prioritize relevance, enabling more flexibility and response to user demands. Here presented about the theoretical ramifications, covering problems like scalability and processing needs as well as gains in relevance. The benefits of unified ranking models are highlighted in this paper, opening up new avenues for contextual optimization in recommendation systems and search engines and paving the way for improved user experiences across a range of search settings.
Paper Presenter
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room B Pune, India

9:30am IST

An Intelligent System-Powered Navigation: An IoT-Based Solution for Visually Impaired Individuals
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - Keerthi AJ, Kalyanasundaram V, Srinivasa Perumal R
Abstract - Individuals who are visually impaired or have dual sensory impairments, such as both hearing and vision loss, face significant challenges in navigating public spaces. These challenges often limit their independence and pose risks of unintentional harm to themselves and others. While traditional mobility aids like canes or guide dogs provide some assistance, they lack the ability to deliver real-time, comprehensive awareness of the user's surroundings. To address these limitations, The Intelligent system-powered Smart Device designed to enhance mobility and safety for visually impaired individuals. This device leverages advanced object detection technology to enable users to navigate public spaces more effectively and confidently. The solution employs a SSD MobileNetV3 Convolutional Neural Network (CNN) model for real-time, efficient, and accurate object detection. Integrated with the Ov7670 for computer vision tasks and an Arduino microcontroller for hardware coordination, the system captures live video through a mounted camera to detect and classify obstacles. Users receive instant alerts via auditory or haptic feedback, promoting safer navigation. To ensure robust performance, Azure Custom Vision is used to evaluate and visualize the precision, recall, and average precision (AP) using the COCO dataset. By offering enhanced mobility and reducing risks, this innovative device fosters independence and inclusivity for visually impaired individuals in public environments.
Paper Presenter
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room C Pune, India

9:30am IST

Detecting and Predicting Hotspots in Urban heat Island with Temperature, Humidity and Soil moisture
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - K.L.Sailaja, Gollapudi Vanditha, Goriparthi Krishna Swapnika, Mohammad Sania Sultana, Madala Pavani
Abstract - The aim of this project is to create an effective machine learning model for the detection and forecast of Urban Heat Island (urban heat islands) phenomenon in the mid region of Andhra Pradesh state specifically Vijayawada. High temperatures in urban areas relative to their rural areas are called Urban Heat Islands. The negative impacts include increasing energy use, health risks, and environmental destruction. The satellite imagery and Random Forest models, in particular, have a long-standing reputation of being inaccurate when it comes to geolocalization and even when time-based forecasts are provided, they are mostly misleading. Thus, this gives rise to inaccuracies and inconsistencies in hotspot identification and forecasting’s metrics. This Project suggests an improved Recurrent Neural Network (RNN) model that incorporates Long Short-Term Memory (LSTM) algorithms, driven by the need for more precise and reliable predictions. The proposed LSTM-based model targets the traditional approaches shortcomings of being spatially and temporarily inaccurate in the detection of hotspots. The patterns of temperature, humidity, and soil moisture in city regions can be explained better by this model. It increases the model's predictive capability and explains urban island’s patterns. The project uses data obtained through NASA/POWER CERES/MERRA2 Native Resolution Daily Data, which provides an extensive collection of temperature, humidity, and soil moisture records. These factors will be used to develop forecasting and predictive models of the Urban Heat Islands hotspots. Normalization is one of the methods employed even during advanced data preprocessing.
Paper Presenter
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room C Pune, India

9:30am IST

Enhancing Cryptocurrency Trading through Artificial Intelligence for Optimal Investment Strategies
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - Payal Khode, Shailesh Gahane, Arya Kapse, Pankajkumar Anawade, Deepak Sharma
Abstract - The technology behind cryptocurrency is secure and transparent. Currently, numerous investors are attracted to cryptocurrencies because of their transparent and safe technology. Additionally, investors find cryptocurrencies fascinating due to their high return potential and innovative possibilities. To optimizes trading and predict prices for investment strategies, some artificial intelligences are required. As of 2024, the global cryptocurrency market capitalization has exceeded 2.5 trillion dollars. Since then, cryptocurrency has established itself in the financial arena, with daily transaction volume reaching enormous heights. Navigating investment strategies are one of the main challenges for investors. There for, leveraging artificial intelligence for optimal investment decisions stands as effective solutions. The study examines recent developments in the field of artificial intelligence methods for cryptocurrencies investment, focusing on trading digital currencies such Bitcoin, Altcoin, Meme coin, and others. Even though price prediction for investing strategies has been the subject of extensive research, notable gaps remain in enhancing cryptocurrency trading through AI for successful investment outcomes. This paper reviews these gaps by examining the role of AI in accurately predicting cryptocurrency prices to enhance optimal investment. The study's findings demonstrate the critical role that precise price forecasting plays in developing adaptable and cautious trading and investing methods, which are essential in the erratic cryptocurrency market. Additionally, the study highlights current issues and suggests future research possibilities, highlighting the importance of ethical issues and multidisciplinary methods in the investment. By filling the knowledge vacuum and providing direction for future study, this synthesis hopes to promote more advanced and successful investment techniques in the crypto space.
Paper Presenter
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room C Pune, India

9:30am IST

Exploratory Analysis for Depression Detection using Deep Learning Models: Internet of Behavior Approach
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - Grishma Bobhate, Pawan Bhaladhare
Abstract - Internet of behavior is a primitive approach to study behavior analysis and predictive learning system to understand user experience and interpret their psychological patterns for betterment of the society. Basically, Internet of Things and Internet of Behavior are closely related to each other and can offer different techniques in various areas for developing technology and significant applications. Major psychological disorder, or depression, is a common but fatal neurological condition that has a disruptive impact on feelings, actions, and ways of seeing actuality. Various Machine learning algorithm have been implemented to detect depression through fusion modalities applied on different parameters such as visual, textual and gaze movement. To ensure preventive measure and provide ethical frameworks, this study aims to identify Internet of behavior technology that can have crucial importance in the comprehensive study of depression detection in healthcare sectors. With the assessment in health monitoring systems, the main objective is to explore and analyze the strategies in the Internet of behavior technology for understanding patient behavior and mental health to detect depression and mood. Various challenges towards Internet of behaviors has discussed. In order to ensure the reliability of the system, it also explores the different machine learning and deep learning approaches to determine depression with the performance validation. This will help to assist medical personnel in acquiring details and evaluating the actions of patients for an effective regimen of treatments. This study outlines the strategies to adopt the behavioral analysis for effective learning of depression detection model.
Paper Presenter
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room C Pune, India

9:30am IST

Improvement of Power Reliability in Rural India Using Isolated Energy Storage System
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - Balasubbareddy Mallala, Azka Ihtesham Uddin Ahmed, P. Kowstubha, T. Murali Krishna
Abstract - The world is now transiting towards Renewable Energy sources (RES) at a rapid pace to overcome the limitation of fossil fuel and generate Green Energy. But due the irregular generation of power in RES (like Solar PV Plant) throughout the day is making it less reliable. This paper integrates RES with an Energy Storage System (ESS) and Fuel Cell to overcome this disadvantage. With the help of this system the dependence on conventional energy sources can be reduced, the cost of generation of power can be brought down to 1/4th compared to an existing traditional system and also increases energy independence. During the morning hours, Combined with the fuel cell, the solar photovoltaic plant will supply power. Any excess power generated will be stored in the energy storage system (ESS). This way, when sunlight is unavailable, the ESS can meet the load demand, ensuring continuity and making the system more efficient and reliable.
Paper Presenter
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room C Pune, India

9:30am IST

Integrated Real-Time Object Detection and Navigation Framework for Autonomous Vehicles on Raspberry Pi
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - Pradeepkumar G, Priya Devi T, S A Suje, Gobinath S, M Dhanapal
Abstract - Nowadays self-driving cars are gaining attraction globally but their implementation in India faces significant hurdles due to the inadequacies of existing approaches reliant on GPS and sensor technologies. The erratic nature of Indian roads, characterized by variable road conditions and inaccuracies in mapping, renders conventional methods unreliable. To address these challenges, propose a novel approach utilizing pattern matching techniques for autonomous navigation. The solution involves deploying specialized patterns on the road surface, facilitating accurate detection and identification of pathways suitable for autonomous driving. By utilizing a modelled car equipped with a Raspberry Pi for image processing, the system captures road imagery via onboard cameras. These images are then transmitted to a remote computer for analysis and subsequent navigation instructions. Additionally, an array of sensors is deployed to detect and avoid obstacles in the vehicle's vicinity. The key innovation lies in the hybrid approach, which combines traditional sensor-based navigation with the novel pattern matching methodology. By leveraging these complementary technologies, the prototype aims to provide robust autonomous navigation tailored to the unique challenges of Indian roads.
Paper Presenter
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room C Pune, India

9:30am IST

IOT-ENHANCED AGRIBOT USING IMAGE PROCESSING AND MACHINE LEARNING FOR EFFECTIVE PEST MANAGEMENT
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - Dhanalakshmi R, Prashaanth S, Hari Prasath S, Dhanaselvam J, Harish R
Abstract - India is mainly an agricultural country, where almost three-fourths of the country's population works on farms. Several crops are grown according to regional situations. High-quality production of these crops can be achieved only with new techniques. The appropriate management of crops and identification of diseases and their respective treatments are very significant to prevent losses after harvesting as it usually happens. Diseases in crops deviate from their normal functions and show symptoms that hinder growth. Pests and insects always devastate major crops like rice, wheat, maize, and soyabeans. Consequently, productivity becomes low. With the adoption of deep learning technologies, pest infestation detection and management in agriculture have accuracy and efficiency. A solution is proposed in this paper that integrates image processing techniques with the MATLAB platform for the classification of pests and the proper fertilizers and pesticides to be applied. An autonomous robotic sprayer is used by this system to remotely traverse crop fields, ensuring pinpoint treatment applications. On the other hand, the infrastructure cost is reduced by the proposed solution. The camera setup density in an agricultural IoT monitoring system is minimized by it. Thus, the advanced technology is integrated with agricultural practice by this approach to promote sustainable farming. A validation accuracy of 99.80% is achieved by it to maximize crop production while minimizing losses due to pests and diseases.
Paper Presenter
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room C Pune, India

9:30am IST

PREDICTIVE MODELING OF FOREST COVER TYPES USING XGBOOST AND HYPERPARAMETER TUNING
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - M.Kavitha, N.Revathy
Abstract - Forest cover prediction has applications in environmental monitoring, forest management, and land-use planning. Governments and conservation organizations can use it to assess forest cover types and predict land changes. The research article depicts the use of the XGBoost algorithm applicable in forest cover prediction, focusing on evaluating model performance through key metrics like Mean Squared Error (MSE), Logarithmic Loss (Log Loss), and confusion matrices. The XGBoost model, optimized through hyperparameter tuning, demonstrates robust performance with a relatively low MSE, indicating accurate predictions. The Log Loss value of 0.5786 suggests that while the model's classifications are reasonably confident, there is room for refinement. The confusion matrix reveals strong performance for certain classes, such as class 1, but highlights significant errors in others, particularly class 5, which shows a high error rate of 60.93%. The proposed model effectively captures underlying data patterns and performs well across most classes. However, further enhancements, such as addressing class imbalances and refining hyperparameters, are needed to improve accuracy in challenging cases. The model's high hit ratios, where the correct class is often among the top predictions, indicate its reliability in multi-class classification tasks, making it a valuable tool for forest management and environmental monitoring.
Paper Presenter
avatar for M.Kavitha
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room C Pune, India

9:30am IST

The study of Security of online examination mode of assessment: A survey of two universities in Africa and Asia
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - Shailesh Gahane, Payal Khode, Arya Kapse, Deepak Sharma, Pankajkumar Anawade
Abstract - Nowadays, many universities and information technology (IT) institutes throughout the world provide online courses, tests, and certificates. In order to administer the tests from any location, delivery technologies have been developed. Putting this into practice will result in time and travel cost savings. Due to the COVID-19 epidemic, there is currently a significant demand for online courses and exams. The majority of universities presently use a variety of assessment methods to evaluate their pupils. These include of oral, paper-based, electronic, and electronic-paper. To help identify the most secure and acceptable assessment method, a survey was carried out. Participants were selected from One Universities in India and Uganda. Population Sample Participants were selected from One Universities in India and Uganda. Using the Krejcie and Morgan formula, a sample of 98 participants was drawn from the 110 research participants. Data was gathered using a questionnaire instrument, and descriptive statistics were generated through data analysis using SPSS software.
Paper Presenter
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room C Pune, India

9:30am IST

Wireless Sensor Network Optimization in IoT Land-slide Detection Systems Using Zigbee Protocol
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - Pradeepkumar G, Pavithramathi R, Jahina J, Tamilselvan K, Arulanantham D
Abstract - This work presents an innovative system designed for remote monitoring of landslides using IoT technology. The solution uses a wireless underground sensor network (WUSN), a cloud computing platform and a dedicated mobile application to provide real-time monitoring capabilities. In this system, a sensor network uses Arduino components connected via Wi-Fi modules to collect data on soil moisture levels. This collected data is then transferred to a cloud computing environment for secure and permanent storage. In addition, the cloud platform hosts the model that can trigger alarms when potential landslides are detected. In addition, the system includes a user-friendly mobile application that facilitates real-time data visualization and alerts on potential landslides. This end-to-end solution, from humidity sensor data collection to citizen-facing data visualization, is particularly suitable for smart cities and IoT environments. The effectiveness of the system was evaluated with both real-life tests and simulated scenarios. The results show that the network of sensors accurately measures soil moisture, while the landslide monitoring model continuously sends alerts when necessary.
Paper Presenter
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room C Pune, India

9:30am IST

AI Based Smart Traffic Law Enforcement System
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - Sahil Shelote, Ritesh Chaudhari, Payal Sirmokadam, Rupali Kamathe, Meghana Deshpande, VandanaHanchate, Sheetal Borde
Abstract - Traditional traffic enforcement methods pose significant challenges to public safety in order to effectively detect and resolve violations. Using the ESP32-Cam module for video capturing, YOLOv3 for object detection, and OCR for license plate recognition, it offers an innovative approach to improving road safety and traffic management. ESP32-CAM module captures realtime videos of intersections. What sets this research work apart is the integration of YOLOv3, an advanced object detection model, to detect possible traffic violations such as helmet detection, rider detection. OCR technology allows extraction of license plate information, ensuring accurate identification of the vehicle involved in violation. Enabling the creation of Echallans and sending the registered vehicle owner an SMS with the payment gateway link when an Echallan is generated. This represents an important development in traffic management and safety, with promising results in terms of increased compliance, reduced accidents and general improvements in road safety. ESP32-CAM integrates YOLOV3 and OCR technologies to provide an efficient and technologybased solution to improve public safety on the road.
Paper Presenter
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room D Pune, India

9:30am IST

Enhancing stereo matching in visual perception with temporal and spatial data
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - Rohini Hongal, Supriya K, Rajeshwari .M, Rahil Sanadi
Abstract - Computer vision applications like object detection, picture matching, 3D reconstruction, and depth estimation in navigation rely on the synchronization of stereo frames. In stereo vision, two cameras separated by known distance are used to capture an image and analyze for differences in both images. To use stereo images in any application, synchronization between the corresponding frames must be ensured. This paper presents an approach to detect the synchronization between the stereo pair images. The synchronization information between the stereo frames can be achieved in two ways: one is by using the temporal data of the image pair and the other is by analyzing the spatial data in the images. This study uses the temporal data i.e. timestamps of the stereo images and validates results with the spatial data, to identify the stereo image pair as synchronous or asynchronous. The spatial algorithm is executed once the timestamp algorithm identifies a possible synchronization. In order to generate a template and extract spatial information from the left frame, this technique makes use of the Sobel filter. An appropriate correlation approach is then used to match the template to the right, right+1, and right-1 frames. If the chosen frame matches the correct frame, the frames are deemed to be synchronized. The frame with the highest correlation is chosen. On the other hand, the frames are considered asynchronous, if the frame with the highest correlation is either the right+1 or right- 1 frame. The suggested approach offers an accuracy of 90.33for static datasets and 96.67frame synchronization. The technique also provides information on the duration of asynchrony when frames are not synchronized. A variety of computer vision applications that depend on synchronized stereo frames might benefit greatly from the presented technique. It allows for more reliable object detection, picture matching, and 3D reconstruction by precisely detecting the synchronization state, which improves visual perception and comprehension in real-world circumstances.
Paper Presenter
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room D Pune, India

9:30am IST

Exploring Advancements in Diabetes Prediction with Machine Learning- An Approach towards Explainable AI (XAI)
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - Karuppasamy M, Jansi Rani M, Poorani K
Abstract - Diabetes is the leading cause of mortality since its prevalence is higher globally. Since it contributes to various kinds of complications it leads to a high mortality rate. Early diagnosis and prediction of contributing features are found with the assistance of machine learning models. These models are instrumental in assisting healthcare sectors in prediction, diagnosis, prognosis, and disease prevention. If diseases are found at earlier stages, it would save many people’s lives. In that aspect, machine learning models are developed to find diseases at earlier stages. However, accuracy of the predictions at not much satisfied. This proposed work explores the techniques to predict diabetes at earlier stages. Several data mining approaches to XAI are discussed. The major features contributing to diabetes are also identified with the feature importance technique. This results in a greater way of understanding which feature contributes more to diabetic progression. The proposed model resulted in 94% accuracy with random forest which is also elaborated with Explainable AI (XAI).
Paper Presenter
avatar for Poorani K
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room D Pune, India

9:30am IST

Exploring Cybersecurity Vulnerabilities and Innovative Defense Mechanisms in Modern Technological Devices
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - Payal Khode, Shailesh Gahane, Arya Kapse, Pankajkumar Anawade, Deepak Sharma
Abstract - An important subject that has always remained on top of the most important areas of concern universally is security as the world deals with dynamic change in technology. It is with this background that this paper explores the frailties that arise from the current technological gadgets such as mobile phones, Internet of Things (IoT) devices, and personal computers that are prone to a range of cyber threats. A comprehensive examination of the security threat is taken to show how application weaknesses and system susceptibilities and network-based threats allow the attacker to erode user confidentiality and data integrity. Moreover, this study compares traditional and modern assessment and protection mechanisms, including cryptography techniques, flow inspection tools, signals intelligence technologies, and hardware-based and artificial intelligence-based security measures with the intention of identifying the most effective paradigm for combatting these threats. That way, the present paper is relevant to the ongoing work in the field aiming at designing new countermeasures to improve the vulnerability of assorted present-day technologies to cyber threats.
Paper Presenter
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room D Pune, India

9:30am IST

Image Classification on CIFAR-10 Using Deep Convolutional Neural Networks
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - N V Bharani Subramanya Kumar, C V Mahesh Reddy, CH. Samyana Reddy, Krishn Chand Kewat, Laxmi Narsimha Talluri, Shaik Mohammed, Rahil Sarfaraz, Sushama Rani Dutta
Abstract - This work showcases an improvement over existing methods by developing a novel deep convolutional neural network (CNN) architecture for image classification specifically targeting the images in the CIFAR-10 dataset [4] which consists of 60,000 color images ( 32 x 32 pixels size) divided into 10 classes. So far, the model architecture incorporates a number of convolution and pooling layers which are then followed by the fully connected layers to better learn the complex structure existing within the input spatial configuration. The typical challenge of overfitting is addressed by employing various techniques such as data augmentation and dropout regularization strategy. Immediately from the experimental evidence, it is clear that the deep CNN performs superior to other traditional models in the case of image recognition classifying problems and therefore the model has proved to be robust in discerning the differences that exist in the categories in the images within the CIFAR-10 dataset.
Paper Presenter
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room D Pune, India

9:30am IST

IMPACT OF ICT ON THE INDIAN HEALTHCARE SECTOR-A REVIEW ARTICLE
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - Shubham Kadam, Chhitij Raj, Pankajkumar Anawade, Deepak Sharma, Utkarsha Wanjari, Vijendra Sahu, Anurag Luharia
Abstract - This paper examines the modern role of information and communication technology (ICT) in healthcare, which has revolutionised patient care, data management, and service delivery. While ICT was initially used solely for administrative purposes, it is now broadly defined to include a range of information and communication technologies such as electronic health records (EHR), telemedicine and analytics that improve operational Efficiency, patient access and quality of care. The ability to innovate, such as AI, cloud computing, etc., provides real-time data access that helps healthcare professionals make better decisions and also improves patient outcomes. In particular, the paper showcases the government's initiative to create an integrated digital health system. The study highlights the need for strategic implementation of ICT to optimize health outcomes and availability and access to services, particularly in resource-poor settings.
Paper Presenter
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room D Pune, India

9:30am IST

SmartMail Insights: Revolutionizing Email Management
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - Sachin Naik, Rajeshree Khande, Sheetal Rajapurkar, Kartik Dalvi, Shubham Rajpure, Vaibhav Kalhapure
Abstract - SmartMail Insights is an intelligent web-based toolkit that is created for email management and all goes above delivering the basic functions of most online mail applications. Through the automation priority ranking, auto-responses and emails summarization, it makes it easier for the users to deal with urgency and important mails to emails that may not be very tiresome. The ML algorithms that it uses help easily sort the emails by content, sender, and, there are separate filters to highlight important emails with variable options. Auto replies are supported by NLP and there is the summarization of text to make it easier to read. Despite this, there are ways that SmartMail Insights could advance its current model of categorization one way is to incorporate its model for identifying and sorting through spam emails and promotional ones at least, into more refined sort of emails such as personal, business and so on since doing so would prove helpful in improving categorization accuracy
Paper Presenter
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room D Pune, India

9:30am IST

Techniques and Best Practices for Creating Accessible Websites and Applications
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - Shailesh Gahane, Payal Khode, Arya Kapse, Deepak Sharma, Pankajkumar Anawade
Abstract - The accessibility for every type of user, including disability, ensures that the websites and applications are developed to allow the access of every user in this electronic world. For my research paper, I aimed to report important techniques and best practices for developing accessible websites and applications while researching the effectiveness of the established accessibility guidelines, the role of assistive technologies, and inclusive design strategies. The first objective of this research is concerned with the practical application and the effectiveness of the general core standards on accessibility overall, including the Web Content Accessibility Guidelines (WCAG) and the Americans with Disabilities Act (ADA), in terms of their positioning on promoting compliance and inclusion. Three are the targets of this paper. The first target concerns how assistive technologies, like screen readers and voice-control programs, interact with web applications along best practice recommendations for optimizing these tools to access better by following accessibility. Third is about inclusive strategies for design issues with color contrast, font selection, and responsiveness meant to improve accessibility for both visual, auditory, and cognitive impairment. This research gives a comprehensive and definitive understanding of the present techniques and best practices in accessible web and app development. Therefore, how the developers can possibly enhance usability and ensure digital inclusivity for all users is provided.
Paper Presenter
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room D Pune, India

9:30am IST

To Inspire Minds: Generating Multimedia Poetry Education Using Gen AI
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - Madhuri Thorat, Priyanshu Kapadnis, Neel Kothimbire, Rameshkumar Choudhary, Atharva Jadhav
Abstract - The emergency of Generative AI has led to the development of various tools that present new opportunities for businesses and professionals engaged in content creation. The education sector is undergoing a significant transformation in the methods of content development and delivery. AI models and tools facilitate the creation of customized learning materials and effective visuals that enhance and simplify the educational experience. The advent of Large Language Models (LLMs) such as GPT and Text-to-Image models like Stable Diffusion has fundamentally changed and expedited the content generation process. The capability to generate high-quality visuals from textual descriptions has exceeded expectations from just a few years ago. Nevertheless, current research predominantly concentrates on text generation from text, with a notable lack of studies exploring the use of multimodal generation capabilities to tackle critical challenges in instruction supported by multimodal data. In this paper, we propose a framework for generating situational video content based on English poetry, which is executed through several phases: context analysis, prompt generation, image generation, and video synthesis. This comprehensive process necessitates various types of AI models, including text-to-text, text-to-video, text-to-audio, and image-to-image. This project illustrates the potential of combining multiple generative AI models to produce rich multimedia experiences derived from textual content.
Paper Presenter
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room D Pune, India

9:30am IST

Usage Intention of Robo-Advisory Services Among Gen Z through DM ISS model Dimensions
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - Rashmy Moray, Sridevi Chenammasetti, Shikha Jain, Ankita, Shivani
Abstract - This study explores the determinants influencing the adoption of robo-advisory services among Generation Z and Millennials. Leveraging the DeLone and McLean Information Systems Success (DM ISS) model, the research examines four key dimensions—system quality, information quality, service quality, and user satisfaction—to evaluate their impact on users' intention to adopt these services. A structured questionnaire was utilized to collect primary data, which was analyzed using Structural Equation Modeling (SEM) via SmartPLS software. Findings highlight that service quality and user satisfaction significantly influence the adoption intent of robo-advisory services. This research expands the DM ISS model's application to robo-advisory services, providing valuable insights for stakeholders on how these dimensions contribute to user satisfaction and overall system performance.
Paper Presenter
avatar for Ankita

Ankita

India
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room D Pune, India

9:30am IST

Artificial Intelligence as a Game-Changer in Healthcare Delivery and Management
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - Shailesh Gahane, Payal Khode, Arya Kapse, Deepak Sharma, Pankajkumar Anawade
Abstract - The global health care is on the threshold of a revolutionary transformation, and the artificial intelligence technology stands at the forefront of this change. This research paper deals with the complex process involved in conceptualizing, designing, developing, and enforcing an AI-pushed health-care system. With the strength of machine learning and deep learning technologies, it can analyze vast ranges of healthcare data, which incorporates digital fitness records, clinical imaging, among others. It begins with reviewing multidimensional literature pertinent to the research study. Through this research, a critical part entails a pilot observe designed in a very deliberate manner in order to conservatively test the effectiveness and reliability of AI algorithms in various healthcare fields. Based on this research, it is predicted that several advantages are bound to be realized among them; more accurate diagnosis, individually tailored treatment plans, optimized effective care resources deployment, and lower healthcare expenses. By making known my findings and insights, I hope to provide helpful guidance and recommendations for health care professionals, policymakers, and developers of technology, which would eventually enrich or enhance the discourse regarding AI integration in health care.
Paper Presenter
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room E Pune, India

9:30am IST

Evaluating the Efficacy of Generative Adversarial Networks in Data Augmentation for Machine Learning Models
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - Ankit Shah, Hardik M. Patel
Abstract - Generative Adversarial Networks (GANs) have revolutionized data augmentation by generating realistic and diverse synthetic data, significantly enhancing the performance of machine learning models. This review evaluates the efficacy of GAN-based augmentation compared to traditional methods across various datasets, including MNIST, CIFAR-10, and diabetic retinopathy images. Using architectures such as DCGAN, WGAN-GP, and StyleGAN, our experiments showed substantial performance improvements: CNN accuracy on CIFAR-10 increased from 82.0% to 87.5%, and ResNet-50 accuracy on diabetic retinopathy images rose from 75.0% to 87.0%. Statistical analyses confirmed the significance of these gains. Despite challenges like computational costs and training instability, GAN-based augmentation proves superior in addressing data scarcity and enhancing model robustness. Future research should focus on optimizing GAN training, integrating hybrid models, and exploring ethical considerations. The results underscore GANs' potential in advancing machine learning applications, particularly in complex and data-scarce domains.
Paper Presenter
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room E Pune, India

9:30am IST

Improvising Healthcare Data Security Through Federated Learning and Blockchain Framework
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - Manoj N, M Thanmay Ram, Manikanta S, Tarun Pradeep, Ramandeep Kaur
Abstract - As IoT devices multiply in smart cities, safeguarding healthcare data's confidentiality, security, and integrity from various sources is getting harder. In order to protect healthcare data and facilitate effective machine learning, this article suggests a secure structure that combines Blockchain technology with Federated Learning (FL). With its immutable ledger, blockchain guarantees data confidentiality and openness throughout the network, whereas FL lets data stay on local devices, protecting privacy while training models. The suggested framework is ideal for smart city applications since it places a strong emphasis on safe data sharing, privacy protection, and dependable model management. The design tackles important problems like data breaches, illegal access, and confidence in model updates by utilizing FL's decentralized training and Blockchain's tamper-proof data management. This combination promotes openness and confidence among stakeholders while strengthening the security of healthcare data. The suggested method, which is intended for smart cities, opens the door for creative and privacy-compliant approaches to healthcare data administration and analysis by facilitating efficient collaboration across healthcare organizations without compromising patient privacy.
Paper Presenter
avatar for Manoj N

Manoj N

India
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room E Pune, India

9:30am IST

Leveraging Big Data and AI for Optimizing Health Insurance Claims and Risk Assessment in Healthcare Financing
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - Sabina Sehajpal, Ravneet Kaur, Ajay Singh, Mukul Bhatnagar
Abstract - This research elucidates the transformative potential of big data analytics and artificial intelligence in optimising health insurance claims and risk assessment by employing an empirically robust framework encompassing reliability and validity metrics, Heterotrait-Monotrait Ratio (HTMT) analysis, and bootstrapping to unravel the intricate interdependencies among constructs such as AI model accuracy, claims processing efficiency, cost efficiency, data quality, fraud detection accuracy, system usability, and user trust interface, thereby advancing a comprehensive understanding of the systemic synergies that enhance predictive precision, operational scalability, and equitable resource allocation within the healthcare financing paradigm.
Paper Presenter
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room E Pune, India

9:30am IST

MANTRALENS: A WINDOW INTO THE EMOTIONAL DEPTHS OF SANSKRIT
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - Prajakta Deshpande, Divya Kasat, Shrushti Mahadik, Rutika Ubalekar
Abstract - Mantras in the Sanskrit language are the soul of Indian culture, carrying deep spiritual, emotional, and cultural implications. These ancient chants, more than words, resonate profoundly and are used in meditation, healing, and divine invocation. Each Sanskrit mantra reveals emotional connotations through its meaning and sound. We have classified them into three groups: Vidur Niti, representing clarity and wisdom; Chanakya Niti, embodying planning and decisive action; and Sanskrit Shlokas, symbolizing harmony and unity. In pioneering work, cutting-edge transformer models, such as XLNet, and the Hugging Face framework are adapted to build an advanced text classification system that decodes the emotional essence of sacred mantras. A hand-curated dataset of annotated Sanskrit mantras has its performance evaluated in terms of accuracy and F1-score on emotional polarity. This kind of research bridges ancient wisdom with modern technology to uncover the revitalization of sacred traditions through computational linguistics for this very modern world.
Paper Presenter
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room E Pune, India

9:30am IST

Shadow Rendering Mechanisms for Dynamic Daylight Conditions in Augmented Reality Applications
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - Panchal Twinkle Shaileshbhai, Pushpal Desai
Abstract - Shadow rendering plays a crucial role in enhancing the realism and immersion of Augmented Reality (AR) applications by seamlessly integrating virtual objects into real-world environments. Dynamic daylight conditions, characterized by varying sunlight intensity, direction, and ambient light, present significant challenges to achieving visually coherent and computationally efficient shadow rendering. This study offers a comparative analysis of diverse shadow rendering mechanisms, evaluating their effectiveness, performance, and suitability for AR applications under fluctuating lighting conditions. Techniques such as Light Direction Approximation, Shadow Mapping, Projected Planar Shadows, and Real-Time Ray Tracing, Dynamic Shadow Blending, Real-Time Sun Position and Shadow Adjustment, Hybrid Shadow Techniques, Brightness Induction and Shadow Inducers and Shadow Perception in AR are examined, highlighting their strengths, limitations, and application scenarios. The research also addresses factors influencing shadow intensity and alignment, providing insights into optimizing realism and computational efficiency in outdoor AR environments. By exploring innovative solution and proposing guidelines for shadow rendering mechanism, this study contributes to advancing AR technology, ensuring enhanced visual fidelity and user experience across dynamic settings.
Paper Presenter
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room E Pune, India

9:30am IST

Smart Entrepreneurship: A Bibliometric analysis on the Research trends in Entrepreneurship in Computer Science
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - T. A. Alka, M. Suresh, Aswathy Sreenivasan
Abstract - This study aims to explore entrepreneurship trends in computer science through Bibliometric analysis. 5530 documents from the Scopus are selected based on inclusion and exclusion criteria in the initially selected documents. The Biblioshiny package under R programming is used for the analysis. The major findings are; entrepreneurship has wider applications in various domains. It is not a single-domain phenomenon. The trend topics and word cloud show the most trends in entrepreneurship in computer science including learning models, artificial intelligence, games, innovation, entrepreneurship education, digital transformation, computer simulation, etc. The limitations of the study are; papers from the Scopus database are only considered. Documents other than in English, and papers from other domains except computer science are ignored. This literature study lacks the benefits of primary data research. The inherent limitations of the bibliometric methodology will affect the results. The findings of this research provide knowledge on various aspects to the policymakers, practitioners, researchers, and academicians to foster an entrepreneurship ecosystem and understand the trends of entrepreneurship in the computer science domain. The novelty of the study is underlying the comprehensive review of the existing body of knowledge to draw future research directions. The main highlight of this literature review paper is that complete in-depth knowledge of the data is possible through bibliometric analysis.
Paper Presenter
avatar for M. Suresh
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room E Pune, India

9:30am IST

Synthetic Speech Detection using MFCC and CQT with Res2Net Architecture
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - Sathiyapriya K, S Bharath, Rohith Sundharamurthy, Prithivi Raaj K, Rakesh Kumar S, Rakkul Pravesh M, N Arun Eshwer
Abstract - The convenience and security offered by voice-based authentication systems results in its increasing use in various sectors such as banking, e-commerce, telecommunications, etc. But these systems are open to vulnerabilities from voice spoofing attacks, including replay synthesis and voice conversion. The following work makes use of Mel-Frequency Cepstral Coefficients (MFCC), Constant-Q Transform (CQT), and a deep learning model Res2Net and creates a framework that can classify genuine and spoofed voices. MFCC and CQT are commonly used for feature extraction, and the Res2Net model classifies the audio. The system was evaluated against the ASVspoof 2021 dataset, the reason being that it has a diverse collection of audio samples (almost 180,000) samples, and also it is recognized by the research community. Our system recorded a low Equal Error Rate (EER) of 0.0332 and a Tandem Detection Cost Function (t-DCF) of 0.2246. This framework contributes to the advancement of secure voice authentication systems, addressing critical challenges in modern cybersecurity.
Paper Presenter
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room E Pune, India

9:30am IST

The Role of Artificial Intelligence in Shaping Consumer Preferences and Behavior in Smart Home Environments
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - Martin Mollay, Deepak Sharma, Pankajkumar Anawade, Chetan Parlikar
Abstract - This study examines how AI affects consumer choices in smart homes. This research determines how AI-supported technologies such as voice-controlled digital assistants, dynamic pricing models, and personalized recommendations significantly affect consumer tastes, behaviors, and purchasing decisions through the use of secondary data sources. Customers’ interactions with goods and services are personal and pragmatic as artificial intelligence is progressively included in smart homes. The study claims, however, that artificial intelligence has a two-edged effect on consumer decision-making. Two such areas where AI can enhance customer experience by improving interactions and decision-making processes are through personalization and optimization. This, however, gives rise to some critical ethical issues concerning algorithmic bias privacy and data security. As technology matures, it is essential to promote responsible AI practices, given its increasing ubiquity in daily life. According to the study findings, for instance, organizations must overcome these challenges if they are to preserve customer trust and ensure that artificial intelligence (AI) will ultimately enhance customer relationships. Reading through many kinds of research, company reports, and scholarly works on AI applications in consumer decision-making gives one a view of its current and potential future applications. Results underline that ethics matter when designing transparent AI systems in order to enhance customer loyalty and trust.
Paper Presenter
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room E Pune, India

9:30am IST

The Use of AI Technology for Optimization of Online ID Card Generating System for Schools
Thursday January 30, 2025 9:30am - 11:30am IST
Authors - Payal Khode, Shailesh Gahane, Arya Kapse, Pankajkumar Anawade, Deepak Sharma
Abstract - The proposed identity card processing system revolutionizes the traditional, manual, and semi-automated ID card creation processes by integrating advanced web technologies and artificial intelligence (AI). Designed for efficiency and user-friendliness, this system employs React JS and JavaScript for seamless operation, enabling students to input required details and generate a printable ID card within 15 minutes. This contrasts significantly with the time-consuming manual design methods using applications like CorelDRAW or Photoshop. Incorporating AI-driven features such as customizable designs and face detection technology ensures quick and accurate retrieval of student data from the school database. The system emphasizes real-time data processing, cross-platform accessibility, and a secure, intuitive interface, allowing users and administrators to handle ID card requests efficiently from any internet-enabled device. By addressing the limitations of existing methods, this automated solution ensures flexibility, reliability, and enhanced usability, making ID card issuance streamlined and error-free. The final system aligns with modern technical and operational requirements, delivering robust functionality and improved organizational efficiency.
Paper Presenter
Thursday January 30, 2025 9:30am - 11:30am IST
Virtual Room E Pune, India

11:15am IST

Session Chair Remarks
Thursday January 30, 2025 11:15am - 11:20am IST
Invited Guest/Session Chair
avatar for Dr. Vijeta Kumawat

Dr. Vijeta Kumawat

Professor & Head, Department of CSE, JECRC, Jaipur, India.
Thursday January 30, 2025 11:15am - 11:20am IST
Virtual Room A Pune, India

11:15am IST

Session Chair Remarks
Thursday January 30, 2025 11:15am - 11:20am IST
Invited Guest/Session Chair
avatar for Dr. Vishal R. Patil

Dr. Vishal R. Patil

Associate Professor Department of Computer Science & Engineering Vishwakarma Institute of Technology, Pune, India
Thursday January 30, 2025 11:15am - 11:20am IST
Virtual Room B Pune, India

11:15am IST

Session Chair Remarks
Thursday January 30, 2025 11:15am - 11:20am IST
Invited Guest/Session Chair
avatar for Dr. Deepika saxena

Dr. Deepika saxena

Associate Professor, Poornima University, Jaipur, India.
Thursday January 30, 2025 11:15am - 11:20am IST
Virtual Room C Pune, India

11:15am IST

Session Chair Remarks
Thursday January 30, 2025 11:15am - 11:20am IST
Invited Guest/Session Chair
avatar for Dr. Kalpesh Popat

Dr. Kalpesh Popat

Associate Professor, Marwadi University, India
Thursday January 30, 2025 11:15am - 11:20am IST
Virtual Room D Pune, India

11:15am IST

Session Chair Remarks
Thursday January 30, 2025 11:15am - 11:20am IST
Invited Guest/Session Chair
avatar for Dr. Kirit J. Modi

Dr. Kirit J. Modi

Professor & Head, Sankalchand Patel College of Engineering, Gandhinagar, India.
Thursday January 30, 2025 11:15am - 11:20am IST
Virtual Room E Pune, India

11:20am IST

Closing Remarks
Thursday January 30, 2025 11:20am - 11:30am IST
Moderator
Thursday January 30, 2025 11:20am - 11:30am IST
Virtual Room A Pune, India

11:20am IST

Closing Remarks
Thursday January 30, 2025 11:20am - 11:30am IST
Moderator
Thursday January 30, 2025 11:20am - 11:30am IST
Virtual Room B Pune, India

11:20am IST

Closing Remarks
Thursday January 30, 2025 11:20am - 11:30am IST
Moderator
Thursday January 30, 2025 11:20am - 11:30am IST
Virtual Room C Pune, India

11:20am IST

Closing Remarks
Thursday January 30, 2025 11:20am - 11:30am IST
Moderator
Thursday January 30, 2025 11:20am - 11:30am IST
Virtual Room D Pune, India

11:20am IST

Closing Remarks
Thursday January 30, 2025 11:20am - 11:30am IST
Moderator
Thursday January 30, 2025 11:20am - 11:30am IST
Virtual Room E Pune, India

12:15pm IST

Opening Remarks
Thursday January 30, 2025 12:15pm - 12:20pm IST
Moderator
Thursday January 30, 2025 12:15pm - 12:20pm IST
Virtual Room A Pune, India

12:15pm IST

Opening Remarks
Thursday January 30, 2025 12:15pm - 12:20pm IST
Moderator
Thursday January 30, 2025 12:15pm - 12:20pm IST
Virtual Room B Pune, India

12:15pm IST

Opening Remarks
Thursday January 30, 2025 12:15pm - 12:20pm IST
Moderator
Thursday January 30, 2025 12:15pm - 12:20pm IST
Virtual Room C Pune, India

12:15pm IST

Opening Remarks
Thursday January 30, 2025 12:15pm - 12:20pm IST
Moderator
Thursday January 30, 2025 12:15pm - 12:20pm IST
Virtual Room D Pune, India

12:15pm IST

Opening Remarks
Thursday January 30, 2025 12:15pm - 12:20pm IST
Moderator
Thursday January 30, 2025 12:15pm - 12:20pm IST
Virtual Room E Pune, India

12:15pm IST

Opening Remarks
Thursday January 30, 2025 12:15pm - 12:20pm IST
Moderator
Thursday January 30, 2025 12:15pm - 12:20pm IST
Virtual Room F Pune, India

12:15pm IST

A system for personalized voice-assisted comprehensive treatment of visually impaired patients using generative AI
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Smita Mehendale, Reena (Mahapatra) Lenka
Abstract - This system is provided to make healthcare services responsive to visually impaired patients’ needs in various circumstances, predominantly during a medical emergency. The system includes multiple stakeholders, including visually impaired patients, patient family members, friends, neighbors, hospitals, healthcare providers, insurance companies & agents, private medical attendants & agencies, pharmacies, blood banks, and medical equipment providers using voice-assisted AI and with the help of various proposed systems. The design and implementation of voice-assisted personalized, comprehensive medical service, both emergency and non-emergency, will use data from shared information by patients and healthcare service providers like hospitals, pharmacies, and pathology and allied services like insurance and medical attendant services. The system uses a voice assistant chatbot to communicate with patients and a user interface with medical and allied service providers. It communicates between multiple service providers, clearly showing the entire patient care cycle for the visually impaired, a special group of people.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room A Pune, India

12:15pm IST

Adopting Agile Methodologies for Digital Newsrooms: A Case Study of BBC News
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Jay Bhatt, Bimal Patel, Anshuman Prajapati, Jalpesh Vasa
Abstract - Software engineering has progressed extensively, adopting structured methodologies and systematic frameworks for developing reliable, scalable, and efficient systems. A key advancement has been the introduction of software process models, which guide development activities. Traditional models use linear, sequential phases, but are rigid and less suited for projects with changing requirements. To address dynamic market demands and evolving business needs, the Agile methodology emerged, providing an iterative, flexible approach to software development. Agile promotes incremental delivery, collaborative team dynamics, and continuous customer feedback, making it highly effective in rapidly changing environments. Agile methodologies have expanded beyond software development into industries like media. With fast-evolving technology and shifting audience behaviors, media companies are under pressure to innovate. BBC News adopted Agile to overhaul its content production and delivery processes. This transition has improved newsroom agility, enabling faster response times, fostering cross-functional collaboration, and enhancing iteration capabilities in digital media workflows. The shift to Agile represents a strategic transformation, positioning BBC News to adapt to audience demands and technological advancements. This paper investigates the integration of Agile at BBC News, detailing the operational benefits, challenges, and the methodology’s influence on sustaining their leadership in the competitive news industry.
Paper Presenter
avatar for Jay Bhatt
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room A Pune, India

12:15pm IST

Business Ethics: Impact of Globalization
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Ankit Aal, Priyanka Patel
Abstract - In today’s interconnected world, ignoring ethical decision-making can have dire consequences. As businesses expand and globalize, the pressure to cut corners and maximize profits can lead to severe ethical breaches. William C. Butcher, retired chairman of the Chase Manhattan Corporation, highlighted the growing recognition that ethics in business is not a luxury but a necessity. Rooted in the concept of “ethos,” the importance of ethics has evolved, especially as business practices have become more complex. Over the decades, unethical business behavior has left a significant mark: the 1960s were defined by social upheaval, the 1980s by rampant financial scandals, and the 1990s by the challenges of a newly globalized economy. However, the rapid growth of markets was paralleled by troubling issues such as the exploitation of child labor, environmental degradation, and product counterfeiting. The 21st century introduced even more sophisticated threats—cybercrimes, intellectual property theft, and workplace discrimination—placing companies at greater risk if they neglected ethical practices. Despite the increasing awareness of these challenges, many businesses still struggle to balance profit with principle. Those that fail to integrate ethics into their strategies risk damaging their reputation, alienating customers, and facing legal repercussions. On the other hand, companies that proactively embrace ethical standards benefit from increased trust, a loyal workforce, and sustainable profitability. As ethics become an integral part of strategic business planning, they act not only as a safeguard against malpractice but also as a catalyst for long-term success in the global marketplace.
Paper Presenter
avatar for Ankit Aal
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room A Pune, India

12:15pm IST

Comparative analysis of different VANET Protocols and the need for a Hybrid Protocol using Satellite Communication and GPS Network
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Samrat Subodh Thorat, Dinesh Vitthalrao Rojatkar, Prashant R Deshmukh
Abstract - Vehicular Adhoc Networks (VANETs) play a vital role in enhancing road safety, and traffic management, and providing infotainment services. Various protocols have been developed to facilitate communication in VANETs, each with its advantages and limitations. This paper shows a comparative analysis of different VANET protocols, mentioning their performance, scalability, and reliability. It also illustrates the need for a hybrid protocol using satellite communication and GPS networks to overcome existing issues and challenges thus improving overall system efficiency.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room A Pune, India

12:15pm IST

Environmental sustainability life cycle mapping software & services leveraging value stream mapping technology
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Ruby Chanda, Rahul Dhaigude
Abstract - There is a need to map, analyse, and visually convey a product's environmental impact over its complete life cycle or a specific aspect of it, given the urgency with which climate change must be addressed. If "what-if" scenarios can be additionally supported this can accelerate decision making towards improved environmental outcomes. Practically such outcomes need to also understand the economic ramifications so the map needs to support a mix of environmental and operational efficiency metrics. This paper explores the adaptation of a leading commercial value stream mapping software (eVSM Mix) for this purpose. Value stream maps come from the Lean domain and provide a high level view of the activities required to provide customer value. The work involved has technical and marketing aspects tied to a new product introduction and to a new customer segment.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room A Pune, India

12:15pm IST

Exploring the Location-Based Privacy Vulnerabilities of Using Bluetooth Low Energy Beacons
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Masakona Wavhothe, Khutso Lebea
Abstract - This paper focuses on the vulnerabilities present in Bluetooth Low Energy (BLE) Beacons by exploring the background of BLE technology and the need to explore the chosen topic. The problem statement and structure of the paper are also explored in the introductory section. The subsequent section covers the case study that will be used to explore the chosen topic in deeper detail. Then, the background explores BLE beacons in detail, explaining their applications and vulnerabilities. The paper then concludes by highlighting all the important facts established in the research and suggesting how the study can be improved.
Paper Presenter
avatar for Khutso Lebea

Khutso Lebea

South Africa
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room A Pune, India

12:15pm IST

Machine Learning Models for Soil Moisture Estimation Using Spectrometry
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Mahek Viradiya, Shivam Patel, Sansriti Ishwar, Veer Parmar, Simran Kachchhi, Utsavi Patel, Hardikkumar Jayswal, Axat Patel
Abstract - Moisture content identification in soil is crucial for various applications in agriculture, construction, and environmental monitoring. Traditional methods for moisture detection often involve labor-intensive processes and specialized equipment which can be invasive, time-consuming, and expensive. This study explores use of spectrometry data, acquired through multispectral sensors using visible light and near-infrared (NIR) spectrum ranging from 400-1000nm, for rapid and accurate moisture identification in soil and sand samples. The sensors leverage on-chip filtering to integrate up to eight wavelength selective photodiodes into a compact 9x9mm array, facilitating the development of simpler and smaller optical devices. The neural network model compromises of input layer, one hidden layer, and an output layer, developed using Tensor-flow and Keras libraries. It was trained using the Adam optimizer and sparse categorical cross-entropy loss function for 35 epochs with a batch size of 16. Results indicate that the neural network model and appropriate classifiers can successfully classify soil moisture levels into 4 distinct categories based on given dataset, demonstrating its potential as a cost-effective and efficient alternative to traditional soil moisture measurement techniques.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room A Pune, India

12:15pm IST

Projecting the Digital Frontier: Predictive Bibliometric Study of E-Games and Gamification in Education
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Ruby Chanda, Reena Lenka
Abstract - E-games and gamification stand out among the innovative pedagogical techniques brought out by the swift integration of digital technology in educational settings because of their capacity to revolutionize the learning process. In order to outline the development, present trends, and future research directions on e-games and gamification in education, this study uses a predictive bibliometric analysis. Using an extensive dataset of Scopus publications from large academic databases, we use cutting-edge bibliometric methods to pinpoint important research themes, significant figures, and foundational works in this emerging subject. Our study displays that, particularly in the previous ten years, there has been a perceptible upsurge in scholarly interest in and publications about e-games and gamification, which is indicative of the rising understanding of these technologies' capacity to engage and encourage students. The study shows that this research area is multidisciplinary, with notable contributions from computer science, psychology, educational technology, and game design. The design and execution of educational games, the psychological principles behind gamified learning environments, and the effectiveness of gamification in improving learning outcomes are among the key study areas that have been highlighted. Our study offers useful insights for academics, educators, and policymakers looking to maximize the educational potential of e-games and gamification by identifying existing tendencies and predicting future developments. In addition to highlighting the current status of research, this predictive bibliometric study also lays out a roadmap for future studies and applications in the digital frontier of education.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room A Pune, India

12:15pm IST

Smart Farming in India: The Role of AI in Agricultural Transformation
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Ruby Chanda, Vanishree Pabalkar
Abstract - The revolutionary potential of artificial intelligence (AI) to improve agricultural practices and outcomes in India is examined in this research. India, a country that depends mostly on agriculture, has many difficulties, such as erratic weather patterns, pest infestations, and ineffective resource management. Artificial intelligence (AI) technologies provide creative answers to these issues, including machine learning, predictive analytics, and IoT-enabled gadgets. AI has the capacity to analyse enormous volumes of data and deliver timely insights and practical recommendations to farmers, resulting in increased agricultural yields, more efficient use of resources, and sustainable farming methods. This paper looks at the use of AI in Indian agriculture today, namely in the areas of automated irrigation systems, insect detection, and precision farming. It also covers the socioeconomic effects of AI adoption, emphasising how farmers could benefit from higher productivity and profits. In order to fully realise the benefits of artificial intelligence (AI), the study finishes with an analysis of the opportunities and obstacles related to its deployment in the Indian agriculture industry. It emphasises the necessity for supportive policies and infrastructure.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room A Pune, India

12:15pm IST

System and method of Conducting Online Exam using Image Processing
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Ruby Chanda, Reena Lenka
Abstract - This invention utilized image processing to achieve the MCQ revision in an extremely simple way. It creates extraordinary work to arrange to eliminate the boundaries of multi-decision evaluation remedies. We are here utilizing the Open-Source PC Vision Library (Open CV) to process and address the responses. The utilization of Numerous Decision Questions (MCQs) to test the information on an individual has been expanded progressively. These tests can be assessed either utilizing OMR innovation or physically. Continuously, it is very challenging to have an OMR machine under all conditions, and simultaneously, manual adjustment is tedious and mistake-prone. These disservices have been conquered in our proposed framework by utilizing an advanced picture-handling strategy to address the responses on the OMR sheet.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room A Pune, India

12:15pm IST

A Comparative Study of Different IDS approaches and their Performance
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Tushar Kulkarni, Pradyumna Khadilkar, Behara Roshan Kumar, Rupesh Jaiswal
Abstract - In the modern era of rapid Internet access, it is essential to safeguard electronic devices and the sensitive data they contain from cybercriminals, who constantly find new ways to exploit users by seeking holes in the system and manipulating them. Globally, the average cost of a data breach in 2024 is expected to exceed $4 million, an increase of ten percent from the previous year. A better intrusion detection system capable of handling both legacy and zero-day threats is needed. To address this, we reviewed several relevant publications, most of which were published in the last five years. This has enabled us to compile the latest techniques and breakthroughs. Although NSL-KDD, CICIDS-2017, and UNSW-NB15 received the most attention, other datasets were included. The performance of various intrusion detection systems has been compared in the literature that has been cited. We have proposed a novel methodology known as hybrid intrusion detection system (Hybrid-IDS), which combines both signature- and anomaly-based IDS.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room B Pune, India

12:15pm IST

Adaptive Learning Platform: Empowering Students with Grasping Abilities
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Vaishali Rajput, Swapnil Patil, Diksha Shingne, Uzair Tajmat, Yashodip Undre, Urja Wagh
Abstract - With the rapid advancements in artificial intelligence (AI) and machine learning (ML) within the Ed-Tech sector, our project explores the development of an Adaptive Learning Platform aimed at enhancing student comprehension and engagement. The platform is specifically designed to cater to diverse learning styles by delivering personalized learning experiences that adapt to each individual’s pace, needs, and preferences. Leveraging AI-driven features, the platform ensures that students achieve mastery of core concepts. By identifying patterns in user interactions, such as repeated engagement with specific video segments or prolonged pauses, the system recognizes areas where learners may struggle and responds with customized explanations powered by GPT technology to clarify complex concepts in text form. Upon successful course completion, the system generates certificates as formal recognition of the user's mastery, providing tangible proof of their progress and achievement. This innovative approach not only addresses the inherent limitations in traditional e-learning platforms by fostering a dynamic and responsive learning environment but also sets a new benchmark in personalized education technology. By incorporating cutting-edge AI and ML, this platform promises to significantly enhance student learning efficiency, knowledge retention, and overall academic performance, offering a transformative shift in how education is delivered and experienced.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room B Pune, India

12:15pm IST

An Online Judge System in Learning Management System
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Sara Umalkar, Aditya Toshniwal, Varad Vanga, Siddhesh Upasani, Vedant Joshi, Sanchit Joshi
Abstract - This paper surveys the integration of an Online Judge System (OJS) within a Learning Management System (LMS) for engineering education, focusing on its role in enhancing programming skills and student engagement. The OJS automates the evaluation of coding assignments, providing real-time feedback, scalability and secure code execution through containerization and sandboxing techniques. The LMS, equipped with interactive modules and gamification, uses the OJS to assess algorithmic solutions against predefined test cases, supporting both learning and competitive programming. The study explores key features such as automated grading, performance evaluation and robust security measures to ensure fairness. By analyzing existing OJS platforms and their applications, this paper highlights the effectiveness of such systems in fostering problem-solving skills and preparing students for industry challenges. The survey also identifies emerging trends and opportunities for improving the OJS in educational settings.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room B Pune, India

12:15pm IST

Credit Card Fraud Detection using Machine Learning & Python
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Ruchita Borikar, Sakshi Thorat, A.S. Ingole, U.A. Kandare
Abstract - Credit card fraud remains a significant issue in the financial industry, with increasing numbers of transactions being processed online and through digital platforms. Traditional fraud detection systems, relying on predefined rules and manual analysis, are no longer adequate to combat the growing complexity and scale of fraudulent activities. In response, machine learning (ML) has emerged as an effective solution for detecting fraud in real time, offering the ability to analyze large datasets and recognize patterns that may indicate suspicious behavior. This project aims to build a system for fraud using machine learning methods, based on a dataset sourced from Kaggle and guided by an IEEE paper. The project involves several key stages, starting from raw data preprocessing and feature selection, followed by training and evaluating machine learning models with algorithms like Decision Trees, Logistic Regression and Random Forest and Support Vector Machines (SVM). Additionally, the model is evaluated through 5-fold cross validation to ensure robustness. This system not only enhances fraud detection accuracy but also minimizes false positives, thereby improving overall efficiency.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room B Pune, India

12:15pm IST

Decentralized Approach for Wildlife Protection Using Blockchain Technology
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Md Imran Alam, Swagatika Sahoo, Angshuman Jana
Abstract - Wildlife is a crucial part of our environment that must be protected to preserve various animal species and their habitats. Effective wildlife conservation faces numerous challenges, including securing funding, preventing illegal wildlife trade, protecting animals from unnecessary harm, ensuring proper treatment, and managing shelters for domesticated animals. Current solutions to these challenges are often centralized, making them vulnerable to corruption and single points of failure. The adoption of blockchain technology to wildlife protection provides secure tracking of animal medical records, transparent management of funding transactions, and real-time alerts to authorities regarding potential threats, all automated through smart contracts. This paper explores the theoretical and practical benefits of integrating blockchain into animal welfare systems. By addressing key issues like illegal trade and medical record management, blockchain technology can significantly improve the security and effectiveness of wildlife conservation efforts.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room B Pune, India

12:15pm IST

Graph based K - Means Clustering for Symbol Recognition
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Vaishali S. Pawar, Mukesh A. Zaveri, Radhika P. Chandwadkar, Varsha H. Patil
Abstract - Pattern recognition involves identifying specific patterns or features within the provided data. Social Network Analysis, Fraud Detection, Biological and Medical Networks, Recommendation Systems, Telecommunication Networks, Traffic and Transportation Networks, Computer Vision and Image Processing, Natural Language Processing (NLP) are among the constantly expanding applications of pattern recognition. Graphs are a potent model utilized in several fields of computer science and technology. This study presents a technique based on graph databases for graphical symbol identification. The suggested method employs graph-based clustering of the graph database, which markedly decreases the computational complexity of graph matching. The suggested algorithm is assessed with a substantial quantity of input hand drawn images, and the output results indicate that it surpasses previous algorithms.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room B Pune, India

12:15pm IST

Indoor Navigation System using BLE
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Aatish Aher, Chinmay Sanjay Lonkar, Ganak Bangard, Sanjay T. Gandhe
Abstract - Bluetooth Low Energy (BLE) technology is increasingly recognized as an effective indoor navigation solution, particularly in areas where GPS cannot function. This review focuses on BLE-based systems tailored for visually impaired users, examining their advantages and limitations. It compares various indoor localization methods such as Wi-Fi, BLE, and image processing in terms of accuracy, efficiency, and deployment simplicity. BLE beacons are highlighted for their low power consumption, adaptability, and cost-effectiveness, making them suitable for large-scale, real-time navigation. To enhance accessibility, the review discusses integrating assistive features like audio navigation using BLE signals paired with voice assistants, ensuring hands-free operation. Additionally, it covers technologies such as haptic feedback and obstacle detection, which provide non-verbal cues to alert users about obstacles or landmarks. The system architecture is explored, focusing on app integration, user-friendly interfaces for visually impaired users, and cloud management for delivering real-time updates. By identifying research gaps, this review suggests directions for future development of BLE navigation systems aimed at enhancing the independence and mobility of visually impaired users in indoor settings.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room B Pune, India

12:15pm IST

Intelligent Farming System for Prediction of Optimal Crop, Fertilizer & Irrigation system
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Nivedita Shimbre, Prema sahane, Shrutika Amzire, Arya Ganorkar, Pavan Kulkarni, Pawan Bondre
Abstract - As INDIA is on the verge of getting digitalised India, farmers also need to become smart. They need a system that recommends them crops and its varieties and proper scheduling methods for increasing their crop yield. To obtain the best crop varieties for a given area factors such as soil moisture, fertility and climatic condition are required. And our system will suggest crops and its varieties on the basis of previous History of crop production and time required for the crop. This system also focuses on recommending suitable fertilizers for crops. It also provides the schedule and quantity of fertilizer to be used. This system will use various Machine learning Algorithms like Support Vector Machine, Random Forest Regressor, Gradient Boosting, Linear Regression etc to recommend best crops, its varieties and fertilizers. It also provides Automatic Irrigation Management by using IoT.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room B Pune, India

12:15pm IST

Precision Rainfall Prediction in India: A Machine Learning Approach for Sustainable Agriculture
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Jay Bodra, Anshuman Prajapati, Priyanka Patel
Abstract - India is one of the leading agricultural countries in the world, and the nation's economy depends heavily on agriculture. For good crop yield, prediction of precipitation is necessary to increase agricultural output and ensure a supply of food and water to maintain public health. To reduce the issue of drought and floods occurring in the nation, wise use of rainfall water should be planned for and implemented. Numerous studies have been carried out utilizing data mining and machine learning approaches on environmental datasets from various nations in order to forecast rainfall. This study's primary goal is to pinpoint the amount of rainfall in several regions of India in the past hundred years and apply machine learning techniques to forecast the amount of rain that will fall in a particular month and year in a given region. The dataset was collected from the government site of the rainfall database for performing machine learning techniques. The Random Forest model's ensemble approach, robustness to noise, ability to handle nonlinear relationships, feature importance analysis, scalability, and tuning flexibility make it a particularly effective choice for rainfall prediction in this project. Its versatility and performance make it a valuable asset for providing accurate and reliable rainfall forecasts to support decision-making in various sectors, such as agriculture, water resource management, and disaster preparedness.
Paper Presenter
avatar for Jay Bodra
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room B Pune, India

12:15pm IST

RAG-Based Chatbots for Document Query – A Comprehensive Review
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Mangesh Salunke, Tilak Shah, Vishal Bhokre, R. Sreemathy
Abstract - Chatbot also known as conversational agents is an interactive software that responds to users’ queries using artificial intelligence. While traditional machine-learning chatbots have shown promise, LLM-powered chatbots offer more natural and relevant conversations, enhancing the user experience. The rise of OpenAI’s ChatGPT, Google’s Gemini, LangChain, etc has widened the horizon of applications of chatbots to almost every sector including education, healthcare, banking, entertainment, e-commerce and telecommunications. The main objective of this comprehensive study is to explore and analyze the current advancements in chatbot development using different artificial techniques. This survey paper examines key trends in the development of chatbots, the components and techniques used, and the evaluation metrics employed to measure performance. We discuss different metrics used to evaluate chatbots' performance like accuracy, BLEU, ROUGE and relevancy. The results suggest that LLM-powered chatbots facilitate more natural and contextually appropriate conversations than traditional machine-learning models, leading to a marked enhancement in user experience. By synthesizing insights from existing research, we aim to provide a comprehensive understanding of RAG-based chatbot technology.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room B Pune, India

12:15pm IST

Assessing the Potential of Blockchain Technology in Transforming Digital Identity Management Systems
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Aafiya Anjum Abdul Rafique, Martin H Mollay, Shailesh Gahane, Deepak S. Sharma, Pankajkumar Anawade
Abstract - Blockchain technology is a complete makeover for digital identity management, which solves the major problems seen with the previous centralized systems, like inefficiency, lack of control in front of the users, and susceptibility towards data breaches. The paper draws the modes of changing the digital identification system using blockchain technology by providing insights into how it is decentralized, transparent, and safe. Blockchain could improve privacy and trust by advising people to take control of their data and reduce dependency on other parties. The literature analysis shows that blockchain technologies, particularly those based on cryptographic security, give solid answers to privacy problems, which allow users to share data but keep sensitive information safe. Analysis of scalability issues, limitations in storage, and massive computational overhead of consensus protocols like Proof-of-Work. It proposes alternative solutions such as proof-of-stake, sharding, and sidechains that may circumvent their weaknesses. Interoperability between different blockchain systems remains one of the most significant areas of development toward support at a broader scale of adoption. Despite the challenges above, blockchain has immense possibilities in many sectors, including government identity systems, healthcare, and finance, for safe and independent management of identity. It underlines the revolutionary importance of blockchain-based identification solutions within the digital economy. It calls for further research, pilot projects, and regulatory modifications to overcome these issues and realize the full potential of these solutions.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room C Pune, India

12:15pm IST

BI-MODEL DEEP LEARNING ARCHITECTURE FOR IMPROVED DETECTION OF SEIZURES FROM EEG SIGNAL
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Sanila S, S Sathyalakshmi, D. Venkata Subrahmanyan
Abstract - Electroencephalography (EEG) remains the leading technique for identifying and diagnosing epileptic seizures due to its effectiveness in monitoring brain activity. However, the nonstationary nature, large volume, and rapid accumulation of EEG data present significant challenges for traditional analysis methods. To address these issues, a transition from basic data mining techniques to advanced machine learning and deep learning approaches is essential. This study focuses on developing algorithms to enhance the accuracy of seizure predictions while minimizing the volume of EEG data processed. The proposed method involves dividing EEG signals into fixed-sized windows to reduce data complexity, followed by extracting key features such as the top_k amplitude values from each window. These extracted features, combined with statistical measures of the EEG data, are then used to train classification algorithms to determine whether a seizure is occurring or not. This approach aims to balance efficiency and predictive accuracy, addressing both the computational and diagnostic challenges associated with EEG analysis. The entire raw dataset is experimented with Deep Neural Network Algorithms like Bidirectional Long Short-term memory with additional functionalities like Attention Mechanism and Spatial Weight matrix addition. Finally, both 2 D CNN and BiLSTM are applied in parallel with the additional functionalities of FFT applied EEG signal. The results are promising and found that ANN predicted with an average accuracy of 98.5%, 2DCNN with 94.3% BiLSTM with 99.6% and bi model architecture of top_k 2D CNN with Bi LSTM on FFT applied EEG signal including attention mechanism and Spatial weight matrix predicts better than all previous models with 99.8 % accuracy. .
Paper Presenter
avatar for Sanila S
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room C Pune, India

12:15pm IST

Binary Classification of Mushroom Edibility Using Machine Learning Algorithms
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Dhairya Goel, Chakshu Gupta, Saarthak Bansal, Chaitali Bhowmik
Abstract - Mushrooms are a type of fungi, which have unique traits and health advantages. They also helps to fight against the cancer cells. Our main initiative of this research is to classify the mushrooms into two categories one is poisonous and other is non-poisonous. Mushrooms are of of different categories some of them can be used for daily needs but some of them has toxic ingredients in them which is harmful for consumption. So classifying the mushrooms correctly becomes very important as if someone uses a toxic mushrooms it can lead to serious health effects. Classification algorithms helps to solve this issue. Here we are using various ml algorithms to classify the mushrooms some of them are random forest and Decision tree Algorithm. Our main goal is to categorize the mushrooms correctly. We have achieved the highest accuracy with random forest. It is able to differentiate the mushrooms most effieciently and effectively. These results shows that classification algorithms can prove to be very important in categorizing mushrooms.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room C Pune, India

12:15pm IST

Crop Yield Estimation Using Machine Learning and Deep Learning
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Pradnya Apte, Dipti Durgesh Patil
Abstract - Agriculture forms the backbone of many developing countries, including India. Accurate crop yield estimation can give societies a better handle on food security and resource management. Existing studies on crop yield and harvest prediction apply deep learning as well as machine learning models. Various crop parameters like soil type, climate, water content, and so on have been used to predict crop yield. More advanced techniques include the use of satellite imagery and optical and SAR data along with plant indices like NDVI. Machine Learning algorithms, including KNN, SVM and Random Forest regression have been widely used for yield estimation. Deep learning approaches work by extracting salient and relevant features from images or non-visual data to estimate crop production. Networks like 3D-CNNs, LSTMs and Auto encoders have achieved significant improvement in accuracy in estimating crop yields from satellite images. This paper aims to summarize the techniques and models being used for the purpose of yield estimation along with limitations, and possible areas of further study.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room C Pune, India

12:15pm IST

Emotional Intelligence Integration in Artificial Intelligence
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Kasif Qamar, Supriya Narad
Abstract - Emotional intelligence in Artificial Intelligence is an important and exciting growing field with much potential for assisting human-computer interactions in different domains. Self and others’ emotional awareness are referred to as emotional intelligence and is steadily being deemed paramount to develop technologies in artificial intelligence that will in the end be effective when handling needs and human states. In recent researches, it has been observed that although in real way, AI cannot be so expected to feel like human beings simulation does exhibit mimic emotions that add to the enhancement of the user experience and acceptance more so in service-oriented applications Emotional Simulation of Artificial Intelligence and The awareness of the EI is really rising in various organizations in today’s workplaces especially with integration and Automation of work using artificial intelligence. Since the roles and responsibilities of human beings working in industries will change due to AI and automation, EI skills will be are important to be applied by the employee’s at all organizational levels. However, machines are good at things that involve rules of logic and therefore getting them to understand and among the limitations of URLs of expressing human feelings, answering to the feelings still remain a problem to be solved in AI. Hence, improving EI becomes something of enormous value to be competent in the new era of jobs.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room C Pune, India

12:15pm IST

Employee Churn Prediction using Machine Learning and Classification Techniques
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Anup Vinod Pachghare, Smita Deshmukh, Satish Salunkhe
Abstract - Human Resources Management plays a key role in the company’s growth by recruiting high-quality employees and evaluating their performance by using the Machine Learning (ML) technique. Despite these rigorous efforts some employees still resign before their contracts expire, which negatively impacts business. Existing methods have considered various factors influencing employee turnover across different employee groups. This paper proposes an Ensemble Learning approach which integrates AdaBoost, K-Nearest Neighbour (KNN), Random Forest (RF), stacking and Voting to enhance churn prediction accuracy. The ensemble learning mitigates the risk of overfitting by combining predictions from multiple models, making it less sensitive to irrelevant features. This approach efficiently captures diverse patterns in the employee churn data, achieving better accuracy. AdaBoost captures complex patterns, while KNN extracts valuable data from employee churn data. By stacking these methods, their combined strengths lead to enhanced accuracy in predicting churn data. Initially, the data collected from employee churn records and pre-processing phases handle unwanted noise and min-max normalization, which standardizes the feature vector to ensuring uniformity across the dataset. The proposed ensemble model obtained 91.06% accuracy, and 0.8853 of recall on the employee churn dataset compared with conventional techniques like Artificial Neural Networks (ANN).
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room C Pune, India

12:15pm IST

GPS Based Toll Tracking and Speed Monitoring System
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Pranav Jawale, Srushti Bonde, Dhruv Gidwani, Omkar Aher, Bhavana Kanawade
Abstract - The GPS-based toll tracking and speed monitoring system uses GPS technology to revolutionise highway toll collection and road safety. The system calculates payables based on distance travelled by the vehicles on highways and monitors vehicle speeds to ensure fair charges while discouraging speeding. The system delivers real-time notifications for toll deductions and penalties, supporting transparency and eliminating the need for traditional toll booths. Users can access a dashboard to track their journey, view toll routes, and review their vehicle and transaction history, offering a comprehensive and user-friendly experience.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room C Pune, India

12:15pm IST

Intrusion Detection Systems: Advanced Techniques for Network Security
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Akshay Kumar, Sudhir Agarmore, Edidiong Akpabio, Kumar Gaurav, Nitesh Kumar, Aditya Mandal
Abstract - Intrusion Detection Systems (IDS) has been seen to be an integral aspect of network security, where an extra layer of protection mechanisms may contribute to protection against different kinds of cyberattacks. With fast-evolving cyber threats, from simple malware to sophisticated zero-day attacks, continuous developments are required in IDS technologies. Traditional IDS models, such as signature-based detection, work better when applied to known threats but are pretty weak against emerging and unseen types of attacks. On the other hand, anomaly-based IDS models give pretty good results in finding unknown attacks, but most of them report a high false-positive rate. Finally, this work provides an overview of current state-of-the-art IDS methods and focuses on machine learning, deep learning, and hybrid models for intrusion detection. We further discuss the benefits and limitations of both the supervised and unsupervised learning algorithms and their applications in anomaly detection and pattern identification within network traffic. It investigates the applications of deep learning methodologies, such as CNNs and RNNs, within the IDS framework. These models give a high chance to work with complex data and detect various kinds of sophisticated attacks efficiently. Hybrid systems that combine traditional detection methods with machine learning demonstrate better accuracy and fewer false positives. Among the future directions of research that will be discussed are development challenges for the scalability of IDSs, real-time detection, and handling zero-day vulnerabilities for improving the efficiency of IDS in securing modern network infrastructures.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room C Pune, India

12:15pm IST

Optimizing Airline Ticket Pricing: A Predictive Modeling Approach for Flight Fare Forecasting using Machine Learning
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Koyana Jadhav, Aditya Shinkar, Mayank Sohani
Abstract - In the light of dynamic pricing policy, flying is becoming too expensive, and it's really hard to book tickets at proper prices. In response to this, researchers began discussions on how machine learning models could be used to predict an approximate fare for a flight so that the passenger could purchase at the most ideal time to get low fares. These models consider travel dates, destination, airlines, stopovers, timing of booking, holidays, and demand. Techniques used are Decision Trees, Random Forest, Gradient Boosting, and ANNs. These have different strengths-some, such as ensemble methods, Random Forest and Gradient Boosting, with high robustness in terms of predictions because they average multiple decision paths; ANNs represent complex, non-linear relationships but at the cost of significant computation. Model performance was evaluated using Mean Absolute Error, Root Mean Square Error, and R-squared. This research informs passengers on price trends, and better booking decisions will be achieved. Real-time data integration and more advanced algorithms comprise future improvement prospects. The research work bridges the gap between revenue strategies employed by airlines and the need of the travellers to travel affordably, thereby optimizing passengers travel costs.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room C Pune, India

12:15pm IST

Secure voice communication using cryptographic algorithms
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Ketan J P, Amulya A Shetty, Ashwini Bhat
Abstract - In an era defined by using heightened virtual connectivity, the safety and privateness of voice communication are important. This research paper introduces an innovative system designed to establish secure voice communication between two systems. Including a combination of cryptographic algorithms, including RSA, 3DES (Triple Data Encryption Standard), modified RSA, and modified 3DES, the proposed solution gives confidentiality and integrity of voice data during transmission. The need for such a system is underscored by the growing demand for secure communication in sectors where sensitive information is exchanged, such as military and intelligence operations, healthcare, and business. This study explains a client-server architecture where the client system employs RSA and 3DES for encryption, while the server system utilizes corresponding decryption mechanisms. Socket programming serves as the connectivity bridge, with the server's IP address acting because the transmission key.
Paper Presenter
avatar for Ketan J P
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room C Pune, India

12:15pm IST

Advancing Connectivity: A Comprehensive Review of Mobile Networks from 1G to 6G
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Sunil Kumar, Sanya Shree, Saswati Gogoi, Anshika Shreshth
Abstract - The evolution of wireless communication, which led to the introduction of cellular networks, has enabled the highly interconnected world we experience today. This research paper discusses the developmental course from the first generation (1G), which introduced analog voice communication to the higher generations of networks, which brought digital signals into play. Multiple access technologies and significant emerging technologies of cellular networks from 1G to 5G are discussed. A comparative analysis among different generations of networks is presented, and a vision of the forthcoming sixth generation is presented. The role of the present widely used fifth-generation (5G) in defence, healthcare and education is discussed along with other applications. The challenges and future directions of the sixth generation (6G) Wireless Communication Network (WCN), which aims for ultra-low latency and extremely high energy efficiency using the specifications of artificial intelligence are discussed.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room D Pune, India

12:15pm IST

Anomaly Detection in Videos for Chain Snatching using Meta-Learning
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Shradha Naik, Suja Palaniswamy, Nicola Conci, Vishal Metri
Abstract - Anomaly detection in videos from CCTV cameras can be an important strategy for crime analysis and prevention. The main focus of our work is on detecting the crime of chain snatching from videos captured in India. Due to the absence of a training set of similar Indian videos, it is challenging to design a classifier for this crime. Hence a technique called Model Agnostic Meta-Learning (MAML) is used to train a network on the well-known UCF crime dataset for detection of chain-snatching in a dataset custom built by us. MAML is further developed to result in a method called Sampling-based Meta-Learning Anomaly Detection (SMLAD). With this, the characteristics of MAML are used automatically to classify chain-snatching as an anomaly and obtain best accuracy and AUC scores of 86 % and 84 % respectively. Thus the proposed work demonstrates the efficacy of MAML to correctly classify chain-snatching which constitutes completely unseen data, as a crime-related anomaly.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room D Pune, India

12:15pm IST

DSPredict: A Novel Approach for Accurate Identification of Eligible Ph.D. Subjects
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Sanal Kumar S P, Arun K
Abstract - Aspiring researchers have to consider choosing an appropriate Ph.D. subject. However, the complexity of the regulations and the large number of possible choices especially in context of cross and multi disciplinary approach render it challenging. The manual processing of applications by universities is time-consuming and prone to errors, which leads to inefficiencies and in-ordinate delays. We created DSPredict, a novel approach that employs machine learning to identify the most appropriate Ph.D. subject for each applicant. Our methodology assesses application profiles and predicts the most suitable subjects. The findings suggest that DSPredict surpasses traditional methods, resulting in increased accuracy and significantly shorter time to identify appropriate subjects.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room D Pune, India

12:15pm IST

DySAMRefine: A Dynamic Scene Adaptive Mask Refinement for Object Segmentation and Tracking in Complex Videos
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Sudha S K, Aji S
Abstract - Rapid advancements in video surveillance and analysis require advanced frameworks capable of detecting, segmenting, and tracking objects in complex, dynamic scenes. This paper introduces DySAMRefine, a novel dynamic scene adaptive mask refinement strategy for robust video object segmentation and tracking (VOST) in dynamic environments. DySAMRefine is built upon a Mask R-CNN pipeline for instance-level segmentation and incorporates a long short-term memory (LSTM) network to capture temporal dependencies, ensuring smooth and consistent object tracking across frames. A spatio-temporal attention block (STAB) is introduced to maintain temporal coherence, supported by a temporal consistency loss (TCL) that penalizes abrupt changes in masks between consecutive frames, promoting temporal smoothness. DySAMRefine dynamically adjusts mask refinement based on the complexity of the scene and optimizes performance in static and highly dynamic environments through a deformable convolutional network (DCN). The training process employs an efficient mixed precision scheme to minimize computational overhead, enabling real-time performance without sacrificing tracking precision. Extensive experiments and ablation analysis demonstrate that DySAMRefine enhances the accuracy and robustness of VOST, achieving superior J&F scores on benchmark datasets.
Paper Presenter
avatar for Sudha S K
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room D Pune, India

12:15pm IST

Empowering the Deaf and Mute with Real-Time Sign Language Translation Using Computer Vision and Deep Learning
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Roshan Kamthe, Yash Gaikwad, Shubham Pawar, Kishan Chandel, Pushpavati Kanaje
Abstract - The purpose of this research is to develop a system that can identify hand movements, facilitating easier communication for the deaf and mute. Apart from providing voice output for calls coming in from non-deaf individuals, the system also includes a mobile application that allows users to communicate through hand gestures. Our solution gives those who are hard of hearing or deaf a straightforward way to communicate by utilizing modern technologies like computer vision and machine learning. The goal of this project is to develop a hand gesture detection system that will improve communication accessibility for people with speech and hearing problems, especially the deaf and mute community. Our project's primary objective is to provide individuals who are incapable a clear solution.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room D Pune, India

12:15pm IST

Implementation of Multi-Threaded Database for Effective Caching using Dynamic Data Hashing
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Atharva Desai, Anurag Raut, Aditya Thatte, Ramchandra Mangrulkar
Abstract - This project proposes an advanced, multi-threaded, opensource NoSQL database architecture designed to extend and improve upon existing database systems. The architecture utilizes a shared-nothing approach, sharding the keyspace into multiple parts, each managed by a dedicated thread. By employing hash-based ownership, the need for synchronization is eliminated, thereby reducing performance bottlenecks. The system is optimized for distribution within a single machine, leveraging a thread pool technique to manage potential thread overhead efficiently. Additionally, the database replaces traditional hash tables with dashtables, which minimize rehashing overhead and optimize memory usage by segmenting the hash space into smaller, more manageable portions. This novel approach significantly improves efficiency and scalability, providing a compelling alternative to existing solutions like Redis.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room D Pune, India

12:15pm IST

IoT-Enabled Waste Bin Monitoring using Raspberry Pi and Cloud-Based Analytics
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Trupal J. Patel, Mahek D. Viradiya, Jaykumar B. Patel, Dhruvi J. Patel, Prisha M. Patel, Dhruv Dalwadi
Abstract - In the era of surging urbanization, the problem of managing waste effectively has become a major concern. This research paper provides a solution by providing a cutting-edge system for real-time monitoring and management of waste bins using IoT sensors integrated with cloud computing technologies. By using an ultrasonic sensor (HC-SRO4) to precisely and accurately gauge levels of waste with a DHT22 sensor to monitor conditions related to the environment. This solution provides innovation that enables the data collected precisely to enhance the efficiency of waste management. The data that is collected is then processed by a Raspberry Pi, which is the core unit of the whole system, that transmits the whole information to a cloud platform where analysis and visualization are done. This makes it possible for stakeholders to access real-time insights of waste levels and factors affecting the environment, which constantly improves the process of decision-making. Moreover, the system integrates predictive analysis to predict waste collection trends, enabling the optimization of collection schedules and minimizing the trips that are unnecessary for collection. By this way, the operational cost can be reduced, and it helps in improving the efficiency of service. This approach not only considers logical challenges but also serves sustainable waste management practices. Ultimately, this research illustrates the potential of IoT technologies to transform, creating smarter and more adaptive environments in urban areas.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room D Pune, India

12:15pm IST

ROBOTIC OPERATING SYSTEM BASED GROUND CONTROL STATION FOR UNMANNED AERIAL VEHICLES
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Divyashree HB, Deepthi Chamkur V, Preesha Tandon, Laranya Subudhi
Abstract - In today's technology age, a Ground Control Station System application software for offboard mode and manual control of unmanned aerial vehicles is essential for a variety of onboard activities like tracking, surveillance, and patrolling. This study discusses software that controls and collects important data from unmanned aerial vehicles. The program is developed in Python 3, and the graphical user interface is created with the Qt5 framework. Melodic is the robot operating system (ROS) that facilitates communication and networking. The software allows you to control the drone's forward, backward, up, down, left, and right motions. The live feed from the RGB camera (day camera) and the night vision camera may be watched and saved as snapshots. It is also possible to save the live stream footage to a CD. Object tracking and detection functions are offered for surveillance purposes. The software may also be used to operate a gimbal fitted to the drone. The entire program is beta tested on the Gazebo real-world simulation, and the experimental findings are based on a real-world hexacopter flight.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room D Pune, India

12:15pm IST

The Future of Profitable Farming: A Review of AGRITECH NAVIGATOR’s Impact
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Sandeep M.Chaware, Mohit Matte, Pratik Dahagaonkar, Anurag Deotale, Laukik Pagar, Jayesh Sarwade
Abstract - Agriculture is the land-cultivation, crop-growing, livestock-raising processes. A nation's economic growth depends on its agricultural sector. Agriculture makes for about 58% of a nation's primary revenue source. Up to now, farmers sow and cultivate or practice agriculture based on favorable weather and soil conditions without considering the future supply and demand of crops and the type of agriculture practiced, thus often doing reduce profits from agriculture. Typically, when demand for a crop is low and supply is high, the price drops too low, leading to debt for the farmer and vice versa. Predicting what crops should be grown or what type of agriculture should be adopted in today's world is essential to meet people's needs and increase farmer productivity. Machine learning, data mining, and data analytics can be used to collect data, train models, and predict the market demand, supply chain, demanding type of agriculture and location of agriculture for revenue generating agriculture. This will help reduce losses for farmers. Due to the ongoing changes in the world, the proposed Machine Learning assistanat helps determine how to manage agriculture intelligently. It assists an individual towards profitable agriculture This work's primary goal is to sustain a single farm profitably while achieving high output at reasonable expenses. Questions including pricing comparisons, government activities, plant protection, animal husbandry, weather, and fertilizer management are addressed by the proposed method.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room D Pune, India

12:15pm IST

Voice - Controlled IoT Devices: A Comprehensive Review of Cybersecurity Challenges
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Priyanshi Desai, Parth Shah
Abstract - The increasing adoption of voice-controlled IoT devices, such as Amazon Alexa, Google Home, and Apple’s Siri, has transformed modern interactions with smart systems in various sectors, including home automation, healthcare, and industry. While these devices offer convenience and enhanced accessibility, they are also vulnerable to significant cybersecurity threats. This paper examines the security challenges associated with voice-controlled IoT systems, focusing on key vulnerabilities such as voice spoofing, man-in-the-middle attacks, insecure APIs, and data privacy concerns. Additionally, the paper explores various attack vectors, including adversarial attacks and physical tampering, and assesses current mitigation techniques like biometric voice authentication, secure data transmission, and anomaly detection. Privacy concerns are also discussed, particularly in relation to data retention and third-party access. As the use of these systems continues to grow, advanced cybersecurity measures, including quantum-resistant encryption and enhanced biometric methods, are essential for securing voice-controlled IoT devices. Furthermore, the establishment of regulatory frameworks to govern the handling of voice data is critical. This paper concludes by identifying future directions to improve the security and privacy of voice-controlled IoT devices, emphasizing the need for innovative solutions to counter an expanding array of cyber threats.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room D Pune, India

12:15pm IST

A Hybrid Framework for Short-Text Similarity Detection by Integrating Semantic and Syntactic Measures
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Mumthaz Beegum M, Raseena Beevi, Aji S
Abstract - Short-text similarity is a vital research area in NLP with significant implications for use cases like search recommendations and question-answer systems. Traditional models often focus solely on semantic similarity, overlooking syntactic factors. Our approach uses the AnglE (Angle Embedding) method for semantic similarity, which transforms text into high-dimensional vectors to capture the nuanced meanings and relationships between words and phrases. The cosine similarity measure is then employed to calculate the similarity score from these vectors. We apply the weighted Tree Edit Distance (TED) method for syntactic similarity, which measures structural differences between parse trees by calculating the minimum cost required to convert one tree into another through a series of edit operations. By integrating these two complementary similarity measures, our approach aims to deliver a more thorough and accurate evaluation of text similarity. This methodology introduces an advanced technique that combines semantic and structural information to enhance the assessment of short-text similarity. The integrated methodology introduces a sophisticated framework that not only enhances the precision of similarity evaluations but also bridges the gap between semantic and syntactic analyses, thereby offering a more comprehensive evaluation of text similarity.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room E Pune, India

12:15pm IST

A REVIEW OF STRESS IN PLANTS
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Suhail Manzoor, Rahul Gupta, Prakhar Sharma, Yash Mittal, Mohammad Arshad Iqbal
Abstract - Plants are mainly suffering from abiotic stress such as drought, salinity, and widely temperature decrease or increase. Thanks to notable advancements in machine learning and hyperspectral imaging, detecting stress in plants has never been easier. For that matter, different machine learning techniques such as Random Forest, Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Kernel Ridge Regression have been used. Hyperspectral image has been widely used in classifying crop water stress by classifiers such as Random Forest and SVM. CNNs have been widely used for plant phenotyping under multiple stresses thanks to their good prediction results, but that entails many computation problems. Other methods, such as Kernel Ridge Regression and Extreme Gradient Boosting have emerged to target specific stress indicators like leaf reflectance spectra at key wavelengths; however, these typically rely on specialized equipment and significant data preprocessing. Here, we synthesize these various approaches to plant stress detection and present an integrated approach for abiotic stress recognition in plants and advocate for models with high generalizability across different environmental conditions or types of biotic stresses.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room E Pune, India

12:15pm IST

Autoimmune Skin Disease Detection with Enhanced Imaging: A Comprehensive Review
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Shriya Vadavalli, Geethika Bodagala, T Sridevi
Abstract - This paper conducts a systematic literature review about the current status of the art in the detection and diagnosis of autoimmune skin diseases. This paper identifies recent studies and advancements in terms of key technologies, methodologies, and approaches used in this domain specifically with regard to imaging and deep learning techniques. Therefore, the work underlines scopes for further studies in terms of improving diagnostics accuracy and increasing robustness and accessibility for diagnostic solutions. This piece of work also puts a hole in the existing literature, and research gaps on machine learning algorithms and image processing have highlighted the enhancement of the precision as well as effectiveness of such detection systems with respect to skin diseases. This review is in a position to provide grounds for future research directions and innovations in skin disease diagnostics.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room E Pune, India

12:15pm IST

Contactless Knuckle Biometric System
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Ch. G.M.A.S. Teja, V. Manmohan, Ch. Srinith, N. Shiva Kumar, Vempaty Prashanthi, R Govardhan reddy
Abstract - This review paper focuses on the emerging potential of FKP recognition as a strong modality for identity verification and authentication. Traditional biometric methods are fingerprint and iris recognition methods, which have been adopted more than others because they can be accurate and reliable, but these techniques also bear limitations, such as problem with false negatives, costly equipment, and vulnerability to data breaches. In For the response to these challenges, FKP offers a new approach in that the ability to search for unique patterns of knuckle skin is the stable and more non-invasive indicator. Unlike, which can deteriorate and is vulnerable to influences over time, FKP has little change from outer conditions and, therefore, is an attractive solution for secure and contactless authentication. This review synthesizes the most recent research and technical advancements in the recognition of FKP. Presenting the benefit of how bringing FKP within the multimodal system compiles diverse strengths of various biometric techniques under one framework and provides the enhancement of a holistic method toward security. The discussion continues with research gaps among the existing literature and ends with a call for further investigation. All the above said, the review given is going to validate the potentiality of FKP becoming a feasible, scalable surrogate to the traditional biometric schemes in various datasets, real word applications.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room E Pune, India

12:15pm IST

Evaluation of Substrate Materials for Inset-Fed Rectangular Patch Antenna in the Terahertz Band: Electrical and Mechanical Performance Analysis
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Kiruthika R, Gunavathi N
Abstract - This study focuses on a conventional inset-fed rectangular patch antenna to investigate various substrate materials for terahertz (THz) frequency applications. The performance of different substrates is evaluated based on their electrical parameters. The operating frequency range by IEEE standards falls within the THz band, specifically from 0.1 to 3, with a center frequency of 1.5 THz. Key performance metrics such as return loss (in dB), bandwidth, gain, directivity, and efficiency are assessed, along with bending tolerance to evaluate mechanical stability. Teflon demonstrates superior radiation characteristics, achieving high gain and directivity values obtained through high-frequency structural simulator (HFSS) and computer simulation technology (CST). Additionally, based on statistical analysis, Arlon Diclad 880 provides better mechanical stability than other substrate materials. The equivalent circuit model is analyzed using advanced design system (ADS) software.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room E Pune, India

12:15pm IST

Flying Duck formation Energy Optimization Algorithm (FDEOA): A Swarm-Based Algorithm to Select Cluster Heads in Wireless Sensor Networks
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Agha Imran Husain, Sachin Lakra
Abstract - In this paper, we proposed a novel swarm-based algorithm called the Flying Duck formation Energy Optimization Algorithm (FDEOA) for selecting cluster heads in Wireless Sensor Networks (WSNs). The FDEOA method tries to minimize energy usage in WSNs by lowering the number of messages delivered by the sensor nodes. It is motivated by the formation of a flock of flying ducks. The best sensor node for the cluster head is chosen by the algorithm using a multi-objective fitness function that takes into account both energy usage and network connection. In terms of network longevity, energy usage, and the number of dead nodes, the FDEOA method is contrasted with other well-known clustering algorithms, including LEACH and SEP. Simulation findings reveal that the FDEOA algorithm beats the existing methods in terms of network lifetime and energy usage while retaining a high level of network connectedness. The suggested approach can be used on low-power sensor nodes and is computationally effective. The FDEOA algorithm has the potential to be used in a variety of WSN applications where network longevity and energy usage are important factors.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room E Pune, India

12:15pm IST

Optimal PRN Code Selection for High-Accuracy Navigation in IRNSS L5 Band
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - K. L. Sudha, Kavita Guddad
Abstract - NavIC (Navigation with Indian Constellation) is a satellite system consisting of seven satellites orbiting the Earth in GEO and GSO orbits. This satellite constellation offers Standard Positioning Service (SPS) for common public use and Restricted Service (RS) for approved users, using two frequencies: the L5 band and the S band, with the CDMA technique. This paper examines the suitability of three binary sequences — Gold, Weil, and Weil-like Sidelnikov-Lempel-Cohn-Eastman (WSLCE) sequences — as PRN codes for the primary in phase and quadrature-phase codes of the L5 band of IRNSS. It describes the generation of these sequences and compares them based on their even auto-correlation and cross-correlation values. The randomness of these sequences is evaluated using the NIST (National Institute of Standards and Technology) statistical test suite. A comparison of the three binary PRN sequences, each 10230 bits in length for the L5 frequency band, reveals that WSLCE sequences exhibit greater randomness compared to the other two sequences
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room E Pune, India

12:15pm IST

Remote Hardware Controlling using Android Mobile Application including Cloud Database Integration
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Anupama Nayak, Shikha, Jahanvi Ojha, Kavita Sharma, S.R.N Reddy
Abstract - Living in a world where science is advanced, the presence of technology can be seen in even the smallest of the appliances we use and it has become a crucial part of our lives. Technologies like the Internet of Things (IoT) along with modern wireless systems have led to the creation of smart appliances which have made the lives of people much simpler and more comfortable. Many automation devices in our homes are accessible remotely via a mobile application. This paper focuses on the design and development of an android mobile application, and its connectivity to a cloud database, which is also accessed by the hardware. Using Android Studio, Firebase cloud database, and Raspberry Pi, we successfully developed an android application that controls hardware remotely.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room E Pune, India

12:15pm IST

Uncovering Hidden Risks: A Vulnerability Analysis of Advanced Conversational AI Models
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Vaishnavi Moorthy, Rupen, Dhruv Chopra, Anamika Jain
Abstract - The issue of security is paramount in any organization. Since the advancement of technology and introduction of Generative AI such as ChatGPT, security concerns have skyrocketed for everyone alike. With the availability of these Generative AI platforms to virtually anyone with internet access the threat of security is bigger than ever before as these platforms can be used for malicious intents by a large number of people. Limited research has been performed by third party researchers on Generative AI as it is a relatively new technology. We intend to perform relative research in identifying potential vulnerabilities in Generative AI models, LLMs etc. The aim of this research is to document various ways cybersecurity can be used in GenAI with the intention of both securing assets and protection against malicious activity. The project also delves into potential applications of GenAI in helping identify, prevent and test various security infrastructure. Some potential threats and uses studied under this project include real time access management, phishing detection, jailbreak of ChatGPT. The mentioned use cases provide us with a wide picture of uses of these LLMs in the world of cybersecurity. The deployment of Generative AI systems introduces significant cybersecurity challenges, necessitating the need of safeguards for monitoring against threats and vulnerabilities. This project aims at performing the necessary research to identify such situations from affecting normal operations of an organization and to spread awareness regarding the use of Generative AI in cybersecurity.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room E Pune, India

12:15pm IST

Unleashing the Current Trends and Insights Towards Adoption of Generative AI Chatbots in Higher Education: A Bibliometric Analysis
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Aatm Prakash Rai, Puneet Kumar Gupta, Santanu Roy
Abstract - The proliferation of Generative Artificial Intelligence Chatbots, also known as Gen AI chatbots, are nowadays in the growth phase of integration with information systems for effective teaching and learning. The learning experience has been enhanced with the arrival of Gen AI tools such as machine learning and natural language processing. Gen AI Chatbots like ChatGPT, Copilot, etc. can be considered as computer programs that can trigger human-like interactions to aid investigating, developing and transferring knowledge. The objective of this research work is to scan the previous scholarly research works on Gen AI chatbots adoption by relying on bibliometric analysis. The study intends to contribute by identifying research trends and insights towards adoption of Gen AI chatbots in higher education sector in the Indian context. The outcome of the analysis highlights prospective research opportunity in Gen AI chatbots due to the evolution of the large language models and machine learning. This emerging technology may alter the course of future research in the higher education sector.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room E Pune, India

12:15pm IST

A Comprehensive framework for Real-Time Monitoring of Human-Wildlife Harmony in Mudumalai Forest of Tamil Nadu
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Lakshmi Priya G G, Padma Lakshmi G, Thomas Felix K
Abstract - The conservation of natural habitats and the coexistence of humans and wildlife are vital for biodiversity preservation. In the Mudumalai Forest Conservation region of Tamil Nadu, India, achieving harmony between human activities and wildlife preservation presents a significant challenge. A framework for real-time monitoring of human-wildlife interactions leveraging a comprehensive integration of satellite imagery, Internet of Things (IoT) sensors, and deep learning techniques is discussed in this paper. The proposed system utilizes high-resolution satellite imagery to identify the hotspots, where human-wildlife interactions are most likely to occur or where conflicts are already prevalent. Deep learning algorithms are applied to analyze the satellite imagery data and detect patterns indicative of potential human-wildlife conflict areas. By training and continuously updating the models with real-time information, the system can accurately identify areas of heightened risk. Throughout the identified hotspots, IoT sensor networks are deployed strategically to monitor the real time human activities, wildlife movements, and predict the possibilities of human - wildlife interactions by employing light weight deep learning models. Based on the prediction, real-time alerts and early warnings with location are communicated via message and mobile apps notification to the relevant stakeholders for necessary actions. Moreover, by incorporating feedback loops, the system can adapt and improve its performance over time.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room F Pune, India

12:15pm IST

Adaptive Neural Network Based Error Mitigation scheme for Free Space Optical Communication in Adverse Atmospheric Conditions
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - N. Mangaiyarkarasi, J. Arputha Vijaya Selvi, T. Pasupathi
Abstract - Free Space Optical (FSO) communication systems offersultra high bandwidth, large data rate and very secure data transmission, making them a feasible solution for next generation communication networks. However, performance of the FSO communication system is greatly impacted by adverse atmospheric conditions such as heavy turbulence, rain, and fog, which set up errors and degrade the quality of the signal. In this paper an adaptive neural network-based approach is presented to mitigate errors under different seasons of atmospheric conditions. This method exploits a convolutional neural network (CNN) based architecture to predict and compensate the atmospheric induced distortions, thereby improving the performance of the FSO communication system. It significantly improves the Bit Error Rate (BER) and Signal-to-Noise Ratio (SNR). Real-time atmospheric data such as temperature (T in C), relative humidity (%), atmospheric pressure (Pa), and wind speed (ms-1) are collected and the CNN dynamically changes the parameters to optimize performance. Achieved result shows that the neural network model significantly improves the robustness and reliability of the communication system. This method triggers the way for more resilient FSO networks, which is more crucial for the implementation of 5G/6G and beyond communication infrastructures.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room F Pune, India

12:15pm IST

Challenges and opportunities for data interpretation in augmented analytics with natural language generation models
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Shivani Kania, Yesha Mehta
Abstract - Augmented analytics, managed by machine learning and natural language processing, handles data analysis findings, reducing the time-consuming pre-processing and feature development processes. The article focuses on the importance of Augmented Data Science (ADS), an interactive, data-driven system that combines personal judgement with analysis of statistics to improve decision-making in data interpretation. The challenges are developing the requirements for assessment, developing defined review methods, and comparing suggested methodologies to real-world datasets and use cases. The goal is to create and develop a model for data interpretation and natural language-based generated output in Augmented Analytics, with objectives including data processing, model design, query processing, and component analysis.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room F Pune, India

12:15pm IST

Dictionary Based Approach for Analyzing Marathi Text Data
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Saroj S. Date, Sachin N. Deshmukh, Mahesh B. Shelke, Daivat D. Sawant, Chatrabhuj B. Kadam, Kailas M.Ambhure
Abstract - Analyzing text data in regional languages is essential for uncovering sociocultural insights. However, languages like Marathi face considerable challenges due to the scarcity of computational resources. Over the past few decades, Linguistic Inquiry and Word Count (LIWC) software has become a gold standard for text data analysis. It uses a dictionary-based approach to classify words into predefined psychological and linguistic categories, enabling researchers to explore aspects of personality, behavior, emotions, and social interactions. This research paper introduces the application of MR-LIWC2015 for analyzing Marathi text data. MR-LIWC2015 is a translation of the English LIWC dictionary into the Marathi language. In this paper, by leveraging a dictionary-based approach, MR-LIWC2015 analyzes psychological and emotional dimensions in textual feedback. Using a dataset of 185 student feedback entries, translated from English to Marathi, this research evaluates Marathi LIWC's efficacy by comparing its results with the original English LIWC. Findings indicate a high positive correlation between the two software, demonstrating the reliability of the Marathi LIWC. This advancement not only facilitates feedback analysis in Marathi but also opens avenues for diverse domains like sentiment analysis, mental health analysis, product review analysis, expressive writing analysis, etc. Future work aims to expand the lexicon set of Marathi LIWC and explore cross-domain applications. Furthermore, the software may be adapted for other regional Indian languages, marking MR-LIWC2015 as the first LIWC translation for any Indian language.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room F Pune, India

12:15pm IST

From Data to Decisions: Enhancing Student Retention with Predictive Analytics
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Hardik I. Patel, Dharmendra Patel
Abstract - Higher education institutions' reputation, financing, and student achievement are all impacted by student retention, which has grown to be a major problem. In order to properly identify at-risk students, traditional methods to retention issues frequently lack the predictive capacity and flexibility required. In order to predict student retention rates, this study makes use of machine learning approaches, giving academic leaders useful information. The suggested approach builds a strong prediction model by combining a variety of information, such as financial, behavioral, academic, and demographic factors. The model finds important patterns and trends related to retention outcomes by using sophisticated techniques like gradient boosting and neural networks. A methodical procedure that includes feature selection, data preparation, and model assessment guarantees excellent accuracy and scalability. By employing Explainable AI technologies to make forecasts clear and actionable, the study also highlights the significance of interpretability. This method allows institutions to apply timely interventions, such academic help, counseling, or financial aid changes, by turning raw data into useful forecasts. The results show how predictive analytics has the power to transform retention tactics and promote an inclusive and effective educational system. This study offers a guide for incorporating machine learning into higher education's strategic decision-making process.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room F Pune, India

12:15pm IST

IOT-Based Aerial Vehicle System for Agriculture Application
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Dhanashree Joshi, Nilesh B. Korde, Pratibha Jape, Gayatri Newase, Mitali Gajbhiye, Maitriyee Kadam, Gayatri Gujar
Abstract - Agriculture is a vital sector in many countries, especially in developing economies like India, where it contributes significantly to GDP and supports over half the population. The sector includes a wide range of crops, such as sugarcane, banana, apple, and pomegranate, which are crucial for food security and economic stability. However, the cultivation of these crops presents challenges, particularly in crop management and pesticide application. For instance, sugarcane fields are dense and tall, making it difficult for farmers to access all areas, and banana plantations in states like Tamil Nadu, Kerala, and Andhra Pradesh also have densely packed leaves that complicate manual spraying efforts. Similarly, apple orchards in hilly regions such as Himachal Pradesh and Uttarakhand, and pomegranate fields in Maharashtra and Karnataka, are challenging due to their dense canopies and rugged terrain. These obstacles make it difficult for farmers to spray pesticides safely and efficiently, and the presence of wild animals hiding within these fields adds an additional layer of risk. To address these challenges, advanced technologies like drone-based pesticide spraying systems are proposed. These drones, equipped with GPS, IoT sensors, and deep learning capabilities, can autonomously navigate dense crop fields and varied terrains. By targeting specific farm coordinates and adjusting their spraying patterns based on real-time environmental data, the drones ensure precise and even pesticide application. This technology not only enhances the safety of farmers by minimizing their exposure to hazardous environments but also improves the efficiency and effectiveness of pest control. As a result, the adoption of drone technology can reduce labour costs, increase crop yields, and promote sustainable farming practices in regions where traditional methods are inadequate and can improve efficiency of farming and reduce human load. Additionally, by taking drone in consideration while spraying pesticides in farming can improve overall efficiency.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room F Pune, India

12:15pm IST

Road Guard: Comprehensive Deep Learning-Based Detection of Potholes, Speed Breakers, and Drain Holes
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Shoib Ahmed Shourav, Shahariar Sarkar, Salekul Islam
Abstract - For developing countries, maintaining road network infrastructure is an essential concern. To strengthen a nation’s economy, road infrastructure must be maintained effectively. Potholes, speed breakers, and drain holes in roads are major reasons for causing accidents, traffic jams, and car damage. In addition to improving driving safety and minimizing vehicle damage and accidents, identifying road anomalies like potholes, speed breakers, and drain holes is essential for enabling authorities to effectively manage road maintenance. Self-driving cars need to be able to handle different road conditions. In this research, a custom dataset comprising potholes, drain holes, and speed breakers was developed. The study employs YOLOv11, a cutting-edge deep learning-based object detection model, to accurately detect these anomalies, including a comparison of road anomaly detection performance under daytime and nighttime conditions. The proposed approach achieved an accuracy of 83.8% on the daytime dataset, 81.6% on the nighttime dataset, and 84.4% on the combined dataset.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room F Pune, India

12:15pm IST

Semi-Supervised Learning Approaches for Imbalanced Multi-Class Classification in Real-Life Applications: A Comprehensive Survey
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Madhusmita Mishra, R. Kanagavalli
Abstract - This comprehensive survey examines advancements in semi-supervised learning (SSL) techniques developed to address imbalanced multi-class classification problems across a variety of real-world applications, including healthcare, fraud detection, and industrial monitoring. Traditional machine learning models often struggle with highly skewed data distributions, leading to biased predictions that favour majority classes while overlooking minority classes. SSL, which leverages both labelled and unlabelled data, has emerged as a promising approach, reducing the need for extensive labelled datasets while improving model generalization for minority classes. This review focuses on methodologies such as re-sampling, cost-sensitive learning, ensemble learning, hybrid techniques, active learning, and evolutionary algorithms, each offering unique approaches to mitigate the impact of class imbalance. Re-sampling methods, such as SMOTE (Synthetic Minority Over-sampling Technique) and its variants, augment minority classes by creating synthetic samples, addressing imbalances within SSL frameworks. Costsensitive learning introduces penalties for misclassifications, improving sensitivity to minority classes, while ensemble learning methods, like bagging and boosting, combine multiple classifiers to enhance predictive accuracy in multi-class settings. Additionally, hybrid techniques that integrate re-sampling with cost-sensitive approaches show promise in balancing class representation and boosting model robustness. Active learning, which iteratively selects the most informative samples, and meta-learning, which enables models to adapt dynamically to different class distributions, provide further innovation in tackling imbalances in SSL applications.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room F Pune, India

12:15pm IST

SKIPNet: Spatial Attention Skip Connections for Enhanced Brain Tumor Classification
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Khush Mendiratta, Shweta Singh, Pratik Chattopadhyay
Abstract - Early detection of brain tumors through magnetic resonance imaging (MRI) is essential for timely treatment, yet access to diagnostic facilities remains limited in remote areas. Gliomas, the most common primary brain tumors, arise from the carcinogenesis of glial cells in the brain and spinal cord, with glioblastoma patients having a median survival time of less than 14 months. MRI serves as a non-invasive and effective method for tumor detection, but manual segmentation of brain MRI scans has traditionally been a labour-intensive task for neuroradiologists. Recent advancements in computer-aided design (CAD), machine learning (ML), and deep learning (DL) offer promising solutions for automating this process. This study proposes an automated deep learning model for brain tumor detection and classification using MRI data. The model, incorporating spatial attention, achieved 96.90% accuracy, enhancing the aggregation of contextual information for better pattern recognition. Experimental results demonstrate that the proposed approach outperforms baseline models, highlighting its robustness and potential for advancing automated MRI-based brain tumor analysis.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room F Pune, India

12:15pm IST

Smart Energy Meter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Authors - Ninaad Nagaraj Yeligar, Rajesh Prakash Unakal, Soumya H Hooli, Prerana Girish Karoli, Kiran M R, Suneeta V Budihal
Abstract - The work aims to address the need for efficient energy management by implementing a smart energy meter using LTE technology. We are developing a smart energy meter system capable of accurately measuring and transmitting energy usage data. This work features easy hardware implementation using a ESP32, a cost-effective microcontroller and a LTE module to establish a connection for transmitting data. The system utilizes LTE technology to ensure reliable and long-range communication. Energy consumption data is then monitored and the captured which could be used in future for different purposes. This work provides a comprehensive and user-friendly solution for continuous monitoring and management of energy consumption. The LTE system’s ability to capture real-time energy usage data enhances accessibility and facilitates data-driven decision-making. This innovative solution is ideal for applications where efficient energy management is crucial, such as in residential, commercial, and industrial settings.
Paper Presenter
Thursday January 30, 2025 12:15pm - 2:15pm IST
Virtual Room F Pune, India

2:00pm IST

Session Chair Remarks
Thursday January 30, 2025 2:00pm - 2:05pm IST
Invited Guest/Session Chair
avatar for Dr. Ruchi Nanda

Dr. Ruchi Nanda

Associate Professor & Head, Department of CS & IT, IIS (Deemed to be University), Jaipur, India.
Thursday January 30, 2025 2:00pm - 2:05pm IST
Virtual Room A Pune, India

2:00pm IST

Session Chair Remarks
Thursday January 30, 2025 2:00pm - 2:05pm IST
Invited Guest/Session Chair
avatar for Dr. Nimisha Patel

Dr. Nimisha Patel

Professor, HoD-CE/CSE/ICT, nSAL Institute of Technology and Engineering Research, SAL Education, Ahmedabad, India
Thursday January 30, 2025 2:00pm - 2:05pm IST
Virtual Room B Pune, India

2:00pm IST

Session Chair Remarks
Thursday January 30, 2025 2:00pm - 2:05pm IST
Invited Guest/Session Chair
avatar for Dr. Uma Maheswari

Dr. Uma Maheswari

Assistant Professor, Jaipur Engineering College & Research Centre, Jaipur, India
Thursday January 30, 2025 2:00pm - 2:05pm IST
Virtual Room C Pune, India

2:00pm IST

Session Chair Remarks
Thursday January 30, 2025 2:00pm - 2:05pm IST
Invited Guest/Session Chair
avatar for Dr. Rajan Patel

Dr. Rajan Patel

Principal, Kalol Institute of Technology & Research (KITRC), KIRC Campus
Thursday January 30, 2025 2:00pm - 2:05pm IST
Virtual Room D Pune, India

2:00pm IST

Session Chair Remarks
Thursday January 30, 2025 2:00pm - 2:05pm IST
Invited Guest/Session Chair
avatar for Dr. Disha S. Wankhede

Dr. Disha S. Wankhede

Assistant Professor, Vishwakarma Institute of Information Technology, Pune, India
avatar for Dr. Jitendra Bhatia

Dr. Jitendra Bhatia

Associate Professor, Nirma University, Ahmedabad, India.
Thursday January 30, 2025 2:00pm - 2:05pm IST
Virtual Room E Pune, India

2:00pm IST

Session Chair Remarks
Thursday January 30, 2025 2:00pm - 2:05pm IST
Invited Guest/Session Chair
avatar for Dr. PRABU P

Dr. PRABU P

Assistant Professor, Christ University (Deemed to be University), Bangalore, India.
Thursday January 30, 2025 2:00pm - 2:05pm IST
Virtual Room F Pune, India

2:05pm IST

Closing Remarks
Thursday January 30, 2025 2:05pm - 2:15pm IST
Moderator
Thursday January 30, 2025 2:05pm - 2:15pm IST
Virtual Room A Pune, India

2:05pm IST

Closing Remarks
Thursday January 30, 2025 2:05pm - 2:15pm IST
Moderator
Thursday January 30, 2025 2:05pm - 2:15pm IST
Virtual Room B Pune, India

2:05pm IST

Closing Remarks
Thursday January 30, 2025 2:05pm - 2:15pm IST
Moderator
Thursday January 30, 2025 2:05pm - 2:15pm IST
Virtual Room C Pune, India

2:05pm IST

Closing Remarks
Thursday January 30, 2025 2:05pm - 2:15pm IST
Moderator
Thursday January 30, 2025 2:05pm - 2:15pm IST
Virtual Room D Pune, India

2:05pm IST

Closing Remarks
Thursday January 30, 2025 2:05pm - 2:15pm IST
Moderator
Thursday January 30, 2025 2:05pm - 2:15pm IST
Virtual Room E Pune, India

2:05pm IST

Closing Remarks
Thursday January 30, 2025 2:05pm - 2:15pm IST
Moderator
Thursday January 30, 2025 2:05pm - 2:15pm IST
Virtual Room F Pune, India

3:00pm IST

Opening Remarks
Thursday January 30, 2025 3:00pm - 3:05pm IST
Moderator
Thursday January 30, 2025 3:00pm - 3:05pm IST
Virtual Room A Pune, India

3:00pm IST

Opening Remarks
Thursday January 30, 2025 3:00pm - 3:05pm IST
Moderator
Thursday January 30, 2025 3:00pm - 3:05pm IST
Virtual Room B Pune, India

3:00pm IST

Opening Remarks
Thursday January 30, 2025 3:00pm - 3:05pm IST
Moderator
Thursday January 30, 2025 3:00pm - 3:05pm IST
Virtual Room C Pune, India

3:00pm IST

Opening Remarks
Thursday January 30, 2025 3:00pm - 3:05pm IST
Moderator
Thursday January 30, 2025 3:00pm - 3:05pm IST
Virtual Room D Pune, India

3:00pm IST

Opening Remarks
Thursday January 30, 2025 3:00pm - 3:05pm IST
Moderator
Thursday January 30, 2025 3:00pm - 3:05pm IST
Virtual Room E Pune, India

3:00pm IST

Opening Remarks
Thursday January 30, 2025 3:00pm - 3:05pm IST
Moderator
Thursday January 30, 2025 3:00pm - 3:05pm IST
Virtual Room F Pune, India

3:00pm IST

Deepfake Detection Using ResneXt Deep Learning Architecture
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Ravi Tene, Dasari Kalyani, N. Sudhakar Yadav, Kondabala Renuka, Gunupudi Rajesh Kumar, Nimmala Mangathayaru
Abstract - A deepfake is a misleading video or image that looks genuine. GANs (Generative Adversarial Networks) are the known name in the domain of machine learning. GANs generate a huge amount of fake human writing with deep-learning-wide-models. The generator model learns to sample points from a latent space so that new samples of the same distribution can be fed in and produce different observable model outputs. Deepfakes for most applications can be convincingly created using Generative Adversarial Networks (GANs). There are fears on the Internet related to deepfake. However, the authors use ResneXt and LSTMs for using Deep Learning Network to identify fake areas of deepfake uses Python facial recognition and C++ visual libraries to identify a face in this video. Fake videos are further validated using models trained on various edge groups.
Paper Presenter
avatar for Ravi Tene
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room A Pune, India

3:00pm IST

Integrating Advanced Technologies in India's Electric Vehicle Market: Challenges and Strategic Solutions
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Vanishree Pabalkar, Rahul Dhaigude
Abstract - The purpose to carry out the research study is to understand the concepts of introducing advanced technology that prevails in EV market and the challenges and strategic solutions. This research validates customer feedback and allows companies to get closer to the true opinion of potential Indian customers. In addition, this can eliminate misunderstandings and problems to trade better. This study was conducted to understand the factors that influence the choice of an electric vehicle. The current research has been conducted to study the purchasing behavior of consumers when purchasing electric cars by identifying the importance ratings assigned to different factors during the selection process. in the electric car, and analyze the reasons for the brand's success by identifying the levels of excellence. Current users use different types. Characteristics and identification of the gap between importance ratings and current ratings.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room A Pune, India

3:00pm IST

IoT-Based Smart Cattle Health Monitoring System Using Machine Learning for Real-Time Anomaly Detection
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Vatsal Suchak, Harmin Rana, Ayush Verma, Nilesh Dubey, Hardikkumar Jayswal, Dipika Damodar, Chirag Patel
Abstract - Cattle farming plays a crucial role in global food production, but monitoring the health of large herds poses significant challenges. Traditional manual inspections are inefficient, reactive, and prone to error, highlighting the need for scalable, automated health monitoring systems. This paper introduces a smart cattle health monitoring system that utilize the Internet of Things technology and machine learning algorithms to provide real-time health tracking. The system used a proposed wearable devices equipped with ESP32 microcontrollers and sensors to monitor cattle’s vital parameters, such as body temperature and heart rate. Data collected from the devices is transmitted to a local XAMPP server and analysed by an edge-computing device, Jetson Nano, which processes the data using supervised and unsupervised machine learning models for anomaly detection. If health anomalies are detected, the system sends real-time alerts to farmers, allowing for timely intervention. The system’s design focuses on local processing for low-latency performance, scalability for large herds, and robust security measures. This project demonstrates the potential of IoT-based livestock health monitoring systems to enhance productivity, improve animal welfare, and reduce economic losses due to illness.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room A Pune, India

3:00pm IST

Liver Disease Detection Using AI-ML
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Teena Bambal, Dipesh Chavan, Nikhil Gadiwadd, Deepak M. Shinde
Abstract - This survey paper investigates the application of artificial intelligence (AI) and machine learning (ML) techniques for the early detection and diagnosis of liver disease. Traditional methods of liver disease diagnosis, such as blood tests and imaging techniques, can be time-consuming and prone to human error. AI-based approaches offer the potential to improve accuracy, efficiency, and accessibility of liver disease diagnosis. The research investigates a range of AI and ML algorithms, such as decision trees, support vector machines, random forests, neural networks, and deep learning models. These algorithms are applied to analyze large datasets containing patient information and medical test results. The performance of the models is evaluated using metrics such as F1-score, precision, accuracy, recall, and AUC. The findings demonstrate the effectiveness of AI-based approaches in accurately detecting liver disease. Compared to traditional methods, AI models can provide more reliable and timely diagnoses, leading to improved patient outcomes. The research highlights the potential of AI to revolutionize the field of liver disease management and improve global healthcare.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room A Pune, India

3:00pm IST

Mahavitaran Help App: A Comprehensive Mobile Application for Reporting Electrical Problems Using Cloud and Location-Based Services
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Yatin Nargotra, Tanya Jagavkar, Tushar Birajdar, L.P.Patil
Abstract - The Mahavitaran Help App is a mobile application aimed at revolutionizing the process of reporting electrical outages in India. Current systems for outage reporting are often slow, inefficient, and lack the integration needed to quickly address user complaints. The Mahavitaran Help App simplifies this process by allowing users to submit complaints via mobile devices, integrating location services with Google Maps and supporting the upload of complaint-relevant images. Moreover, this project introduces a critical migration from Firebase to AWS or Google Cloud, offering improved scalability, reliability, and faster processing of complaints. This paper presents a detailed review of existing mobile complaint management systems and explores cloud-based scalability and security features, including OTP authentication for securing user data.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room A Pune, India

3:00pm IST

Methodologies for Visual Memes Content Analysis
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Rani S. Lande, Amol P. Bhagat, Priti A. Khodke
Abstract - Visual memes have become a pervasive form of communication in digital spaces, presenting a unique challenge and opportunity for content analysis due to their blend of visual, textual, and often humorous elements. This paper reviews and synthesizes methodologies employed in the analysis of visual memes, aiming to provide a comprehensive overview of current practices and future directions. The methodologies discussed encompass a range of approaches, including qualitative, quantitative, and mixed-methods strategies. Qualitative methods delve into semiotic analysis, exploring how visual and textual components interact to convey meaning and cultural references. Quantitative approaches employ computational tools to analyze large datasets, focusing on metrics such as image recognition, sentiment analysis, and virality metrics. Mixed-methods studies combine these approaches to offer nuanced insights into the multifaceted nature of visual memes. Challenges in visual memes content analysis include the rapid evolution of meme formats, cultural context sensitivity, and the ethical implications of meme reuse and modification. Additionally, the paper explores emerging trends such as deep learning techniques for image recognition and natural language processing for text analysis within memes. By synthesizing these methodologies, this paper aims to provide researchers and practitioners with a foundational understanding of how to effectively analyze visual memes, highlighting opportunities for interdisciplinary research and applications in fields ranging from communication studies to digital humanities and beyond.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room A Pune, India

3:00pm IST

Predictive Analysis of Apple Stock Market Trends: A Data-Driven Approach
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Nitika Sharma, Rohan Patel, Hardikkumar Jayswal, Nilesh Dubey, Hasti Vakani, Mithil Mistry, Dipika Damodar, Shital Sharma
Abstract - This study explores the use of advanced machine learning models to forecast trends in Apple’s stock market performance. Stock market forecasting presents a formidable challenge, given the inherent volatility and unpredictability of market behavior. The study investigates various advanced models, such as Logistic Regression, XGBoost, Artificial Neural Networks, Recurrent Neural Networks, Long Short-Term Memory (LSTM), and ARIMA, for predicting stock prices. Analyzing historical data spanning from 2014 to 2024, which includes Apple's daily stock prices and trading volume metrics, the research applies Grid Search optimization to fine-tune model parameters, thus enhancing predictive accuracy. The findings reveal that LSTM achieved the highest accuracy at 96.50%, followed closely by ARIMA at 90.91%. These results highlight the critical role of machine learning in improving stock price predictions, thereby facilitating more informed investment decisions.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room A Pune, India

3:00pm IST

Secured Data Sharing using Blockchain
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Anant Nikam, Atharva Gangapure, Samarth Deshpande, Sonali Shinkar
Abstract - Among the primary concerns in the digital era, secure sharing of data stands prominent. The integrity, confidence, and authenticity of information being shared form a very significant concern. Blockchain technology promises much towards overcoming such challenges due to its decentralized and immutable nature. It significantly enhances data security through its use of encryption techniques, such as hashing and digital signatures, which eliminate the need for middlemen in transactions while also reducing the probability of data breaches. It leverages smart contracts that provide mechanisms for automating access controls wherein data is shared appropriately, according to agreed terms, among the participants in a trustless environment. Some practical illustrations of use cases from healthcare, supply chain management, and finance are found in the context provided below. Findings thus reveal the needed innovation to revolutionize the state of secure data sharing on blockchain technology by providing a strengthened, decentralized infrastructure that promotes trust, transparency, and accountability of stakeholders involved.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room A Pune, India

3:00pm IST

The Smart Trends of Using Healthcare Apps to Engage Customers
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Dibyendu Rath, Arunangshu Giri, Dipanwita Chakrabarty, Puja Tiwari, Satakshi Chatterjee, Shamba Chatterjee
Abstract - The study reveals how mobile health apps and information technology can take a pivotal role for healthcare improvement, especially for rural population, where people suffer from medical infrastructural inadequacy. Healthcare apps facilitate the users by providing a 24x7 accessibility at a cost-effective rate. The study used cross-sectional surveys for analyzing responses across different demographic profile, like age, gender, qualification, income group etc. This study has identified some key factors that help to engage customers with healthcare apps. The study also reveals that trust on healthcare app will enhance intention to adopt healthcare apps and trust will be positively influenced by Perceived Benefits (PB). Again, trust will be negatively induced by Perceived Risks (PR) and Technology Anxiety (TA). Four hypotheses were made to validate the relationships among the factors. Finally, the study balances the benefits and risks of using healthcare apps and guides how m-health technology can increase adoption intentions.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room A Pune, India

3:00pm IST

Video Activity Detection using Deep Learning
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Vasu Agrawal, Nupur Chaudhari, Tanisha Bharadiya, Manisha Sagade
Abstract - The recent progress in AI and deep learning has significantly transformed the public safety landscape, particularly in the area of real-time threat detection in public domains. This comes with increased complexity and density as urban environments become more complex; traditional surveillance systems are no longer enough for monitoring large crowds, detecting potential threats, or ensuring public safety. This has necessitated the development of automated systems that could process large volumes in real time to pick anomalies, suspicious behaviors, and objects liable to imperil security. We delve into the core methodologies that object detection models, such as YOLO, Faster R-CNN, SSD, and compare them .To further improve the accuracy in detection of anomalous and illegal activities and reduce false positives and negatives we created a custom dataset by fusing data from different sources, these systems enhance the overall reliability of the surveillance systems.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room A Pune, India

3:00pm IST

Analysis Of Customer Reviews On Food Delivery Platforms Using Deep Learning
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Aditya Bhabal, Aditi Bharimalla, Shruti Balankhe, Vaibhav Chavan, Vaibhav Narawade
Abstract - The overnight growth of OFD service businesses, due to technological advancement and a change in consumer behavior, has made reviews furnished by customers imperative for improvement in service quality, demand forecasting, and customer satisfaction. The vast amount of unstructured data makes the conventional method too ineffective. The following review thus provides valuable insights from diverse studies that are being done to apply deep learning, reinforcement learning, and ensemble learning in analyzing customer reviews of food delivery platforms. It goes on to provide ways through sentiment analysis, demand forecasting, dynamic recommendations of orders, and personalized marketing that these studies have proven how machine learning can make a difference in operatively effectively producing efficiency. It also provides an overview of the challenges in terms of data imbalance, scalability, and sustainability concerns, thus showing perspectives for further research in developing OFD platforms' capabilities for optimized and personalized services that take into account environmental and social impacts.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room B Pune, India

3:00pm IST

Black and White Image Colorization with OpenCV and Deep Learning
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Manav Bagthaliya, Madhav Desai, Priyanka Patel
Abstract - This paper introduces a black and white image colorization model based on deep learning techniques and OpenCV by combining available open-source toolkits. The model uses the Lab color space, in which a single grayscale image (the luminance or L channel) that is processed by a convolutional neural network (CNN) predicts chromatic values (a and b channels). The colorized version of the image is, therefore, generated by merging it with the original L channel. This approach makes use of deep learning in order to enhance the quality and performance of the reconstructed images. Experimental results verify that the proposed method is both valid and flexible and, hence, can vividly restore color from monochrome photographs in such domains as historical photo restoration and artistic creation.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room B Pune, India

3:00pm IST

Decoding Customer Sentiment: A BERTopic Approach to Review Analysis of Heritage Tourism
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Kavisruthi K, Anet Reji, Adhrushta V, Sangeetha Gunasekar
Abstract - The aim of the study is to identify and analyze the factors that contribute to positive emotions expressed by visitors about the Taj Mahal based on user-generated content, a top destination for tourists in India. The reviews were scrapped from Trip Advisor.com, from which 77 themes have been identified using BERTopic modeling, reflecting both positive and negative aspects of the visitor experience. These 77 themes were grouped into 9 broader themes like value for money, history, time of visit, architecture, location, facility, tours, memories, and crowd which influence the positive and negative emotions of the visitors to heritage sites. Logistic regression is used to study the impact of the variables on customer satisfaction. Except for the variables location and memories, all the other variables significantly affect whether visitors give a star rating or not. These insights are valuable to tourist destination managers in helping them better strategies for enhancing tourist satisfaction.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room B Pune, India

3:00pm IST

Healthcare: An Ever-Evolving Field Embracing Change and Practical Application
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Pushpavati V. Kanaje, Pratik Raj Dahal, Shivam Anant Ghorpade, Aditya Jaikumar Sharma, Atharva Dinesh Phatak, Parth Ravindra Sawant
Abstract - In this paper, we strive to explore the various technologies that can be implemented to streamline the process of getting medication, and to make this a patient-centric experience. Exploring all the various solutions and methods that we can implement. Furthermore, these advancements will be beneficial as it will strive to remove the discrepancies that persist in the conventional methods. The advent of all the new technologies open up numerous doors for the medical sector to improve substantially and to make advances beyond what the existing models can do. Incorporation of various techniques like telemedicine, digital health records, and mobile health apps have all been put into test in the past few years. But, with the surge of new methods and techniques these existing techniques can further be enhanced and be made to work a lot more efficiently both figuratively and literally.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room B Pune, India

3:00pm IST

IoT Enabled Semantic Context-Aware Service Composition for Active Spaces
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Anil R. Surve, Vijay R. Ghorpade, Ganesh S. Sagade
Abstract - Service-Oriented Architecture (SOA) consists of autonomous, standardized, and self-describing components known as services that communicate with each other and are provisioned as per the rules decided. This architecture is being widely used worldwide. SOA is verified for dynamic, automatic, and self-configuring distributed systems such as in automation systems. This paper explores SOA paradigm for active spaces in an IoT environment with devices realized by device profile for web service (DPWS) wherein the context information is acquired, processed, and submitted to a composition engine so as to provision relevant services which suit to the context. Identification of profiled users in the context is achieved using RFID tags accordingly composition plans are created for every user. A six-phased composition process is employed to complete this task. DPWSim simulator is deployed for illustration and testing the system. WS4D explorer is used to scan the available devices in the network. IoT platform is employed for providing context information notifications comprising of various sensors and IoT environments.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room B Pune, India

3:00pm IST

Learning Metamorphoses: Revolutionising Education with Technology Integration and Customisation in Education 5.0
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Nilay Vaidya, Kamini Solanki, Krishna Kant, Jay Panchal
Abstract - With the seamless integration of state-of-the-art technology and customization of learning experiences, Education 5.0 signifies a transformative shift in the educational landscape. This paradigm leverages big data analytics, virtual reality, and artificial intelligence (AI) to personalize learning paths and facilitate the development of critical 21st-century skills. Unlike outdated one-size-fits-all methods, Education 5.0 emphasizes personalized learning, enhancing student engagement and productivity. This transformation calls for collaborative efforts between educators, students, and industry stakeholders, ensuring education is relevant and future-ready. This chapter highlights the potential of Education 5.0 to foster flexible, inclusive, and progressive learning environments. Blended learning models, which combine AI technology with traditional teaching methods, demonstrate how tailored learning paths can improve outcomes. AI-based solutions offer engaging learning experiences through gamification, virtual reality, and simulations while streamlining administrative tasks like attendance and grading. By providing data-driven insights, AI helps teachers identify growth areas and adjust strategies in real-time to improve student success. This approach enhances accessibility, offering diverse learning methods, and ensuring tools are available for students with impairments. Scalable and adaptable, blended learning encourages lifelong learning, collaborative skills development, and peer interaction through AI powered platforms.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room B Pune, India

3:00pm IST

NewsFlow: Leveraging AI and Machine Learning for Multilingual News Classification and Sentiment Analysis to Support Feedback and Insights on News in Regional Media
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Jay Madane, Aniket Jaiswal, Sejal Balkhande, Kirti Deshpande
Abstract - Using machine learning (ML) and artificial intelligence (AI) methods, this article suggests creating a 360-degree feedback software for the Indian government. The system’s objective is to automatically analyze and classify news articles from local media sources according to department and sentiment. Press Information Bureau (PIB) officials will receive real-time notifications concerning unfavorable stories, and stakeholders will be able to efficiently visualize and filter news coverage thanks to an intuitive dashboard. With the use of this program, the Indian government would be able to make more informed decisions by increasing the effectiveness of media monitoring.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room B Pune, India

3:00pm IST

Securing IoMT: Intrusion Detection to Improve Medical Data Security on Internet of Medical Things using Machine Learning Techniques
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - B Shilpa, Shaik Abdul Nabi, G Sudha Reddy, Puranam Revanth Kumar, Thayyaba Khatoon Mohammad
Abstract - The Internet of Medical Things (IoMT) has made it possible for digital devices to collect, infer, and disseminate data related to health through the use of cloud computing. Securing data for use in health care has unique challenges. Various studies have been carried out with the aim of securing healthcare data. The best way to protect sensitive data is to encrypt them so that no one can decipher them. Conventional encryption methods are inapplicable to e-health data due to capacity, redundancy, and data size restrictions, particularly when patient data is transmitted across unsecured channels. Due to the inherent dangers of data loss and confidentiality breaches associated with data, patients may no longer be able to fully protect the privacy of their data contents. These security threats have been recognized by researchers, who have then proposed various methods of data encryption to fix the problem. As a result, the area of computer security is deeply concerned with finding solutions to the security and privacy issues associated with IoMT. This research presents an intrusion detection system (IDS) for IoMT that utilizes the machine learning techniques: Decision Tree (DT), Naive bayes (NB), and K-Nearest Neighbor (KNN). Feature scaling using the minimum-maximum (min-max) normalization method was performed on the CIC IoMT 2024 dataset to prevent information leakage into the test data. The effectiveness of the output was then evaluated, ensuring that the scaling process was correctly implemented as the initial step of this work approach. Five types of assaults are identified in this dataset: DDoS, DoS, RECON, MQTT, and Spoofing. Principal Component Analysis (PCA) was used to reduce dimensionality in the subsequent stage. The suggested methods have a high detection rate of accuracy 98.2%, specificity of 97.6%, recall of 98.0%, and f1- score of 97.8%, which offer a viable option for protecting IoMT devices from attacks.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room B Pune, India

3:00pm IST

Smart Surveillance for Classroom Management
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Vinay Nawandar, Sakshi Rokade, Nitin B. Patil
Abstract - This paper reviews recent progress in Smart Surveillance Systems, with an emphasis on their roles in crowd control, crime prevention, and behavior monitoring in educational and workplace environments. The adoption of technologies like Machine Learning and Artificial Intelligence, particularly deep learning models such as YOLO (You Only Look Once), has greatly improved the ability to detect and evaluate events in real time. The paper also explores the drawbacks of traditional surveillance systems, including human error and inefficiencies, and how modern AI-driven solutions are addressing these issues. In addition, it discusses key approaches in face detection, behavior analysis, and anomaly detection, along with a comparative evaluation of various algorithms in intelligent surveillance. It also highlights potential energy-saving strategies and future developments in AI-driven surveillance.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room B Pune, India

3:00pm IST

Women Led Startup & Success Story---Case of Zucate
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Pradnya Vishwas Chitrao, Pravin Kumar Bhoyar, Rajiv Divekar
Abstract - Background Significant advancements in technology, economy, and society have ushered in the twenty-first century. Advances in IT will significantly expand opportunities in energy, commerce, production, transportation, education, and health. New learning opportunities have multiplied due to advancements in information and communication technology. Numerous educational institutions, including colleges, universities, and organizations, employ the notion of online learning to augment students' knowledge, skills, and capacities in an efficient and participatory way. Through its ability to help students grasp fundamental ideas in topics like physics, arithmetic, and reading, technology can broaden the scope of what they learn. Use of Technology by Businesses to Enhance Education in India Given that both parents work these days, there is a great demand for resources that will meet the needs of the student body as well as the parents. A lot of computer and internet technology has become available in the twenty-first century, and this technology is being employed in the creation of educational resources for students. Zucate is one such business that was started by Dr. Moitreyee Goswami and Ms. Roli Pandey. Objective The company Zucate, which aims to establish “a teacher-learner-parent ecosystem with learner at the center,” is the subject of this paper (https://www.f6s.com/zucate). The aim of this study is to investigate how two women entrepreneurs attempted to help students, particularly school-age children, learn independently beyond school hours by utilizing technology in an economical way. It also aims to study how they survived the pandemic and expanded the enterprise Research Methodology The principal research method employed by the researchers involved conducting in-person interviews with the enterprise's founders. In addition, they made reference to papers from reputed data bases and other secondary sources. Importance of the Research The research is significant because it sheds light on how technical knowledge can be utilized to develop pedagogical and learning materials that help kids and learners understand ideas and learn on their own without having to rely on pricey, time-consuming tuition or even tech-savvy, expensive learning resources provided by traditional businesses. It is also important because it helps us understand how women entrepreneurs strategize and use technology to expand and flourish their enterprises.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room B Pune, India

3:00pm IST

A Survey on Fire Alarm Systems: Technological Advancements and Challenges
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Karthika M, Raghu Nandan KS, Salanke Anni Rao, Ramkumar S
Abstract - Fire detection and prevention are essential to preventing fire spread and substantial loss or damage, especially in remote regions like lakes where traditional approaches are almost useless. This review covers fire alarm system advances, focusing on machine learning (ML) to improve detection. The paper also examines how these innovations improve remote fire alarm systems functioning and how they integrate with emerging IoT protocols, sensor networks, and radio technologies like LoRaWAN. It focuses on ML models like CNNs and deep learning to analyse sensor data and detect fires accurately and quickly. This paper discusses fire detection innovation and how ML can improve future systems' coverage and accuracy.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room C Pune, India

3:00pm IST

AWS Pricing Structure and Cost Management: A Basic Comparison with Azure
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Vishal Karpe, Meetu Kandpal
Abstract - In order to better understand how Amazon Web Services (AWS) charges for its services and how users can efficiently manage expenses, this research examines the price choices offered by AWS. It illustrates how various pricing strategies such as pay- as-you-go, volume discounts, and cost savings affect users' AWS spending by explaining them and providing examples of how they work. To emphasize the advantages and disadvantages of each, the research also contrasts the costs of Microsoft Azure and Amazon Web Services. In order to track and optimize user spending, it also comes with features like AWS Budgets, AWS Pricing Calculator, and AWS Trusted Advisor. The goal is to provide clients with an extensive manual on how to maximize their financial and material investments when utilizing cloud services.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room C Pune, India

3:00pm IST

Carbon footprint tracking using AI
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Ritveek Rana, Manisha Manoj, Anitha Dhanasekaran
Abstract - The escalating threat of climate change has made it imperative to understand and mitigate the environmental impact of human activities, particularly by reducing carbon footprints. This research ventures on predicting carbon emissions for India using autoregressive integrated moving average (ARIMA) models. The findings may signal appreciable implications for decisions in governmental policies and energy sector. This study highlights a potential situation for India in the coming years due to increased expenditure of carbon-based fuel sources to meet the need for increased manufacturing and demand. The ARIMA models developed in this research can serve as a valuable tool for forecasting carbon emissions and guiding future energy policies.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room C Pune, India

3:00pm IST

Enhanced Deep Learning Model For Character Recognition Of Regional Language
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Amoggha C H, Padmapriya R, Adithya Narayana Holla, Manoj C Aradhya
Abstract - This paper presents the development of a deep learning model for recognizing handwritten Kannada characters. Kannada character recognition presents unique challenges due to the complexity of the script and the variety of symbols. To address these, we utilize a hybrid model combining ResNet50 and VGG16 architectures. ResNet50 is leveraged for its ability to train deep networks on complex patterns, while VGG16 excels in capturing detailed feature representations. The model is trained on carefully pre-processed datasets, optimized through iterative parameter tuning to ensure high accuracy and robustness. The backend infrastructure uses Flask and TensorFlow, with the frontend built using java script, HTML, and CSS. The system features a sketchpad where users can draw Kannada characters, which are then processed by the deep learning model for recognition. An interactive tool further supports language learning. Through extensive testing, the system has proven to be reliable and effective. This project represents a significant advancement in automated Kannada language processing, offering a powerful tool for character recognition. By enabling accurate, efficient recognition, it contributes to promoting linguistic diversity and inclusivity, making it an invaluable resource for Kannada language processing applications.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room C Pune, India

3:00pm IST

Fortifying the Future: Cybersecurity Strategies for Critical Energy Infrastructure
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Edidiong Akpabio, Sudhir Agarmore, Akshay Kumar
Abstract - As digital technologies become increasingly ingrained in critical energy infrastructure, a looming threat is cyberattacked as the sector has absorbed all the data acquisition and supervisory control systems, smart grids, and industrial control systems, with associated operational efficiencies, but at the cost of an expanded attack surface in terms of cyber threats. This paper aims at identifying the unique cybersecurity issues in CEI that pose threat scenarios that include, for instance, their vulnerability to legacy system vulnerabilities, insider threats, and more complex attack vectors such as advanced persistent threats and ransomware. Finally, it points out the need for proactive risk assessment, network segmentation, advanced defence mechanisms such as intrusion prevention and detection systems, and zero trust architectures. Newer technologies like machine learning, blockchain, artificial intelligence, and quantum cryptography offer new opportunities for better cybersecurity. It can foresee the occurrence of a particular attack through AI-based threat detection systems. Blockchain provides security in energy transactions while making unbreakable encryption of critical communications. This paper insists on better, much more comprehensive disaster recovery and incident response plans to minimize the impacts caused by cyberattacks and it concludes by advocating a multi-layered cybersecurity strategy with the intent of integrating advanced detection systems and risk management practices into a solid collaboration between the government and private sectors aimed at enhancing the stability of global energy supplies.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room C Pune, India

3:00pm IST

Harnessing Machine Learning for Egg Shell Powder Shampoo Formulation in Hair Health
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Archana L. Rane, Sanskruti R. Talele, Rashika A. Ghavate, AditiS.Khairnar, Harisha A. Chothani
Abstract - Nowadays, the world is increasingly focused on health care, with hair care emerging as a key aspect of personal well-being. Many people face confusion when selecting the best shampoo based on their scalp and hair health. The purpose of this study is to provide a natural alternative to conventional shampoos by incorporating eggshell powder, a readily available, eco-friendly resource, into future hair care formulations. A comprehensive study was conducted to evaluate various shampoos currently available and to identify the benefits of eggshell powder. This study highlights the potential of eggshell powder in enhancing shampoo production. Machine learning algorithms such as Naive Bayes, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest were employed to analyze manufacturing parameters and optimize the absorption of eggshell powder. The results of the analysis revealed varying accuracies for each model: Naive Bayes (52%), KNN (71%), SVM (72%), and Random Forest (82%). These techniques allowed for precise adjustments to ingredient concentrations and interactions, improving the overall efficacy of the shampoo. The results demonstrate that shampoos formulated with eggshell powder offer several advantages, including stronger hair, better moisture retention, and enhanced scalp health. Additionally, eggshell powder proved to be a sustainable material, aligning with growing consumer demand for environmentally friendly products. This study highlights the potential of using natural resources and machine learning to drive data-driven improvements in hair care formulations, offering a promising alternative to conventional products while meeting the increasing preference for sustainability.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room C Pune, India

3:00pm IST

Impact of AI on Social Services: Opportunities, challenges, Future Direction
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Vidhi Aakash Pandya, Meetu Joshi
Abstract - The present research looks at how artificial intelligence (AI) is affecting various interpersonal sectors and offers opportunities, problems, and potential solutions. It investigates how artificial intelligence (AI) has developed into a crucial instrument for tackling social problems and providing answers in a variety of fields, including healthcare, education, the environment, and agriculture. The history of AI's development from historical turning points to modern deep learning applications opens the study. It then dives into a thorough review of the literature, highlighting important research and the condition of AI application in many industries at the moment. The study examines AI's potential applications in healthcare, with a focus on tailored treatment methods, diagnostics, and disease prediction. It also addresses ethical issues. AI is being used in education to investigate how diversity and specific instruction might be achieved using voice assistants and virtual mentors, among other technologies.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room C Pune, India

3:00pm IST

Mental health detection using EEG signals
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Yash Dargude, Jui Ambekar, Yash Gadakh, S.T Gandhe
Abstract - Mental health disorders, such as depression, anxiety, and stress, are global challenges that significantly affect individuals’ well-being and productivity. Early detection and diagnosis are crucial for effective intervention, yet traditional methods often rely on subjective assessments, leading to potential delays. Electroencephalography (EEG) has emerged as a promising non-invasive tool for objectively monitoring brain activity, offering valuable insights into mental health conditions. This survey paper explores the current state-of-the-art in mental health detection using EEG signals. We provide an overview of EEG-based systems, highlighting key signal processing techniques such as filtering, artifact removal, and noise reduction. Feature extraction methods, including time-domain, frequency-domain, and time-frequency domain techniques, are reviewed to emphasize how patterns in brainwave activity correlate with mental health states. Additionally, we examine various machine learning and deep learning algorithms, such as Support Vector Machines (SVM), Random Forest, and Convolutional Neural Networks (CNNs), which have been applied to classify mental health conditions based on EEG data. The paper also presents a comprehensive analysis of the effectiveness of these models in detecting specific mental health conditions like depression, anxiety, and stress. We discuss the challenges faced in using EEG for mental health detection, such as signal variability and the need for large datasets, and propose future directions for enhancing the accuracy and generalizability of these models. This survey aims to contribute to the development of more reliable, EEG-based diagnostic tools for mental health assessment.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room C Pune, India

3:00pm IST

Plugin-Based Tor Traffic Analysis: A Deep Learning Approach for Identification of Obfuscated Tor Traffic
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Krishan Pal Singh, Emmanuel S. Pilli, Vijay laxmi
Abstract - Tor network provides anonymity and privacy to online users. Hence, analyzing Tor traffic to identify applications and services, especially when encrypted tunnels and pluggable transports are used, remains a significant challenge. This paper presents a novel framework for identifying obfuscation techniques by analyzing their unique traffic characteristics, such as packet sizes, inter-arrival times, byte sizes, and byte frequencies. A custom-built network traffic collection environment is established to evaluate the proposed framework. A large Tor traffic dataset is created that contains Obfs4 and Snowflake Plugin traffic, ensuring realistic user behavior simulation utilizing modified Tor browser configurations. The framework leverages a combination of statistical analysis of encrypted payloads, examines timing sequences during authentication, and packet length filtering. The Traffic data is evaluated on diverse deep learning models, such as Neural Networks, Adaboost, and XGBoost, achieving high accuracy rates (95% to 98%) across different Tor plugins. The proposed framework demonstrates robustness with low false positive rates. It is also adaptable to new Tor obfuscation techniques such as Obfs4 and Snowflake. The research findings highlight the importance of using up-to-date and diverse datasets to train effective Tor plugin identification models, with potential applications for improving Tor network security.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room C Pune, India

3:00pm IST

Prioritizing SHE Packets for Emergency Response
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Vinayak Suresh Bhajantri, Aishwarya B Kalatippi, Rahul B Sajjan, Babusingh Ramsingh Rajput, Kiran M R, Suneeta Budhihal
Abstract - In recent years, natural disasters like earthquakes, tsunamis, floods, and storms have happened frequently, causing severe damage. These disasters have shown how crucial it is to have reliable communication for rescue operations. Often, disasters damage communication network. The heavy demand for data transfer on the Internet is pushing its infrastructure to the limit, making it difficult to respond quickly to emergencies and disasters. To solve this problem, Internet networks need to prioritize certain types of data traffic: Security, Health, and Emergency (SHE) data traffic. These specialized networks work in private domains to support specific tasks for particular groups of users. We proposed network flow priority management system based on Software-Defined Networking (SDN) to give SHE data traffic the highest priority. Using the Mininet simulator, we tested our system extensively. The results show significant improvements in handling SHE data traffic, ensuring that during network congestion, SHE data is transmitted quickly, improving the effectiveness of emergency response efforts.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room C Pune, India

3:00pm IST

A Novel Mary Improvisation-based Parameter Aggregation Algorithm for Segmenting Brain Tumours in a Federated Learning Setup
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Shiva Kumar Bandaru, Upendra Pratap Singh
Abstract - In a federated learning-based setup, parameter aggregation plays a pivotal role in obtaining global parameter estimates that assimilate the knowledge learned by the different clients. With an efficient parameter aggregation strategy, the global parameter estimates derived are more generalizable, accelerating the local client training in the subsequent communication rounds. In the proposed approach, we propose a novel m-ary improvisation-based parameter aggregation algorithm to obtain the global parameters. Specifically, after a threshold number of communication rounds has elapsed, the performance of the clients is evaluated on an independent test set, and the clients with better generalization are labeled as strong and do not participate in the next set of a threshold number of communication rounds. In this way, weak clients participate in the federated learning for more communication rounds; after the next set of threshold communication rounds has elapsed, the clients undergo a similar evaluation to be labeled as strong or weak again. The proposed algorithm ensures weak clients get more attention/exposure to learn the model parameters collaboratively. The global model trained on the BraTS2020 dataset in a federated learning-based framework reports the Dice coefficient, Jaccard index and pixel accuracy values of 0.8851, 0.8965, and 99.92%, respectively. Further, we show empirically that the training time for the different clients reduces from 180 minutes in the first phase of federated learning to only 64.8 minutes in the last phase, highlighting an accelerated training process. Consequently, the results reported by the proposed federated learning-based segmentation model highlight its usability for efficiently carrying out brain segmentation involving private and sensitive brain scans.
Paper Presenter
avatar for Shiva Kumar Bandaru

Shiva Kumar Bandaru

United Kingdom
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room D Pune, India

3:00pm IST

Analysing Web 3.0-Based Metaverse Banking Services: Through the Lens of Diffusion of Innovation Theory
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Saurav Kumar, Shivani, Rashmy Moray, Shikha Jain, Sridevi Chennamsetti
Abstract - The aim of the study is to inspect the factors determining the use of web 3.0 Meta based banking services. Diffusion of innovation theory has been used to explain the influence of perceived factors on attitude and behavioural intention to use the meta based banking services. Structured questionnaire as primary source of data collection has been applied and data gathered was analyzed using Structural equation model as statistical technique to achieve the stated objectives. SmartPLS as statistical tool was employed in analyzing the data and the outcome reveal that compatibility, observability and trialability showed a significant impact on attitude towards usage intent of Web 3.0 based meta banking services. The study has proved to be significant in the field of banking on metaverse for various stake holders and policy makers and be helpful to understand the perception of the customers in the usage of web 3.0 based banking.
Paper Presenter
avatar for Shivani

Shivani

India
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room D Pune, India

3:00pm IST

ARTIFICIAL INTTELIGANCE IN EDUCATION AN OVERVIEW
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Shubham Kishor Kadam, Pankajkumar Anawade, Deepak Sharma, Anurag Luharia
Abstract - Artificial Intelligence (AI) may be defined as utilization of computer systems in undertaking processes, which are typical of human intelligence. AI is an incomparably new and actively developing scientific direction, which can qualitatively change most of the social processes. In the context of the increased usage of AI, the different educational settings are applying this technology to create new perspectives in the sphere of pedagogy nowadays. Today it is utilized to sift through incalculable quantities of information in order to discover patters, which would help devise better and more appropriate policies and educational strategies than the existing ones. This paper determine the pertinence of the AI in consideration of education along with the challenges using AI in education.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room D Pune, India

3:00pm IST

Biodegradable Packaging for Green Logistics: A Multi- Factor Analysis Optimizing Sustainability in Freight Transportation
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Harishh N, Drisya Murali, Suresh M
Abstract - The study explores the possibilities of green logistics and the adoption of biodegradable packaging in freight transportation, focusing on the impact on reducing packaging waste and bringing in sustainability. The research uses the Grey Influence Analysis (GINA) methodology to analyze the identified eleven significant factors, which impact the adoption of biodegradable packaging in freight transportation. The primary role of packaging is to protect products during storage and transport, reduce costs, and sustainable way of product distribution and safety. The study also highlights the importance of improving the material properties of packaging, which can mitigate or minimize adverse environmental impacts. The study's findings highlight the need for various perspectives in future studies and the need for a comprehensive understanding of the relationship between various factors influencing biodegradable packaging in freight transportation.
Paper Presenter
avatar for Harishh N
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room D Pune, India

3:00pm IST

Bridging the Digital Divide: The Role of ICT in Promoting Inclusive Social and Economic Development
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Utkarsha Wanjari, Shubham Kadam, Chhitij Raj, Pankajkumar Anawade, Deepak Sharma
Abstract - The digital divide continues to be a global issue since it accounts for the marginalization between the group owning access to Information and Communication Technology (ICT) and those without access. This report looks at the crucial role of ICT in bridging this gap and ensuring integral social and economic development. ICT does hold tremendous transforming potential through its power to enrich education, modify healthcare delivery systems, and strengthen governance through digital inclusion. Economically, it propels innovation, expands access to global markets, and creates financial inclusion through digital tools. Though still highly significant, challenges persist in the form of infrastructure deficits, digital literacy gaps, and socioeconomic inequalities. Through case study examples and successful global initiatives, this report is shaped by best practices and strategies to work around these challenges. It draws attention to public-private partnership efforts, policy reform, and investment in ICT infrastructure and ICT training. Bridging the digital divide is not just technical but also a pathway to achieving equitable and sustainable development in an increasingly digitalizing world.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room D Pune, India

3:00pm IST

Context Aware Data Synchronization in Ubiquitous Networks
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Vasudha V. Ayyannavar, Lokesh B. Bhajantri
Abstract - The healthcare sector is rapidly evolving, making the continuous exchange of healthcare data essential for both patient care and maintaining operational efficiency. In today’s landscape, file and data synchronization is no longer optional but a crucial requirement. This work presents a real-time data synchronization system tailored for hospital records management, enabling seamless and secure communication among healthcare users. The system uses real-time synchronization to ensure that updates made on the server are instantly reflected across all connected clients. In this work, a robust architecture is developed to support both MySQL and MongoDB databases, offering flexible data storage. It associates with Node.js and Express.js, utilizing Socket Input and Output for real-time and bidirectional communications. On the front end, HTML, CSS, and JavaScript are combined with Bootstrap to create a responsive and user-friendly interface, allowing easy data input and retrieval by healthcare users. The proposed solution ensures conflict-free data dissemination across various devices and is compared against existing methods, analyzing key metrics such as synchronization time, memory usage, and data accuracy. Overall, the system aims to enhance hospital records management through a reliable, scalable, and intuitive real-time synchronization solution.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room D Pune, India

3:00pm IST

Droid Guard: Integrated Malware Scanner
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Ganesh Haricharan Mungara, Pranai Govind Soorneedi, Karthik Mungara, C.N.S.Vinoth Kumar
Abstract - The proliferation of smartphones has transformed communication, work, and information access. However, this convenience has brought significant security challenges, particularly from malware that can compromise user data and privacy. Despite numerous antivirus applications, detecting and removing malware from Android devices remains a challenge. Current solutions of ten fail to detect sophisticated malware, necessitating the intervention of cyber security experts, which can compromise user privacy. This project aims to develop a tool that detects malware on Android devices based on installed applications, eliminating the need for users to install third-party software. The proposed solution leverages pattern matching by checking installed packages against a database of known malware. If a match is found, the tool indicates potential malware presence. This method offers a privacy-preserving approach, focusing on app behavior rather than relying solely on signatures, making it harder for malware to evade detection. The tool addresses the limitations of existing antivirus solutions, which often require extensive permissions and access to personal data. By providing a user-friendly interface and ensuring privacy, this project aims to enhance the overall security of Android devices. Future enhancements include incorporating machine learning models to improve detection accuracy and expanding the tool to other mobile platforms like iOS. This innovative approach offers a reliable and privacy-focused alternative for malware detection on Android devices.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room D Pune, India

3:00pm IST

Improving Accuracy in Coronary Artery Disease Diagnosis with an Artificial Neural Network Bagging Ensemble Learning Classifier
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Pratibha Verma, Sanat Kumar Sahu, Latika Tamrakar
Abstract - Coronary Artery Disease (CAD) is a major crisis midst populace worldwide. So, we prerequisite a system that is effective for the identification of CAD problems. In this study we formed a model substance on the classification technique that can clarification the problem of CAD. The Ensemble Bagging classification method develops the creation of multiple classifier models and their mutual outputs to achieve a unified classification outcome. This technique has been implemented in the field of CAD using Artificial Neural Network (ANN) models. The ANN based models are Multi-layer Perceptron Network (MLPN or MLP), Radial Basis Function Network (RBFN), ensemble bagging –RBFN (EB-RBFN), and ensemble bagging MLP (EB-MLP). Our experimental outcomes indicate that the anticipated ensemble bagging model suggestively enhances dataset classification accuracy when compared to individual MLP and RBFN classifiers. This ensemble model consistently delivers more accurate and valuable classification results. Its implementation substantially improves CAD diagnostic accuracy, enabling the more precise identification of patients affected by this condition. These findings imply that the utilization of ensemble learning techniques, specifically ensemble bagging with ANN models, holds great potential in enhancing the precision of CAD diagnosis. This advancement has the potential to improve patient management and treatment outcomes.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room D Pune, India

3:00pm IST

Indian Stock Market Prediction Using Neural Networks: A Comparative Analysis
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Pampati Sreya, Yashaswi D, Stephen R, Gobinath R, Ramkumar S
Abstract - Predicting stock prices remains a challenging problem due to the highly dynamic and non-linear nature of financial markets. Traditional statistical models like ARIMA and GARCH often fail to capture the complexities inherent in stock market data. This paper investigates the use of deep learning techniques, focusing on Convolutional Neural Networks (CNNs) and a hybrid CNN-LSTM ensemble model for stock price prediction in the Indian stock market. The CNN model efficiently extracts temporal patterns from sequential data, while the CNN-LSTM ensemble leverages temporal dependencies for improved long-term prediction accuracy. Historical data from Tata Motors, spanning over two decades, was used to train and evaluate the models. Experimental results highlight the CNN-LSTM ensemble's superior performance in capturing volatile trends and long-term dependencies, with a notable decrease in test loss compared to standalone CNN. This study underscores the effectiveness of hybrid deep learning architectures in enhancing prediction reliability, paving the way for more adaptive and robust financial forecasting systems.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room D Pune, India

3:00pm IST

Network Intrusion Detection System Using Learning Algorithms
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Mohmed Umar, Jeevakala Siva Rama Krishna
Abstract - In the era of complete digital connectivity, it is the need of the hour to keep the networks safe from a wide range of cyberattacks. Traditional Network Intrusion Detection Systems (NIDS) rely mainly on signature-based approaches; though highly efficient in identifying known threats, they suffer from weaknesses in discovering new and developing attacks, such as zero-day vulnerabilities. This results in higher false positives and lower detection efficiency. We present a novel NIDS based on the ensemble methods in machine learning, namely Random Forest and Bagging Classifiers, with which we may promise detection accuracy at the cost of a reduced level of false alarms. We conduct extensive evaluations based on systematic data preprocessing, feature selection, and model training against benchmark datasets like KDD Cup 99 and NSL-KDD. The system being considered achieves a detection accuracy of 99.81%, along with an F1 score of 99.82% and an AUC score of 99.81%, thus significantly surpassing the performance from traditional approaches. These results show the aptness of machine learning methodologies in enhancing network security, as it makes for a flexible and scalable solution suited for real-time deployment in extensive environments. Future work will focus on further developing the scalability of the system and minimizing latency to ensure seamless real-time operation.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room D Pune, India

3:00pm IST

A Comparative Evaluation of ML, DL, and Transformer Models in Arabic Sentiment Analysis
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Amani A. Aladeemy, Sachin N. Deshmukh
Abstract - Sentiment analysis (SA) discerns the subjective tone within text, categorising it as positive, neutral, or negative. Arabic Sentiment Analysis (ASA) has distinct obstacles owing to the language's intricate morphology, many dialects, and elaborate linguistic frameworks. This study compares SA models for Arabic text across multiple datasets, evaluating traditional machine learning (ML) algorithms, such as Random Forest (RF) and Support Vector Machine (SVM); deep learning (DL) models, including Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU); and transformer-based models like BERT, AraBERT, and XLM-RoBERTa. Experiments on datasets—HARD, Khooli, AJGT, and Ar-Tweet—covering MSA and dialects such as Gulf and Egyptian demonstrate that transformer-based models, particularly AraBERT v02, achieve the highest accuracy of 93.9% on the HARD dataset. The study highlights the significance of dataset characteristics and the advantages of advanced models, offering valuable insights into Arabic NLP and advancing SA research.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room E Pune, India

3:00pm IST

A comparative study of Rainfall Prediction on Indian Regions using Gradient Boosting and Random Forest Algorithms
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Ritika Upadhyay, Eshita Dey, Munmun Patra, Roji Khatun, Chinmoy Kar, Somenath Chaterjee
Abstract - Predicting accurate rainfall is crucial for a country like India, which has a diverse economy. Agriculture is a vital aspect of life for many rural communities in India, making timely rainfall a significant concern for improving agricultural yields. However, predicting rainfall has become increasingly challenging due to drastic climate changes, resulting in more frequent natural calamities like floods and soil erosion. To address this issue, extensive research is underway to enhance rainfall prediction, allowing people to take appropriate precautions to protect their crops. Currently, predictive models tend to be complex statistical frameworks that can be expensive in terms of both computation and budget. As a more effective solution, using historical data combined with machine learning algorithms is being proposed. This research aims to improve rainfall prediction through algorithms such as Gradient Boosting and Random Forest. Model evaluation will utilize metrics like Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). This study has considered approximately 150 years of historical rainfall data (from 1813 to 2006) for different regions of India.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room E Pune, India

3:00pm IST

AFR System: Optimizing Traffic Signals for Emergency Vehicle Prioritization
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - G.KALANANDHINI, VIGNESHWARAN.D, R.KARTHIKA, S.PUSHPALATHA, D.SAKTHIPRIYA
Abstract - Activity delays confronted by crisis vehicles regularly result in basic time misfortune, imperiling lives. The Help to begin with Responders (AFR) framework addresses this issue by utilizing LoRa SX1276 communication modules designed as transmitters in crisis vehicles to communicate with recipients at activity intersections. This framework empowers programmed green light signals for drawing closer crisis vehicles, guaranteeing continuous section. GPS NEO-6M modules give directional data, whereas a centralized authorization component anticipates abuse. Particular vehicle IDs permit for prioritized reaction, with fire motors taking the most noteworthy need, taken after by ambulances and police cars. Activity policemen are informed to oversee synchronous mediations successfully. Typical activity flag operations continue when no crisis vehicle is recognized. The AFR framework leverages Arduino Nano for LoRa modules, Arduino UNO for activity control, and ESP8266 for authorization. This integration improves crisis reaction times and moves forward security for both patients and responders, displaying a noteworthy progression in urban activity management.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room E Pune, India

3:00pm IST

Analysis of the Audio in the Game of Cricket Using Machine Learning
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Varun M V, Venkat Raghavendra A H, V Hemanth, Ashwini Bhat
Abstract - The work undertaken is a comprehensive analysis of cricket sounds, focusing on the interaction of the ball with the bat and the wicket, the study aims to distinguish between edged, shot, and bowled audio in both noisy and noise-free environments. Upon feature extraction, machine learning models XGBoost and Random Forest were trained, to accurately classify these distinct cricketing events. This not only enriches the realm of cricket analysis by facilitating informed decision-making and insights into player performance but also showcases the potential of audio-based sports analytics.
Paper Presenter
avatar for Varun M V
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room E Pune, India

3:00pm IST

Cross-Modality Attention Networks for Multi Phase Lung Tumour Detection
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - N.Janani
Abstract - In clinical practices, almost 18-20% cases go either unnoticed or misdiagnosed due to overlapping and subtle features in imaging, especially in complicated cases. We tackle this by using Cross-Modality Attention Network (CMAN) which integrates details from multi-phase CTs. By leveraging the distinct advantages of each scan phase, this method provides a comprehensive understanding of the tumor’s structure and characteristics. The cross-modality approach employs an attention mechanism to integrate information from multiple scan modalities, each capturing unique details. This process emphasizes the most critical tumor-related features while effectively minimizing noise, ensuring enhanced classification accuracy. Achieving an impressive accuracy of 98.47% on the LIDC-IDRI dataset, the CMAN significantly reduces misdiagnosis in complex cases. This approach can be really helpful in filling the diagnostic gaps, facilitating more informed clinical decision-making and improved patient outcomes.
Paper Presenter
avatar for N.Janani
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room E Pune, India

3:00pm IST

Enhancing Visual Question Answering for Medical Images using Transformers and Convolutional Autoencoder
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Parekh Rikita Dhaval, Hiteishi M. Diwanji
Abstract - The Visual Question answering is an emerging multidisciplinary research field that intersect computer vision and natural language processing. Medical Visual Question Answering is one of the prominent area of VQA. Medical images and Clinical Questions related to the medical image is given as input to the VQA model and VQA model respond with corresponding answer in natural language. The aim of Medical VQA is to enhance interpretability of medical image data for enhancing diagnostic accuracy, clinical decision making and patient care. This paper presents a novel framework that integrates Vision Transformer (ViT), Language transformer (BERT), and a Convolutional Autoencoder (CAE) to improve the performance of Medical VQA task. The Vision Transformer is used to capture complex visual features from medical images, while BERT processes the corresponding clinical question to understand its context and generate meaningful language embedding. To further enhance visual feature extraction, a Convolutional Autoencoder [1], [2] is incorporated to preprocess and denoise the medical images, capturing essential patterns, compressing medical images without losing key features, thereby providing cleaner input to the ViT. The combined use of these three components enables the model to effectively align visual features with textual information, leading to more precise and context-aware answers. We evaluate the proposed ViT+BERT+CAE model on benchmark medical VQA dataset MEDVQA-2019, showing significant improvements over traditional methods based solely on convolutional or recurrent networks. The results demonstrate significant increase in accuracy, precision, recall, F1-Score and WuPS score after applying Covolutional AutoEncoder in Preprocessing stage.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room E Pune, India

3:00pm IST

FFAFER: Fiducial Focus Augmentation for Facial Expression Recognition
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Ritu Raj Pradhan, Darshan Gera, P. Sunil Kumar
Abstract - This paper explores static facial expression recognition (FER) and presents a novel facial augmentation technique designed to enhance model training. By utilizing pre-trained facial landmark detection models, we analyze the spatial structure of faces within the FER training dataset. Based on the predicted landmark coordinates, facial images are augmented by strategically masking patches of varying sizes at key landmark locations. This approach emphasizes the structural significance of facial landmarks while preserving other critical facial features, enabling models to capture both global facial structure and nuanced expression-related details. Extensive experiments on benchmark datasets validate the effectiveness of the proposed method, showcasing its potential to improve FER performance, particularly in challenging scenarios.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room E Pune, India

3:00pm IST

Hybrid Model of Chaos Theory and Quantum Techniques for Portfolio Optimisation
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Deepali, Karuna Kadian, Kashish Arora, Saumya Johar, Liza
Abstract - The stock market has become increasingly unpredictable in recent years due to various factors like public sentiments, economy and geopolitical issues. The Traditional methods being used like time series model and Long Short-Term Memory (LSTM) models, often don’t make the correct predictions as they rely mostly on historical data of stock market and so they fail to grasp how market behaves or how chaotic behavior of market can be analyzed. These models hence may fail in case of making wise investment decisions. Our proposed methodology comes up with a hybrid approach using chaos theory, sentimental analysis for overcoming these challenges by analyzing the how stock prices might change according to the sentiments of people. We analyze 65,000 tweets of 95 organizations and their stocks and use chaos theory to find hidden patterns in stock movements. The classical computers take high computational time to analyze complex problems like stock market predictions. Hence, we combine these approaches with the Quantum Approximate Optimization Algorithm (QAOA) to solve the complex patterns of stock price prediction faster and more accurately than classical methods. We have used sentimental analysis, chaos theory with QAOA which is a combinatorial algorithm, being used to optimize the stock portfolio based on specific stock metrics- inclusive of F1 score(from sentimental anaylsis) and chaos theory assessments, it researches for the organisations with stability and low risk-high returns in stock market. Thus aiding investors and traders to make an informed decisions regarding where to invest with low risk and high returns.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room E Pune, India

3:00pm IST

Learning beyond Limits: Exploring Augmented Reality and Virtual Reality in Education and Training
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Dipti Varpe, Kalyani Kulkarni, Vaidehi Deshpande, Vedaant Deshpande, Vaishnavi Habbu
Abstract - Augmented Reality (AR) and Virtual Reality (VR) enhance traditional pedagogical methods by providing immersive, interactive and experiential learning environments, while catering to diverse learning styles. The paper examines their effectiveness in improving knowledge retention, fostering engagement, and enabling hands-on practice in simulated real-world scenarios, citing comparisons with traditional teaching tools. In education, AR and VR allow visualization of abstract concepts, collaborative virtual environments and gamified learning experiences that make complex subjects accessible and engaging. For training purposes, these technologies are instrumental in safe skill acquisition, particularly in high-risk fields such as healthcare and military operations. Challenges such as high costs of facility maintenance and safe implementation are also addressed. This review concludes with recommendations for leveraging this technology to create impactful and scalable solutions for learners and trainees in various disciplines.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room E Pune, India

3:00pm IST

Quality assessment of Fruits and Vegetables using Deep Learning
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - R.V. Sai Sriram, A. Srujan, K. Rahul, K. Sathvik, Para Upendar
Abstract - Freshness plays a crucial role in determining the quality of fruits and vegetables, directly impacting consumer health and influencing nutritional value. Fresh produce used in food processing industries must go through multiple stages—harvesting, sorting, classification, grading, and more—before reaching the customer. This paper introduces an organized and precise approach for classifying and detecting the freshness of fruits and vegetables. Leveraging advanced deep learning models, particularly convolutional neural networks (CNNs), this method analyzes images of produce. The training and evaluation dataset is large and varied, including diverse fruits and vegetables in various conditions. Freshness is determined by analyzing key features like color, texture, shape, and size. For example, fresh produce typically shows vibrant color and is free from mold or brown spots. Traditional methods for assessing quality through manual inspection and sorting are often slow and error prone. Automated detection techniques can significantly mitigate these challenges. Therefore, this paper proposes an automated approach to freshness detection, which first identifies whether an image shows a fruit or vegetable and then classifies it as either fresh or rotten. The ResNet18 deep learning model is employed for this identification and classification task. It also estimates the size of the fruit/vegetable using OpenCV. The qualitative analysis of this approach demonstrates outstanding performance on the fruits and vegetables dataset.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room E Pune, India

3:00pm IST

6G: A revolutionary transformation, network congestion issues, and a singular value decomposition based approach to optimize congestion using RLNC in wireless networks
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Syed Abidhusain, Baswaraj Gadgay, Shubhangi D C
Abstract - A notable problem in the contemporary expanding networking paradigm is the congestion resulting from the substantial volume of data transmitted between nodes in any network. During congestion, packets may become damaged or lost. By evaluating the relationships among various network QoS measures, we may identify and address congestion issues that occur in such scenarios. This study use singular value decomposition (SVD) to mitigate congestion issues. Singular Value Decomposition (SVD) is a matrix factorisation method utilised in diverse applications, such as principal component analysis (PCA) and linear regression. We employ an innovative method known as SVDULR, which utilises singular value decomposition and linear regression to efficiently identify congestion and improve service quality.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room F Pune, India

3:00pm IST

Deep Learning for Uncovering of Fraud: A Design for Automated Financial Protection
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Ajay Singh, Ambu Sharma, Pawan Kumar, Sanjay Taneja, Mukul Bhatnagar
Abstract - Leveraging the unparalleled adaptability and hierarchical feature stratification capabilities of deep learning, this study constructs a sophisticated framework for fraud detection, seamlessly integrating convolution and recurrent neural architectures with advanced anomaly detection algorithms to decode complex, non-linear transactional patterns within heterogeneous financial datasets, thereby enabling real-time fraud identification while addressing pivotal challenges of algorithmic interpretability, adversarial resilience, regulatory compliance, scalability, and data confidentiality, ultimately redefining the paradigm of automated financial security in an increasingly digitized global economy..
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room F Pune, India

3:00pm IST

Enhancing Communication for the Hearing-Impaired with Deep Learning Approaches on Bangla Sign Language Recognition
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Nadiya Hoque Shudha, Fatema Tuj Johura, Anupam Singha, Kingkar Prosad Ghosh
Abstract - Sign language is vital for effective communication among deaf individuals, helping them to connect with others and break down communication barriers, which improves their overall well-being. For those with hearing impairments, knowing sign language is key to facing these challenges. While sign language is not used everywhere, sign language recognition has gained significant attention in computer vision and deep learning, aiming to improve this communication method for broader use. This paper introduces a convolutional neural network model designed to identify images of Bengali sign language gestures for native speakers through image classification. The model was trained using two publicly available datasets: one for one-handed Bengali sign language with 30 different sign alphabets and another for two handed Bengali sign language with 36 distinct sign alphabets. Various image preprocessing methods including gamma correction, grayscale conversion, CLAHE, resizing and normalization were used to enhance the datasets, making the model more robust. The results were impressive, with the model achieving 98.58% accuracy on the one-handed dataset and 94.86% on the two-handed dataset. These results show the model’s effectiveness in classifying Bengali sign alphabets, which could improve communication access for the hearing-impaired community.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room F Pune, India

3:00pm IST

MULTI-RELATIONAL GRAPH ATTENTION BASED DEPTHWISE SEPARABLE CONVOLUTIONAL NEURAL NETWORK FOR SPATIO-TEMPORAL EPIDEMIC FORECASTING
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Ravi Kumar Suggala, B Hema, B Naga Jahnavi, Ch Anannya Sai, Ch Saumya Prasanna, J Hari Keerthi
Abstract - The basic modeling strategies of infectious diseases have become particularly important, especially during the times of the COVID-19 crisis. Although GNNs achieve remarkable accuracy in mimicking inter-regional interactions using spatiotemporal information, they infrequently capture the causal factors that govern the spread of epidemics. In an attempt to fill this research gap, this study puts forward the Multi-Relational Graph Attention Depth wise Separable Convolutional Neural Network with Satin Bowerbird Optimization Algorithm (MRGADSCNNet-SBOA). Both DSCNN and MRGAT components of the model are used to model temporal and spatial correlations of epidemic data. DSCNN can capture spatial interconnections well while adjusting node attributes through graph attention based on the relations that existed between neighboring areas even though it only has relatively few parameters for temporal relations. Here, the result reflects the model with the help of improved Satin Bowerbird Optimization Algorithm (SBOA) with the RMSE 1231, MAE 13823, MAPE 10.24%, PCC 96.42% and CCC 98.91%. Due to its high reliability, the model offers a reasonable instrument for explaining epidemics in time and space.
Paper Presenter
avatar for B Hema

B Hema

India
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room F Pune, India

3:00pm IST

OpenRAN Orchestration for 5G and Beyond: A Comparative Analysis of End-to-End Deployment Capabilities of Orchestration Solutions
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Huzaib Shafi Shah, Rajendra Gupta
Abstract - 5G has introduced unprecedented speed, reliability, and low latency in telecommunications. 6G is expected to enhance these capabilities further. OpenRAN architectures for these networks offer notable cost efficiency and operational flexibility advantages but introduce greater complexity in deployment and orchestration (Azariah et al., 2024). This study critically evaluates four OpenRAN orchestration systems across five essential factors. The findings reveal limited support for multi-vendor interoperability and significant variability in the systems' practicality and coverage of use cases. While some systems focus on specific RAN functions, others adopt a more holistic approach. Furthermore, inconsistent key performance indicators (KPIs) complicate direct comparisons.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room F Pune, India

3:00pm IST

Revolutionizing Healthcare: Contributions and Considerations of AI in Clinical Practices
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Parambrata Sanyal, Mukund Kuthe, Sudhanshu Maurya, Ajay Kumar, Firdous Sadaf M. Ismail, Rachit Garg
Abstract - The integration of artificial intelligence (AI) in healthcare and its significant rise has gained attention in recent days. The rapid and notable advancements in AI technologies and their co-domains have truly aided the healthcare sector with its praiseworthy way of shaping and serving healthcare that is beneficial for society. From disease diagnosis, doctor assistance, and patient assistance to healthcare machinery advancements, AI, along with its codomains such as the Internet of Things (IoT) and robotics, have played a crucial part in aiding society. However, with the unprecedented benefits, AI comes with different challenges. The evident and immense potential of AI and its co-domains can be highlighted, but its ethical and responsible use should be underscored and ensured. This paper aims to navigate ethical and technical considerations, ultimately leading to a revolutionized impact that is ethically grounded and innovative at the same time by responsible AI use. Addressing these challenges can bring out the full potential of AI and enhance the overall patient treatment outcomes along with the quality of care. This paper also systematically reviews all the AI-driven advancements in the recent past and their overall performances, which different stakeholders and users underscore.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room F Pune, India

3:00pm IST

Smart Healthcare Systems and Artificial Intelligence: Leveraging Data Driven Solutions in Healthcare
Thursday January 30, 2025 3:00pm - 5:00pm IST
Authors - Sidra Zaidi, Nandini Paliwal, Riya Baby, Amala Siby
Abstract - Smart healthcare systems, powered by artificial intelligence (AI), enables drastic shift in healthcare sector. The benefits of data driven solutions anchored in advanced disease diagnosis, efficient administrative records management and operational efficiency. AI algorithms, including machine learning and deep learning, enable real-time data analysis from diverse sources such as Internet of medical Things (IoMT), wearable devices, and medical imaging. This facilitates early disease detection, personalized treatment plans, and predictive analytics, leading to improved health outcomes. Additionally, AI-powered systems optimize resource management, reduce human error, and streamline administrative tasks. While challenges such as data privacy, integration complexities, and ethical concerns exist, the potential of smart healthcare systems to revolutionize healthcare delivery is undeniable. This study explores the role of AI in advancing smart healthcare, focusing on the integration of data-driven technologies to create a more efficient, accessible, and patient-centered healthcare ecosystem.
Paper Presenter
Thursday January 30, 2025 3:00pm - 5:00pm IST
Virtual Room F Pune, India

4:45pm IST

Session Chair Remarks
Thursday January 30, 2025 4:45pm - 4:50pm IST
Invited Guest/Session Chair
avatar for Dr. Smita Agrawal

Dr. Smita Agrawal

Professor & Head, Department of Information Technology, JECRC Foundation, Jaipur, India
Thursday January 30, 2025 4:45pm - 4:50pm IST
Virtual Room A Pune, India

4:45pm IST

Session Chair Remarks
Thursday January 30, 2025 4:45pm - 4:50pm IST
Invited Guest/Session Chair
avatar for Dr. Akhilesh Kumar Sharma

Dr. Akhilesh Kumar Sharma

Professor & Head, Manipal University Jaipur, India
Thursday January 30, 2025 4:45pm - 4:50pm IST
Virtual Room B Pune, India

4:45pm IST

Session Chair Remarks
Thursday January 30, 2025 4:45pm - 4:50pm IST
Invited Guest/Session Chair
avatar for Dr. Upesh Patel

Dr. Upesh Patel

Associate Professor & Head, CHARUSAT University, Gujarat, India.
Thursday January 30, 2025 4:45pm - 4:50pm IST
Virtual Room C Pune, India

4:45pm IST

Session Chair Remarks
Thursday January 30, 2025 4:45pm - 4:50pm IST
Invited Guest/Session Chair
avatar for Prof. Ronakkumar N. Patel

Prof. Ronakkumar N. Patel

Assistant Professor, Computer Engineering, CSPIT, CHARUSAT University, Gujarat, India
Thursday January 30, 2025 4:45pm - 4:50pm IST
Virtual Room D Pune, India

4:45pm IST

Session Chair Remarks
Thursday January 30, 2025 4:45pm - 4:50pm IST
Invited Guest/Session Chair
avatar for Dr. Kaushal Shah

Dr. Kaushal Shah

Assistant Professor, Pandit Deendayal Energy University, Gujarat, India.
Thursday January 30, 2025 4:45pm - 4:50pm IST
Virtual Room E Pune, India

4:45pm IST

Session Chair Remarks
Thursday January 30, 2025 4:45pm - 4:50pm IST
Invited Guest/Session Chair
avatar for Dr. Sangeeta Kurundkar

Dr. Sangeeta Kurundkar

Associate Professor, Vishwakarma Institute of Technology, Pune, India
Thursday January 30, 2025 4:45pm - 4:50pm IST
Virtual Room F Pune, India

4:50pm IST

Closing Remarks
Thursday January 30, 2025 4:50pm - 5:00pm IST
Moderator
Thursday January 30, 2025 4:50pm - 5:00pm IST
Virtual Room A Pune, India

4:50pm IST

Closing Remarks
Thursday January 30, 2025 4:50pm - 5:00pm IST
Moderator
Thursday January 30, 2025 4:50pm - 5:00pm IST
Virtual Room B Pune, India

4:50pm IST

Closing Remarks
Thursday January 30, 2025 4:50pm - 5:00pm IST
Moderator
Thursday January 30, 2025 4:50pm - 5:00pm IST
Virtual Room C Pune, India

4:50pm IST

Closing Remarks
Thursday January 30, 2025 4:50pm - 5:00pm IST
Moderator
Thursday January 30, 2025 4:50pm - 5:00pm IST
Virtual Room D Pune, India

4:50pm IST

Closing Remarks
Thursday January 30, 2025 4:50pm - 5:00pm IST
Moderator
Thursday January 30, 2025 4:50pm - 5:00pm IST
Virtual Room E Pune, India

4:50pm IST

Closing Remarks
Thursday January 30, 2025 4:50pm - 5:00pm IST
Moderator
Thursday January 30, 2025 4:50pm - 5:00pm IST
Virtual Room F Pune, India
 
Friday, January 31
 

9:30am IST

Opening Remarks
Friday January 31, 2025 9:30am - 9:35am IST
Moderator
Friday January 31, 2025 9:30am - 9:35am IST
Virtual Room A Pune, India

9:30am IST

Opening Remarks
Friday January 31, 2025 9:30am - 9:35am IST
Moderator
Friday January 31, 2025 9:30am - 9:35am IST
Virtual Room B Pune, India

9:30am IST

Opening Remarks
Friday January 31, 2025 9:30am - 9:35am IST
Moderator
Friday January 31, 2025 9:30am - 9:35am IST
Virtual Room C Pune, India

9:30am IST

Opening Remarks
Friday January 31, 2025 9:30am - 9:35am IST
Moderator
Friday January 31, 2025 9:30am - 9:35am IST
Virtual Room D Pune, India

9:30am IST

Opening Remarks
Friday January 31, 2025 9:30am - 9:35am IST
Moderator
Friday January 31, 2025 9:30am - 9:35am IST
Virtual Room E Pune, India

9:30am IST

AI-Powered Digital Stethoscope for Telemedicine Applications
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Sheela S. J, Rajeshwari B. S, Harsha M, Subhash T. D, Tejas H. S, Thanmaya Ganesh C. S, Harsha S. M, Keerthana T. V
Abstract - One of the leading causes of death Globally cardiovascular diseases (CVDs). 2019 key Statistics on CVDs is as follows: Total Deaths: 17.9 million people, 32% of global deaths, 85% of CVD deaths which approximately 15.2 million deaths from heart attacks and strokes. Hence, early diagnosis plays a crucial role in reduction of heart related diseases. Usually, the healthcare professionals collect the initial cardiac data using their quintessential instrument called stethoscope. Traditionally, these stethoscopes have significant drawbacks such as weak sound enhancement and limited noise filtering capabilities. Moreover, the low frequency signals such as below 50 Hz may not be heard because of the variation in sensitivity of a human ear. Hence, the usage of conventional stethoscopes requires experienced medical practitioners. In order to overcome these limitations, it is necessary to develop a device which is more sophisticated than conventional stethoscopes. In this context, the proposed work aims in the development of digital stethoscope which has the capability of displaying heart and lungs sound separately. Further, the proposed digital stethoscope permits to document, convert and transmit heart and lungs sounds to dB range digitally thereby reducing unnecessary travelling to medical facilities. The proposed stethoscope results are compared and validated with conventional techniques.
Paper Presenter
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room A Pune, India

9:30am IST

Artificial Intelligence in Medical Imaging
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Prem Gaikwad, Parth Masal, Mandar Kulkarni, Mousami P. Turuk
Abstract - Visual Language Models (VLMs) are an emerging technology that integrates computer vision with natural language processing, offering transformative potential for healthcare. VLMs significantly enhance disease detection, diagnosis, and report generation by enabling automated analysis and interpretation of medical images. These models are designed to support healthcare professionals by streamlining workflows, improving diagnostic accuracy, and assisting in clinical decision-making. Applications include early disease detection through image analysis, automated report generation, and integration with electronic health records (EHR) for personalized medicine. Despite their promise, challenges such as data privacy, interpretability, and the scarcity of labelled datasets remain. However, ongoing advancements in AI-driven medical systems and the integration of multimodal data can potentially revolutionize patient care and operational efficiency in healthcare settings. Addressing these challenges is crucial for realizing the full potential of VLMs in clinical practice.
Paper Presenter
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room A Pune, India

9:30am IST

Automated Polycystic Ovary Disease Diagnosis from Ultrasound Using Deep Convolutional Networks
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Kamini Solanki, Nilay Vaidya, Jaimin Undavia, Jay Panchal
Abstract - Polycystic ovary disease (PCOD) is a condition in which the ovaries of women of childbearing age produce too many immature or partially mature eggs. As time passes, these eggs develop into cysts within the ovaries. These cysts can lead to enlargement of the ovaries and an elevated production of male hormones (androgens). Consequently, this hormonal imbalance can result in a range of issues like fertility challenges, irregular menstrual cycles, unanticipated weight gain, and various other health complications. The associated symptoms often exert a long-term impact on both the physical and mental well- being of affected women. Statistics indicate that approximately 34% of individuals facing PCOD also grapple with depressive symptoms, while almost 45% experience anxiety. The primary object of this proposed framework is to detect and classify PCOD disease from standard X-ray pictures with assistance of volume datasets using deep learning model. Polycystic Ovary Disease (PCOD) significantly affects women's reproductive health, leading to various long-term complications. This work introduces a novel framework for automated PCOD detection using integrating Convolutional Neural Networks (CNN) with deep learning, applied to ultrasound imaging. Unlike traditional diagnostic methods, which rely on manual interpretation and are prone to subjectivity, the proposed system leverages the powerful feature extraction capabilities of CNNs to classify infected and non-infected ovaries with 100% accuracy. This high level of precision outperforms existing models and can be seamlessly integrated into clinical workflows for real-time diagnosis during sonography, facilitating early detection and improved fertility management. By focusing on a deep learning approach, this work provides a scalable, reliable, and automated solution for PCOD diagnosis, marking a significant advancement in the use of medical imaging with artificial intelligence.
Paper Presenter
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room A Pune, India

9:30am IST

Design and Implementation of Latent Fingerprints Using Weighted Hybrid Optimization Technique
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Poornima E. Gundgurti, Shrinivasrao B. Kulkarni
Abstract - Latent fingerprints play a crucial role in forensic investigations, driven by both public demand and advancements in biometrics research. Despite substantial efforts in developing algorithms for latent fingerprint matching systems, numerous challenges persist. This study introduces a novel approach to latent fingerprint matching, addressing these limitations through hybrid optimization techniques. Recognizing latent fingerprints as pivotal evidence in law enforcement, our comprehensive method encompasses fingerprint pre-processing, feature extraction, and matching stages. The proposed latent fingerprint matching utilizes a novel approach named as, Randomization Gravity Search Forest algorithm (RGSFA). Acknowledging the shortcomings of traditional techniques, our method enhances convergence speed and performance evaluation by integrating weighted factors. Precision, recall, F-measure, and recognition rate serve as performance metrics. The proposed approach has a high recognition rate of 99.9% and is successful in identifying and matching latent fingerprints, furthering the development of biometric-based personal verification techniques in forensic science and law enforcement. Experimental analyses, using publicly accessible low-quality latent fingerprints from FVC-2004 datasets, demonstrate that the proposed framework outperforms various state-of-the-art approaches.
Paper Presenter
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room A Pune, India

9:30am IST

Enhancing Cassava Disease Detection: Integrating Stacked CNNs with ResNet-18 Feature Extraction
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Krunal Maheriya, Mrugendra Rahevar, Martin Parmar, Deep Kothadiya, Arpita Shah
Abstract - Plant diseases pose a significant threat to agricultural output, causing food insecurity and economic losses. Early detection is crucial for effective treatment and control. Traditional diagnosis methods are labor intensive, time-consuming, and require specialized knowledge, making them unsuitable for large scale use. This study presents a novel approach for classifying cassava leaf diseases using stacked convolutional neural networks (CNNs). The proposed model leverages pre-trained ResNet-18 features to enhance feature learning and classification accuracy. The dataset includes images of cassava leaves with various diseases, such as Cassava Mosaic Disease (CMD), Cassava Green Mottle (CGM), Cassava Bacterial Blight (CBB), and Cassava Brown Streak Disease (CBSD). Our method begins with data preparation, including image augmentation to increase robustness and variability. The ResNet-18 model is then used to extract high-level features, which are then fed into a stacked CNN architecture made up of pooling layers, several convolutional layers, and non-linear activation functions. A fully connected layer is then used for classification. Experimental results demonstrate high accuracy in categorizing cassava leaf diseases. The proprietary stacked CNN architecture combined with pre-trained ResNet-18 features offers a significant improvement over conventional machine learning and image processing methods. This study advances precision agriculture by providing a scalable and effective method for early disease identification, enabling farmers to control diseases more accurately and promptly, thereby increasing crop yield. The findings point to the promise of deep learning techniques in agricultural applications and provide directions for further study to create more complex models for the classification and diagnosis of plant diseases.
Paper Presenter
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room A Pune, India

9:30am IST

Enhancing Consumer Decision Satisfaction in Agricultural Product Retailing through IoT
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Ruchi Tripathi, Anjan Mishra, Subrata Mondal, Arunangshu Giri, Dipanwita Chakrabarty, Wendrila Biswas
Abstract - Agricultural product shares a significant part of retail industry. The growing popularity of digital ecosystem can immensely affect agricultural sector as well. The consumers and retailers both can get benefited from Internet of Things or IoT, as it has a vast application in agricultural product retailing. IoT helps a retailer to establish an efficient supply chain with minimum wastage without compromising with quality. On the other side, it delivers authentic real-time information to the consumers, so that they can take efficient decision. This study has identified some factors that yields decision satisfaction to the consumers through application of IoT in agricultural product retailing sector.
Paper Presenter
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room A Pune, India

9:30am IST

ENHANCING SEARCH EFFICIENCY: A PERSONALIZED, PROFESSION-BASED APPROACH USING AIML-DRIVEN BROWSER EXTENSIONS
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Khush Zambare, Amol Wagh, Sukhada Mahale, Mayank Sohani
Abstract - In the all-digital world of today, the search engines are more of entry points to knowing most things. In this regard, most search engines often service the general user; most other needs, specific to a profession, go unattended. Use of "Amazon" will return results for the e-commerce giant, even when the user is the environmental scientist looking for something about the Amazon rainforest or the cloud developer searching for Amazon Web Services (AWS). This generic approach leads to inefficiencies as users need to sift through lots of useless information. This paper allows for a browser extension that personalizes the result of search on Artificial Intelligence and Machine Learning, with the aim of catering to individual users, based on their profession, interests, and specific needs. The solution dynamically re-ranks the search results as it learns from user behavior and search patterns to provide the most relevant information to save the precious time of the users. The paper will discuss current trends in SEO, AIML applications, and personalization techniques to outline how this solution can revolutionize the search engine experience.
Paper Presenter
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room A Pune, India

9:30am IST

Harnessing AI for Personalized Training: Opportunities and Challenges
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Suruchi Pandey, Hemlata Vivek Gaikwad
Abstract - The rapid shift in the integration of AI in various sector for more personalized and efficient training. This research explores into the potential of AI in various training methods, the challenges and vast opportunities of learning and growth while using it. The potential for AI-driven training is vast, spanning fields like corporate, healthcare, education, and the military. This study examines how emerging technologies like virtual reality, augmented reality, and simulation-based training can personalize learning experiences, enhance skill development, and provide real-time feedback. It also addresses critical challenges to implementing AI in training, such as costs and data privacy concerns. Additionally, the paper discusses how AI-enabled training could transform traditional learning and development practices, opening up new possibilities for advanced, adaptive learning methods.
Paper Presenter
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room A Pune, India

9:30am IST

Inventory Prediction and Waste Management
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Shripad Kanakdande, Atharva Kanherkar, Ayush Dhoot, P.B.Tathe
Abstract - Efficient inventory forecasting and waste management are essential for streamlining supply chains and cutting expenses, particularly in sectors like retail and food services where inadequate stock management can lead to large losses and environmental damage. This study presents a data-driven approach to inventory prediction that makes use of sophisticated machine learning models that evaluate past data, sales patterns, and seasonal fluctuations. The model seeks to increase demand forecasting accuracy by utilizing predictive skills, which would ultimately result in improved stock management and customer satisfaction. In order to help organizations reduce waste and increase resource efficiency, it also focuses on improving waste management through real-time monitoring and forecasting of surplus inventory. Furthermore, combining sustainable practices with predictive analytics promotes long-term corporate viability while minimizing environmental harm. In addition to increasing operational effectiveness, this all-encompassing strategy supports more general environmental sustainability goals. The suggested framework gives businesses a practical way to optimize and streamline their supply chain operations while fulfilling sustainability goals by offering a complete solution that can minimize the ecological footprint and the costs associated with keeping inventory.
Paper Presenter
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room A Pune, India

9:30am IST

Multi-Class Ship Classification of Commercial and Naval Vessels using Convolutional Neural Network
Friday January 31, 2025 9:30am - 11:30am IST
Authors - U.Sakthi, Aman Parasher, Akash Varma Datla
Abstract - This work seeks to classify various ship categories on the high-resolution optical remote sensing dataset known as FGSC-23 using deep learning models. The dataset contains 23 types of ships, but for this study, six categories are selected: Medical Ship, Hovercraft, Submarine, Fishing Boat, Passenger Ship and Liquified Gas Ship. The adopted ship categories were thereafter used to train four deep learning models which included VGG16, EfficientNet, ResNet50v2, and MobileNetv2. The accuracy, precision, and AUC parameters were used to evaluate the models where the best one, the ResNet50v2, was set up as accurate. Using these models, it should be possible to achieve a practical deployment aiming at fine-grained classification of ships that will contribute to enhancing maritime surveillance techniques. ResNet50v2 model had the highest precision of 0.9058 and on the other hand MobileNetv2 had the highest AUC of 0.9932. The analysis of the identified models is performed further in this work to illustrate their advantages and shortcomings in adherence to fine-grained ship classification tasks. Based on this research, the practical implications transcend theoretical comparisons of performance metrics, as useful information is provided to improve security applications in the maritime domain, surveillance, and monitoring systems. Categorization and identification of ships is a very important process in going global maritime business because it is used in decision-making processes in fields like security and surveillance, fishing control, search and rescue and conservation of the environment. The models highlighted are namely ResNet50v2 as well as MobileNetv2, proved to be robust in real-time applications such scenarios because of their ability to accurately and proficiently distinguish the differences between the ship types. In addition, this study suggests the luminal possibility of doing further improvement on these models using data enhancement strategies like transfer learning, data augmentation, and hyperparameter optimization which would enable it to perform impressively on any other maritime datasets. Therefore, the outcomes are beneficial for furthering work in automated ship detection and classification and is important toward enhancing the overall effectiveness and safety of navies across the globe.
Paper Presenter
avatar for U.Sakthi
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room A Pune, India

9:30am IST

BrightMind: AI Interview or Test Taker Bot
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Sandeep Shinde, Parth Kedari, Atharva Khaire, Shaunak Karvir, Omkar Kumbhar
Abstract - With the use of cutting-edge technologies like Flask, web technology, API rendering, Make It Talk, and machine learning (ML), an AI smart tutor bot is being implemented with the goal of giving users an engaging and customized learning experience. The bot uses machine learning techniques to analyze responses and generates quiz-style questions with multiple-choice possibilities and extended answers. This allows for quick feedback. Additionally, it has an interview mode in which the user engages with an AI avatar that reads their body language and facial emotions. Using written material and specialized alphabets, the AI avatar is dynamically educated, gaining comprehensive knowledge and an accurate evaluation of user performance. The research article goes into detail about the system architecture, how different technologies were integrated, and the process for training the avatar and gauging user response. Through user feedback and experimental trials, the AI Smart Tutor Bot's performance is assessed, showcasing its potential as an advanced teaching tool that can adapt to each student's unique learning needs while boosting comprehension and engagement.
Paper Presenter
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room B Pune, India

9:30am IST

Exploring Learning algorithm to Qualitatively Assess Medicinal Plants
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Bhagyashree D. Lambture, Madhavi A. Pradhan
Abstract - Phytochemical qualities, geographic information, environmental conditions, and traditional medicinal knowledge are some of the sources of information that are incorporated into this research project, which presents a comparative examination of machine learning (ML) algorithms for the qualitative evaluation of medicinal plants. In order to categorize and forecast the medicinal value of plants based on multi-modal data, the purpose of this study is to investigate the effectiveness of various machine learning algorithms. For the purpose of determining which method is the most effective for evaluating complicated and diverse datasets, a full evaluation is carried out utilizing well-known machine learning models. These models include decision trees, random forests, support vector machines, and deep learning algorithms. Key criteria including as accuracy, precision, recall, F1-score, and computing efficiency are utilized in order to evaluate the levels of performance achieved by each method. For the purpose of gaining a deeper comprehension of the role that each data source plays in determining the medicinal potential of plants, the value of features and their interpretability are also investigated. A basis for the ongoing development of AI-driven tools in pharmacological research and plant-based drug discovery is provided by the findings of this comparative analysis, which offer vital insights into the usefulness of machine learning for medicinal plant assessment. Contributing to the expanding fields of computational botany and natural product science, the purpose of this study is to improve the precision and effectiveness of the evaluation of medicinal plants.
Paper Presenter
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room B Pune, India

9:30am IST

Fraud Detection in Insurance Using Machine Learning
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Azhar Abbas, Farha
Abstract - Fraudulent claims in the insurance industry lead, to significant financial losses and negatively affect both policyholders and insurance firms. Machine learning has proven to be revolutionizing fraud detection since it is more than just performing the ordinary rule-based systems while automating and optimizing detection processes. The current work proposes a novel hybrid approach that combines supervised and unsupervised techniques in machine learning with applications in accurately and robustly detecting insurance fraud. Three primary models include in the framework are Decision Tree, Random Forest, and Voting Classifier, which improve detection performance on real-world datasets. In addition, an embedding-based model interprets sequential claims data, and a statistically validated network is used to detect patterns of collusion and fraud among related entities. Extensive experimentation was conducted using large-scale motor, and general insurance datasets and showing that the proposed hybrid model achieved an accuracy of 89.60%. Hyperparameter tuning and data preprocessing were used to further refine the model's performance; it was able to counterbalance all issues brought forth by imbalances, complexities, and complexities due to variations in fraud types. The methodology outperformed the existing models, better at identifying rare sophisticated cases of fraud. The practical implications of deploying machine learning models in the insurance sector are also discussed from the angle of best practices for data governance, model interpretability, and stakeholder trust. In Future this work will be improved by incorporating real-time analytics to provide quicker detection, enhancing interpretability features, and adapting the model to emerging fraud patterns in evolving data environments.
Paper Presenter
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room B Pune, India

9:30am IST

MRI-Based Parkinson's Disease Diagnosis with Deep Learning
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Poonam Yadav, Meenu Vijarania, Meenakshi Malik, Neha Chhabra, Ganesh Kumar Dixit
Abstract - Parkinson's disease is aging-associated degenerative brain illness that results in the degeneration of certain brain regions. Early medical diagnosis of Parkinson's disease (PD) is difficult for medical professionals to make with precision. Magnetic Resonance Imaging (MRI) and single-photon emission computed tomography, or SPECT are two medical imaging strategies that can be used to non-invasively and safely assess quantitative aspects of brain health. Strong machine learning and deep learning methods, along with the efficiency of medical imaging techniques for evaluating neurological wellness, are necessary to accurate the identification of Parkinson's disease (PD). In this study, we have used dataset of MRI images. This study suggests three deep learning models: ResNet 50, MobileNetV2 and InceptionV3 for early diagnosis of PD utilizing MRI database. From these three models, MobileNetV2 demonstrated superior accuracy in training, testing and validation with a rate of 99%, 94%, and 96%, respectively. With its effectiveness and precision, MobileNet V2 has a lot of potential for PD identification using MRI scans in the future. We may further advance the development of dependable and easily accessible AI-powered solutions for early diagnosis and better patient care by tackling the issues and investigating the above-mentioned future paths.
Paper Presenter
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room B Pune, India

9:30am IST

Real-Time Interaction with Machines through Gesture and Speech Translation: A CNN and LSTM-Based Approach
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Aye Thiri Nyunt, Nishi Vora, Devanshi Vaghela, Brij Kotak, Ravi Chauhan, Kirtirajsinh Zala
Abstract - This paper is an AI and Machine Learning Algorithm - based dualistic Gesture-to-Speech and Speech-to-Gesture framework. The core of this initiative is to enable machines and humans to converse with each other by enabling the translation of physical body movements into reasonable speech and vice versa. We used deep learning models- Convolutional Neural Networks (CNN)- to train our system using a dataset consisting of human gestural movements and the relevant speech patterns. For the Gesture-to-Speech module, real-time gesture recognition and interpretation were used, which involved computer vision and were implemented to interpret gestures into speech output containing words and phrases representing the message illustrated by the gestures. The Speech-to-Gesture module, on the other hand, uses speech as input to produce context-related gestures-the main purpose of which is to improve user interaction and experiences. In the system, multiple applications were tested, including sign language and webcams. Further research will try to extend the flexibility of the system to include various languages, cultural backgrounds and characteristics of individual gesture styles which eventually has a high level of customization. We had designed the CNN architecture for real-time gesture recognition and taken care of data preprocessing as well to increase accuracy concerning different types of gestures. We created Gesture-to-Speech translation with the use of an LSTM, then added in a Text-to-Speech engine for it to have a very natural sound. We then worked on Speech-to-Gesture and even refined the gestures through a CNN-based network, to ensure transitions are very fluid. Everything was coordinated such that there would be synchronous gestures and speech for extremely natural real-time interaction. We coached on how one would integrate, test, and further optimize models with dropout and batch normalization for higher performance.
Paper Presenter
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room B Pune, India

9:30am IST

Small Language Models:An Advancing Efficient Open-Source Alternatives to Large Language Models
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Varun Maniappan, Praghaadeesh R, Bharathi Mohan G, Prasanna Kumar R
Abstract - This paper constitutes a comprehensive review of how language models have changed, focusing specifically on the trends toward smaller and more efficient models rather than large, resource-hungry ones. We discuss technological progress in the direction of language models applied to attention mechanisms, positional embeddings, and architectural enhancements. The bottleneck of LLMs has been their high computational requirements, and this has kept them from becoming more widely used tools. In this paper, we outline how some very recent innovations, notably Flash Attention and small language models (SLMs), addressed these limitations by paying special attention to the Mamba architecture that uses state-space models. Moreover, we describe the emerging trend of open-source language models, reviewing major technology companies efforts such as Microsoft’s Phi and Google’s Gemma series. We trace here the evolution from early models of transformers to the current open-source implementations and report on future work to be done in making AI more accessible and efficient. Our analysis shows how such advances democratize AI technology while maintaining high performance standards.
Paper Presenter
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room B Pune, India

9:30am IST

STOCK MARKET PREDICTION USING MACHINE LEARNING MODELS
Friday January 31, 2025 9:30am - 11:30am IST
Authors - S.K. Manjula Shree, Shreya Vytla, J. Santharoopan, Harisudha Kuresan, A.Anilet Bala, D.Vijayalakshmi
Abstract - The goal is to use a Random Forest classifier to categorize future price movements as "up" or "down" in order to forecast stock market trends. In order to guide investing strategies, this model will examine pertinent attributes and previous stock data. The effectiveness of Logistic Regression, Support Vector Machines (SVM), and Random Forest Classifier in forecasting stock market movements is compared in this study. The ensemble approach Random Forest is very resilient under erratic market situations since it is excellent at handling noisy, complex data and capturing non-linear patterns. SVM performs best on smaller, more structured datasets, however noise and non-linearity might be problematic. Despite its simplicity and interpretability, logistic regression is constrained by its linear character and finds it difficult to account for the dynamic, non-linear behavior of stock prices. In recall focused tasks, logistic regression is helpful because it performs well in identifying true positives (such preventing missed opportunities in stock predictions). SVM's reliance on kernel functions makes it computationally expensive, but it can also be helpful when handling smaller datasets with clear patterns and where accuracy is needed. All things considered, Random Forest offers the greatest results around 99% especially for difficult stock market prediction assignments.
Paper Presenter
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room B Pune, India

9:30am IST

STOCK PREDICTION USING MACHINE LEARNING TECHNIQUES
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Umakant Singh, Ankur Khare
Abstract - This paper aims to find the optimal model for stock price forecast. In examining the different approaches and aspects that need to be considered, it is exposed the methods decision tree and Gradient Boosted Trees Model. This paper aims to propose a more practical approach for making more accurate stock predictions. The dataset including the stock bazaar values from the prior year has been considered first. The dataset was optimized for actual analysis through pre-processing. Therefore, the preprocessing of the raw dataset will also be the main emphasis of this work. Again, decision trees and gradient tree models are used on the pre-processed data set and the results thus obtained are analyzed. In addition, forecasting papers also address issues related to the usage of forecasting systems in actual situations and the correctness of certain normal value. This paper also presents a machine learning model for predicting stock stability in financial markets. Successful stock price forecasting greatly benefits stock market organizations and provides real solution to problems faced by investors.
Paper Presenter
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room B Pune, India

9:30am IST

Utilizing Object Detection and Lane Assistance to Optimize Visibility in Foggy Conditions: A Review
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Aneesh Kaleru, Chaitanya Neredumalli, Mrudul Reddy, Ramakrishna Kolikipogu
Abstract - One major risk factor that contributes to traffic accidents globally is poor visibility in foggy situations. Drivers are seriously threatened by fog because it weakens contrast, hides important objects, and makes lane markings almost invisible. Recent developments in visibility enhancement methods for foggy circumstances are summarized in this paper, with a focus on picture defogging combined with object detection and lane aid. We analyze the application of models such as Conditional Generative Adversarial Networks (cGANs), Single Shot Multibox Detectors (SSD), All-in-One Defogging Network (AODNet), and You Only Look Once (YOLO) from the perspective of deep learning and computer vision. These methods have the potential to increase driver safety in inclement weather by identifying impediments, improving visibility, and offering lane guidance. The review also covers the limitations of these solutions, such as computational demands and requirements for real-time processing. Our goal is to provide researchers and practitioners with a comprehensive understanding of the current methods and their uses, allowing for the development of effective visibility enhancement systems that can prevent accidents and save lives.
Paper Presenter
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room B Pune, India

9:30am IST

VEDA: Visual Extraction and Decryption of Ancient Scripts
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Sindhu C, Taruni Mamidipaka, Yoga Sreedhar Reddy Kakanuru, Summia Parveen, Saradha S
Abstract - India is a country with very rich ancient historical legacy. It preserved vast cultural and linguistic knowledge through stone inscriptions. Extracting text from ancient stone inscriptions and translating it into a language which is understandable by everyone is a very challenging task due to script variations, natural wear, and the uneven degraded surfaces of stone carvings. Our idea is to build a model which can extract the text from these stone inscriptions which were written in Telugu language and translate them into other Indian local languages. The Region-Based Convolutional Neural Network (R-CNN) model which is integrated with Tesseract OCR is trained on a custom dataset of 30,000 labelled images of Telugu script, encompassing Achulu (vowels), Hallulu (consonants), and Vathulu. By achieving a 96% accuracy in character detection, this model demonstrates significant reliability in recognizing Telugu characters from degraded and complex inscriptions. Data augmentation techniques, including rotations, flips, and shifts were used to further enhance the model’s robustness to different orientations and environmental conditions encountered in historical artifacts. The text which is extracted from the image is ultimately translated into Indian local languages using an API-based translation module, enabling a seamless interpretation of ancient content. This research contributes a comprehensive and automated solution for cultural preservation, giving us a scalable method to digitize and make historical inscriptions accessible to everyone which are integral to Telugu heritage and linguistic history.
Paper Presenter
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room B Pune, India

9:30am IST

A Comparative Analysis of Intrusion Detection System Models and Suitability of Datasets for Smart Grid Communication
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Nisarg Dobariya, Rutik Dobariya, Rikita Chokshi, Sarita Thummar
Abstract - The transition from traditional to smart grids has been driven by the pursuit of greater efficiency, reliability, and consumer engagement. While smart grids offer numerous benefits, they are vulnerable to cybersecurity threats. Intrusion detection systems (IDS) are indispensable tools for safeguarding smart grid operations by identifying and preventing malicious attacks. This research investigates the application of various IDS models, classifiers, datasets, and algorithms in smart grid environments. The study underscores the importance of using datasets specifically designed for smart grid networks to ensure accurate and reliable IDS performance. Moreover, the research demonstrates the potential of distributed approaches and advanced algorithms in enhancing IDS capabilities, thereby bolstering the security and resilience of smart grid infrastructure.
Paper Presenter
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room C Pune, India

9:30am IST

A Comprehensive Survey on AI-based System for Detecting Package Damage and Food Packet Spillage
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Revathy P, Rakshana A, Tinu A V, Vijayakumar R
Abstract - The increasing demand for efficient package delivery has led to a challenge in detecting food spills during transit. Traditional methods rely on manual inspection, which is time-consuming and prone to human error. This study proposes an AI-based approach using Convolutional Neural Networks (CNNs) implemented with TensorFlow to detect both damaged packages and spilled food packets. The model is trained on a large dataset of package and food packet images, learning key features indicative of physical damage and identifying food spills. By fine-tuning pre-trained CNN architectures, the model achieves high accuracy in detecting both damage and spills. The interface is attached with an alert mechanism that notifies when damage or spill is detected. The TensorFlow framework is used for building, training, and deploying the model efficiently. The proposed system aims to automate package and food packet inspection, reduce human labor, and improve delivery service reliability by providing fast and accurate damage and spill detection.
Paper Presenter
avatar for Tinu A V
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room C Pune, India

9:30am IST

Behaviour Based Driver Drowsiness Detection Using Convolutional Neural Network
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Smita Mahajan, Archana Chaudhari, Ameysingh Bayas, Devika Shrouti
Abstract - Drowsiness is a critical issue that contributes to a significant number of accidents in various scenarios, such as driving and hazardous work environments. Existing drowsiness detection projects often rely on subjective measures and single modality detection, leading to limited accuracy and applicability. This research proposes a drowsiness detection system that employs deep neural networks and machine learning-based object detection techniques to overcome these limitations. The ability of the recent drowsiness detection systems to reliably and impartially detect drowsiness is restricted. The proposed model uses computer vision and machine learning algorithms to identify drivers' drowsiness based on facial attributes like eye movement monitoring. The model aims to improve the accuracy and reliability of drowsiness detection by combining multiple modalities. The implementation includes using the Keras library, which is required for a Convolutional Neural Network (CNN) architecture. The model is trained on a customized dataset of facial images with open or closed eyes labels. The CNN discovers the complex relationships and features from the data, classifying drowsiness critically. The proposed drowsiness detection system's results demonstrate an optimistic accuracy of 98.88%. The system signals real-time alerts when the drowsiness in the behavior of the driver is caught, potentially averting accidents and enhancing safety. This technique suggests an accurate and trustworthy approach for detecting drowsiness in different domains, including driving and unsafe work environments, with 98.88% accuracy. This system can be a valuable means for improving safety and controlling the accidents caused by driver drowsiness.
Paper Presenter
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room C Pune, India

9:30am IST

Clustering-Driven Insights for Recommending Ideal Student Locations
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Aswathkrishna S D, M. Sujithra
Abstract - In today's rapidly evolving world, recognizing student food choices is crucial. This study explores food choices and how they align with areas containing restaurants and grocery stores. Clustering techniques including K-Means, Hierarchical Clustering, and DBSCAN were employed with the silhouette score used to validate and determine the most effective method for analysis. Based on food choices data sourced from Kaggle and location data from the Foursquare API, the research provides location recommendations for students. Suggestions guide students to areas that align with their food choices aiming to enhance their overall experience.
Paper Presenter
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room C Pune, India

9:30am IST

Ethical, Security, and Privacy Considerations for Internet of Medical Things Adoption for Developing Countries
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Ofaletse Mphale, V. Lakshmi Narasimhan, S. Sasikumaran
Abstract - The Internet of Medical Things (IoMT) presents transformative potential for healthcare by enabling real-time patient monitoring, advanced diagnostics, and personalized treatments. However, its adoption in developing countries is hindered by significant ethical, security, and privacy challenges. Studies focused on developing countries often identify these challenges but rarely propose rigorous frameworks for successful adoption. This study employs a desktop search methodology to comprehensively review the existing literature, identifying crucial ethical, security, and privacy concerns associated with the IoMT adoption. Through this analysis, the study proposes potential mitigation strategies and a framework to facilitate the effective adoption of IoMT in developing countries. Findings will support healthcare decision-makers and policymakers in developing countries, enabling them to devise strategies that ensure ethical practices, secure patient data, and safeguard privacy in healthcare IoT integration. This will lead to improved healthcare delivery and enhanced patient outcomes.
Paper Presenter
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room C Pune, India

9:30am IST

Intelligent Phishing Detection Using GANs
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Priyal Donda, Vatsal Upadhyay, Janhavi Gulabani, Sharvari Patil, Vinaya Sawant
Abstract - Phishing is increasingly being one of the frequent cyber-attacks. Since this trend has seen the incidence increased significantly in the last few years, people and organizations have been highly affected by data breaches and financial losses. Such growth only increases the demand for effective mechanisms of defense, as traditional approaches of machine learning like SVM, Random Forest, and Long Short-Term Memory networks often fail to detect phishing attempts with accuracy. SVMs can be computationally expensive, sensitive to noise, and require careful selection of kernel functions, while LSTMs are complex, prone to overfitting, and require substantial amounts of labeled data. In light of these limitations, the use of GANs has been recent in order to improve detection capabilities. GANs create realistic phishing URLs that advanced detection models struggle to distinguish, using semi-supervised training to differentiate between adversarial and legitimate URLs. Specifically, this holistic approach grapples with the sophistication of phishing attacks and places an emphasis on adaptive defense, since it has changed the basis for detection from content-based to URL-based techniques. Finally, these novel approaches introduce a promising pathway for the mitigation of phishing risks and sensitive information safeguarding, thus building security strength in the digital world.
Paper Presenter
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room C Pune, India

9:30am IST

Internet of Things (IoT) in Retail Industry
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Amol Mashankar, Smita Kalokar
Abstract - The retail business marketplace is experiencing a significant shift, with a growing emphasis on the innovations brought by internet of things (IoT) technology. The retail aspect is rapidly evolving, driven by new improvements in internet technology, which play a important role in the transformation of the retail sector. The new updation involves continuously adapting to the fast-paced changes within the retail environment. New techniques and innovations are emerging daily to better address customer needs and satisfaction preferences. This paper focus on to explore the practices and performance of IoT technology in the retail sector. It also emphasis an analytical framework for evaluating the approaches to IoT technology practices and their effectiveness in retail stores.
Paper Presenter
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room C Pune, India

9:30am IST

Rainfall Prediction Using Machine Learning
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Bakka Vamshi, Munnuru Umakanth, Kadwasra Swapna, Punuru Venkata Usha Sree, Mannepalli Rohini Sri, Sushama Rani Dutta
Abstract - Predicting heavy rainfall remains a significant challenge for meteorological departments as it greatly impacts economies and human lives. Severe rainfall can result in natural disasters like floods and droughts, impacting millions of people globally every year. Precise rainfall prediction is especially important for nations like India, where agriculture serves as a key economic pillar. Due to the atmosphere’s dynamic nature, statistical methods often fall short in achieving high prediction accuracy. The complex, nonlinear characteristics of rainfall data make Artificial Neural Networks a more effective method. This paper reviews and compares various methods and algorithms employed by researchers for rainfall forecasting, presenting the findings in a tabular format to make these techniques accessible to non-specialists.
Paper Presenter
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room C Pune, India

9:30am IST

Real-Time Air Quality Monitoring and Predictive Pollution Control Using Big Data and IoT
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Rajitha Kotoju, Sugamya Katta, Abrar Khan
Abstract - Real-time air quality monitoring and predictive pollution control are critical for addressing escalating environmental and public health challenges, particularly in low-income areas with limited infrastructure. This paper explores the integration of Big Data analytics and IoT to develop cost-effective, scalable solutions for real-time air quality assessment. The proposed framework aims to identify pollution patterns, predict air quality trends, and provide actionable insights for policymakers. A unique feature of this study is its emphasis on low-cost sensor deployment and edge-computing techniques to ensure accessibility in resource-constrained settings. The interdisciplinary approach combines environmental science, AI, and public health perspectives to establish a holistic framework for data collection, analysis, and decision-making. Additionally, this paper addresses the integration of findings into policy frameworks by proposing data-driven recommendations for urban planning, industrial regulation, and community health interventions. The results demonstrate significant advancements in predictive accuracy and actionable intelligence generation while minimizing implementation costs.
Paper Presenter
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room C Pune, India

9:30am IST

The Smart Footwear : A Survey Report
Friday January 31, 2025 9:30am - 11:30am IST
Authors - D.K. Chaturvedi, Nisha Verma
Abstract - Artificial Intelligence and Machine Learning (AIML) are quickly proceeding in many areas. These technologies, including the smart footwear (SF) industry, have significantly impacted the consumer goods market. AIML are widely used in the design and production of SF. There are different applications of SF such as healthcare SF, assistive SF for old age persons or impairments, navigating footwear for unknown areas, mobility and gait analysis, safety footwear, anti-skid footwear, footwear for army personnel, and power generated footwear etc. The SF helps in acquisition of real-time data of patients to monitor and suggest suitable treatment. Besides these, SF can be classified based on the different architecture and processing techniques. This paper includes different research studies conducted in the past on various tools and techniques used to create smart footwear for different applications.
Paper Presenter
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room C Pune, India

9:30am IST

A Comparative Analysis of Machine Learning Models
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Kajal Joseph, Deepa Parasar
Abstract - This study conducts a predictive analysis of company status using various machine learning algorithms, aiming to identify the models that deliver the highest accuracy and reliability for decision-making in finance and business intelligence. The study employs a range of algorithms, including Logistic Regression, DecisionTreeClassifier, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naive Bayes, Gradient Boosting Machines (GBM), XGBoost, AdaBoost, LightGBM, CatBoost, and Extra Trees Model, each rigorously tested on a preprocessed dataset split into training and testing sets to ensure robust validation. (Kunjir et al., 2020) Results indicate that ensemble models, particularly XGBoost and Random Forest, outperformed other methods, achieving accuracy rates exceeding 93%. This high level of performance highlights the value of ensemble techniques for handling complex predictive tasks, showcasing their suitability for applications where precise forecasting is critical. The study underscores the importance of model selection in predictive analytics, as it directly impacts the reliability of predictions in financial contexts. These findings suggest that machine learning, especially ensemble models like XGBoost and Random Forest, can significantly improve the accuracy of company status predictions, offering a dependable tool for stakeholders operating in uncertain environments. This research contributes valuable insights into the efficacy of machine learning in predictive tasks, advocating for data-driven decision-making approaches that can enhance business intelligence and strategic planning. (Liaw et al., 2019)
Paper Presenter
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room D Pune, India

9:30am IST

A highly secure video steganography method utilizingFRT and ECC-ChaCha20 based encryption
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Meenu Suresh, Tonny Binoy, Saritha M S, Vimal Babu P, Dheeraj N, Aiswarya R Lakshmi
Abstract - The present work introduces a video steganography technique which employs Finite Ridgelet Transform (FRT) and Elliptic Curve Cryptography (ECC)-ChaCha20 encryption to hide confidential information. The proposed method begins by identifying key frames through the detection of scene changes. The FRT is employed to analyze the key frames, extracting their orientation and subbandswithin which the secret data is encoded. To boost security, ECC-ChaCha20 encryption technique serves as a preprocessing step prior to incorporating the secret data. The technique attains an embedding capacity of 72%, SSIM of 0.9890 and PSNR value range from 70dB and 72 dB. The experimental results highlight that the algorithm besidesboosting security also ensures superior resilienceand video quality.
Paper Presenter
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room D Pune, India

9:30am IST

Availability Evaluation in a Thermal Power Plant using Markov Birth-death Approach
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Jagriti Singh Chundawat, Ashish Kumar, Monika Saini
Abstract - The purpose of this paper is to optimize the availability of a thermal power plant. A thermal power plant (TPP) is a comprehensive system with multiple interconnected subsystems which are used for power generation. This TPP system has three subsystems such boiler, superheater and reheater. These subsystems connect to each other in series configuration. To improve the availability of the system a study-state availability is derived with the help of normalizing equations and the chapman Kolmogorov equations are derived from Markov birth-death process. The system’s failure and repair rates are statistically independent and exponentially distributed. The numerical results show that availability increases from 0.997903 to 0.998725 as the repair rate increases.
Paper Presenter
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room D Pune, India

9:30am IST

Design and Implementation of Face Image-Based Liveness Detection Using Deep Learning
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Mannem Sri Nishma, Satendra Gupta, Tapas Saini, Harshada Suryawanshi, Anoop Kumar
Abstract - Face recognition-based authentication has become a critical component in today's digital landscape, particularly as most business activities transition to online platforms. This is especially evident in the finance and banking sectors, which have shown significant interest in adopting online processes. By leveraging this technology, these industries can enhance operational efficiency, promote business growth, reduce reliance on manpower, and automate several processes effectively. However, face recognition systems are susceptible to face spoofing attacks, where malicious actors can attempt to deceive these systems using facial images or videos. Some attackers even use masks resembling authorized individuals to trick recognition cameras into perceiving them as real users. To counter such threats, liveness detection has emerged as a critical research area, focusing on identifying and preventing face spoofing attempts. The proposed approach utilizes a deep learning technique tailored for face liveness detection. The experiments are conducted using the Replay-Mobile, MSU-MFSD, Casia-FASD and our own datasets, which are widely used for recognizing live and spoofed faces. The system achieved an impressive area under the ROC curve (AUC) of 0.99, demonstrating its effectiveness in detecting face spoofing.
Paper Presenter
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room D Pune, India

9:30am IST

Indian Sign Language to Audio-Video Converter for Regional Languages
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Kusuma B S, Meghana Murthy B V, Preksha R, Srushti M P, C Balarengadurai
Abstract - Against the backdrop of either a Deaf World or hearing people, the major challenges which face modern society concern communication barriers in general. The paper proposes a system for translation through gestures in Indian Sign Language to audio and video outputs for non-signers to enable easy interaction with them. Advanced machine learning techniques, such as Support Vector Machine and Convolutional Neural Network, will be used to enable this tool to recognize motions of ISL in real time. It converts these into the correct format for video and audio. In this respect, the paper claims to "make communication more accessible and bridge the gap in communication in which gestures are recognized and translated." Real-time recognition algorithms overcome the challenges faced by hand gesture detection to provide an intuitive and seamless interaction experience. This approach is an effective strategy to enhance communications in government and industry with special focus on smart writing. Results confirm this method's promise in the broader social interaction by significantly improving the speed and accuracy of deaf individuals.
Paper Presenter
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room D Pune, India

9:30am IST

Study on the Effects of Memory on Learning in Neural Networks
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Bitan Pratihar
Abstract - We, human-beings, have two different forms of memory, namely pulling memory and pushing memory (also known as working memory). A pure pulling memory pulls a person towards itself, and consequently, he/she spends some significant amount of time on memorizing the incident but does not gain anything significant in his/her decision making directly. On the other hand, a pure pushing memory pushes a human-being to take some decisions, and thus, it may have direct influence on his/her learning. However, neither pure pulling memory nor pure pushing memory alone may be beneficial to effective learning of human brain. A proper combination of pulling and pushing memories may be required to ensure a significant effect of memory on learning of neural networks. The novelty of this study lies with the fact of formulating it as an optimization problem and solving the same using a recently proposed nature-inspired intelligent optimization tool. The effectiveness of this novel idea of correlating the combined form of memory with learning of neural networks has been demonstrated on two well-known data sets. This combined form of memories is found to have a significant influence on learning of neural networks, and this proposed approach may have the potential to solve the well-known memory loss problem of neural networks.
Paper Presenter
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room D Pune, India

9:30am IST

The Impact of Privacy Regulations on Digital Marketing Practices: A Descriptive Study
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Martin Mollay, Deepak Sharma, Pankajkumar Anawade, Chetan Parlikar
Abstract - The primary research intention of the present study is to find out the impacts of laws such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) on the digital marketing landscape. The set of regulations relates to data protection, which involves a stringent regime for how the firms gather, process, and hold the privacy of their clients. Therefore, the two main bottlenecks of marketers are fewer consent mechanisms, less data, and a need for more options for personalizing. Still, technology is a fashionable thing that has been launched, and the effectiveness of the new technology revitalizes it. Firms mostly turn to first-party data that question the need for intermediaries. This means that they can collect information directly from the consumer, which then naturally results in much more productive and meaningful customer relation-ships. Getting hold of advanced technologies, for instance, artificial intelligence and machine learning, which work with smaller datasets, also provides a window for companies to discover a large number of customized, and possibly even more valuable, aspects through customer behavior without invading the privacy of the person who is identical to the threat of the law. Additionally, the price of compliance with the regulations is high, notably for Small and Medium sized Enterprises (SMEs). In contrast, it is the most highly cost-effective way for the consumer to win consumers’ trust in the brand and make them loyal to it in the long run. In this new era, ethical marketing follows the footsteps of the evolutionary journey where complete openness and consumers’ private space value are the main topics. Personal data can be acquired in a way that is not compliant with privacy laws. However, zero-party data or consumer information given to businesses might still be the source for personalized experiences that are privacy compliant.
Paper Presenter
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room D Pune, India

9:30am IST

The Next Frontier in Cancer Diagnosis: A Thorough Examination of Machine Learning and Deep Learning Advancements
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Sachi Joshi, Upesh Patel
Abstract - Cancer is a grave category of illnesses in which the body's aberrant cells proliferate and spread uncontrollably. It can appear in nearly every tissue or organ and take many different forms, each with its own distinct set of symptoms and side effects. Environmental variables, lifestyle decisions, and genetic abnormalities are typically linked to the development of cancer. The varied approaches to cancer diagnosis are examined in this study, with a focus on early detection and therapeutic strategies. This literature review covers a wide range of cancer kinds, such as brain tumours, leukaemia, breast, lung, and cervical cancer, and offers recommendations for creating reliable ma-chine learning-enhanced cancer detection techniques. The research elucidates several applications, techniques, and comparative analysis in this significant subject, ranging from imaging analysis to biomarker identification. The study explores the developing methods that lead to a more precise diagnosis. The study offers insights with a thorough examination of the benefits, drawbacks, and innovations of each technique, ranging from conventional diagnostic procedures to state-of-the-art technologies. It also directs future research efforts towards the hunt for more effective personalized illness management.
Paper Presenter
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room D Pune, India

9:30am IST

Topic Modelling in Hindi using BERT, LDA, LSA and NMF approaches
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Akshay Honnavalli, Hrishi Preetham G L, Aditya Rao, Preethi P
Abstract - In todays information-driven world, organizing vast amounts of textual data is crucial. Topic modelling, a subfield of NLP, enables the discovery of thematic structures in large text corpora, summarizing and categorizing documents by identifying prevalent topics. For Hindi speakers, adapting topic modelling methods used for English texts to Hindi is beneficial, as much of the research has focused primarily on English. This research addresses this gap by focusing on Hindi language topic modelling using a news category dataset, providing a comparative analysis between traditional approaches like LDA, LSA, NMF and BERT-based approaches. In this study, six open-source embedding models supporting Hindi were evaluated. Among these, the l3cube-pune/hindi-sentence-similarity-sbert model exhibited strong performance, achieving coherence scores of 0.783 and 0.797 for N-gram (1,1) and N-gram (1,2), respectively. Average coherence scores of all embedding models significantly exceeded traditional models, highlighting the potential of embedding models for Hindi topic modelling. Also, this research introduces a novel method to assign meaningful category labels to discovered topics by using dataset annotations, enhancing the interpretation of topic clusters. The findings illustrate both the strengths and areas for improvement in adapting these models to better capture the nuances of Hindi texts.
Paper Presenter
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room D Pune, India

9:30am IST

Use of NLP in Medical Document Translation for Low Resource Language(Tamil)
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Guhan Senthil Sambandam, Priyadarshini J
Abstract - Machine learning has significantly impacted daily life, with machine translation emerging as a rapidly advancing domain. In healthcare, machine learning presents opportunities for innovation, particularly in translating medical documents into low-resource languages like Tamil. This research develops a transformer-based model fine-tuned for medical terminology translation from English to Tamil. A major challenge was the lack of English-Tamil medical datasets, addressed through innovative data collection methods, such as extracting bilingual subtitles from Tamil YouTube videos. These datasets complement existing resources to enhance model performance. The final model was deployed as a REST API using a Flask-based server, integrated into a React Native mobile application. The app enables users to scan English medical documents, extract text via on-device Optical Character Recognition (OCR), and obtain Tamil translations. By combining advanced Natural Language Processing (NLP) techniques with user-friendly application design, this end-to-end system bridges linguistic gaps in healthcare, providing Tamil-speaking populations with improved access to critical medical information. This study highlights the potential of NLP-driven solutions to address healthcare disparities and demonstrates the feasibility of adapting machine translation systems to specialized domains with resource limitations. The approach also emphasizes scalability for broader applications in similar low-resource settings.
Paper Presenter
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room D Pune, India

9:30am IST

Analysis and Classification of Water Quality Using Machine Learning Technique
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Manohar R, N Abhishek, Nagesh S, Sumith R, C Balarengadurai
Abstract - Water quality monitoring is essential for public health and environmental stewardship. Conventional methods, while effective, are often costly, time-intensive, and require specialized skills. In response to these limitations, this paper explores machine learning as a rapid, scalable solution to classify water quality using key parameters, including pH, turbidity, organic carbon, and contaminants. By implementing algorithms such as Random Forest, SVM, and other advanced models, we seek to enhance the precision of water purity assessments. This paper shows the potential of ML applications in real-time monitoring, addressing the need for accessible, cost-efficient, and accurate water quality solutions suitable for broad deployment across diverse environments.
Paper Presenter
avatar for Nagesh S
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room E Pune, India

9:30am IST

Contextual Visual Question Answering On Remote Sensing Images
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Hrudai Aditya Dharmala, Ajay Kumar Thallada, Kovvur Ram Mohan Rao
Abstract - Recent advances in vision-language models have demonstrated remarkable multimodal generation capabilities. However, their typical reliance on training large models on massive datasets poses challenges in terms of data and computational resources. Drawing inspiration from the expert-based architecture of Prismer, we propose a novel framework for contextual visual question answering specifically tailored to remote sensing imagery. Our methodology extends the Prismer architecture through a two-stage approach: first, by incorporating a domain-specific segmentation expert trained on remote sensing datasets, and second, by integrating a fine-tuned Large Language Model (Mistral 7B) optimized using Parameter-Efficient Fine-Tuning (PEFT) with QLoRA for remote sensing terminology, with hyperparameters optimized with help of Unsloth framework. The segmentation expert performs the analysis of remote sensing imagery, At the same time, the language model acts as a reasoning expert, combining domain-specific knowledge with natural language understanding to process visual contexts and generate accurate responses. In our framework, the use of the Unsloth fine-tuning approach for the language model helps maintain high performance within the defined scope of remote sensing classes and terminology while avoiding hallucination or deviation from established classification schemas. This opens an exciting direction for making the use of Earth observation data more accessible to end-users, demonstrating significant improvements in accuracy and reliability compared to traditional approaches. Experimental results validate that this architecture effectively balances domain expertise with computational efficiency, providing a practical solution for remote sensing visual question answering that requires substantially fewer computational resources compared to end-to-end training of massive models.
Paper Presenter
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room E Pune, India

9:30am IST

Cryptocurrency Price Prediction and Security Challenges in Machine Learning Approach
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Jenat Arshad, Afruja Akter, Tanjina Akter, Kingkar Prosad Ghosh, Anupam Singha
Abstract - Cryptocurrencies have emerged as a significant financial asset class, attracting global attention for their potential to disrupt traditional financial systems. Due to its extreme price volatility and ability to be traded without the assistance of a third party, cryptocurrencies have gained popularity among a wide range of individuals. This paper presents a comprehensive study of machine learning techniques, particularly deep learning models such as Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Artificial Neural Network (ANN) in predicting cryptocurrency prices. Furthermore, this study addresses the security and privacy challenges inherent to blockchain technology, upon which cryptocurrencies operate. We predict the prices of popular cryptocurrencies like Bitcoin, and Ethereum, and lesser-known ones like Binancecoin, Litecoin, and Ripple through a hybrid deep learning model. This paper also compares cryptocurrency price prediction with machine learning models like GRU, ANN, and our proposed model Hybrid LSTM-GRU. The results demonstrate the efficacy of machine learning in price prediction, highlighting blockchain's potential to enhance security and privacy in financial transactions. Our model gives the value of MSE, RMSE, MAE and MAPE to determine the forecasting. We’ve also added the manual calculation for each metric and compared the actual price with the predicted price that our model gave.
Paper Presenter
avatar for Afruja Akter

Afruja Akter

Bangladesh
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room E Pune, India

9:30am IST

Deep Learning-Based Stress Detection Using Facial Expression Recognition and the AffectNet Dataset
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Harsha S Khurana, Payal D Joshi
Abstract - Stress is a major health concern that significantly affects mental stability and can have adverse effects on physical well-being if prolonged. Early detection of stress can help and prevent health-related issues. Individual stress patterns are detected using a variety of bio-signals, including thermal, electrical, auditory, and visual cues which are invasive methods. But according to the well-known saying statement, "Face is a mirror of mind," one can observe one’s emotion or mental state on one’s face. Based on this, Investigated the potential of using facial expressions as a non-invasive method to detect stress levels. Facial expressions could be analyzed and classified as stress and non-stress by examining facial expressions. To solve this problem we have used pretrained network models - Inception, Xception, MobileNetv2, Vgg19, EfficientNet deep learning models, and Affectnet Dataset for stress detection and also represent the comparative study of networks based on confusion and performance metrics. Testing on a separate set of data of images indicates that the MobileNetv2 and Xception models give more accuracy for stress detection.
Paper Presenter
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room E Pune, India

9:30am IST

Enhancing Automated Cotton Disease Detection Using CNNs for Sustainable Agriculture
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Rinkesh N Parmar, Payal D Joshi
Abstract - Cotton, an essential crop for the textile industry and millions of farmers, is vulnerable to diseases that can significantly affect yields and profitability. Traditional methods of disease detection, relying on expert visual inspections, are labour-intensive, time-consuming, and prone to errors, often causing delays in addressing problems. This study investigates the use of Convolutional Neural Networks (CNNs) for automated, early, and accurate detection of cotton diseases. CNNs are effective at extracting hierarchical features from raw image data, making them ideal for image classification tasks. In this approach, a labelled dataset of cotton plant images is utilized to train the CNN model, incorporating data augmentation to enhance variability and generalization. The model employs convolutional layers for feature extraction, max-pooling layers for dimensionality reduction, dropout layers for regularization, and fully connected layers for classification. The Adam optimizer, known for faster convergence, is used during training, along with categorical cross-entropy loss. The evaluation is based on accuracy, precision, recall, and F1-score. The model showed significant improvements in performance. The baseline CNN achieved 92.34% accuracy, but advanced architectures like Hybrid CNN-LSTM, DenseNet-121, ResNet-50, and InceptionV3 enhanced accuracy by 2-3%, along with increased precision, recall, and F1-score. The Hybrid CNN-LSTM model outperformed others, achieving 94.5% accuracy, 93.5% precision, 93.2% recall, and 93.3% F1-score. These results suggest that CNN-based models, particularly Hybrid CNN-LSTM, offer substantial improvements in cotton disease detection. The incorporation of data augmentation and dropout regularization strengthens the model, making it effective for real-time agricultural disease management. Future work will focus on expanding the dataset, improving the model, and implementing it in real-world cotton farming practices.
Paper Presenter
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room E Pune, India

9:30am IST

Forecasting electricity consumption using ARIMA model
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Joven A. Tolentino
Abstract - The growing demand for electricity necessitates effective monitoring and forecasting of consumption trends. This study employs ARIMA modeling, using data from the Department of Energy, Philippines, to analyze and predict electricity consumption. The forecast for the next two years indicated an 18.99% increase in consumption between 2016 and 2017.To enhance analysis, the predicted data was clustered using the K-Means algorithm to group months with similar consumption patterns. This approach identified periods of high, medium, and low electricity usage, providing valuable insights into peak demand months. Such data-driven findings can guide electricity providers in prioritizing resources and implementing strategies to address fluctuations in consumer demand effectively. This study emphasizes the importance of forecasting and clustering as tools for decision-making to mitigate challenges arising from increasing electricity demand.
Paper Presenter
avatar for Joven A. Tolentino
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room E Pune, India

9:30am IST

Machine Learning Driven Non-invasive Biomarker Measurement
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Manmeet Borkar, Suneeta Raykar
Abstract - Monitoring biomarkers is essential for patients to effectively manage their health profiles and prevent potential complications. Regular tracking of these indicators allows for timely interventions and better control over one’s health, particularly when the methods employed are non-invasive and grant convenience and comfort to the patient. Conventionally, this monitoring is accomplished in pathology laboratories, by collecting blood samples or finger-pricking, which can be distressing and impractical for regular use. Given the growing need for more accessible and affordable healthcare solutions, the development of a cost-effective non-invasive method has become crucial. We propose the use of machine learning models to enable non-invasive measurement of biomarkers such as Total Cholesterol, Uric acid and Blood Sugar. Several Machine learning algorithms, including Linear Regression, K-Nearest Neighbors (KNN), Decision Tree, Random Forest and Support Vector Regression (SVR), were applied to the datasets constructed using the MAX30102 sensor. The metrics used to evaluate regression models were Mean Square Error (MSE) and Coefficient of determination (R²). The final prediction model was built using the algorithm that yielded the highest Coefficient of determination (R²). A user-friendly interface was developed using Tkinter, allowing the input of sensor values from the MAX30102 sensor. The prediction of biomarker values promotes health awareness and timely alerts against potential complications. The results obtained using this approach were validated against laboratory blood reports, revealing an average offset of less than 10% in the predictions.
Paper Presenter
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room E Pune, India

9:30am IST

Mapping Social Barriers in Indian Plantation Communities: Insights and Recommendations
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Kirthika. P, M. Suresh, S. Kanagaraj
Abstract - This paper explores the social barriers faced by Indian plantation communities. It focuses on how these obstacles impact their well-being, productivity, and social mobility. By analyzing historical, socioeconomic, and cultural factors, the study uncovers the multifaceted challenges plantation workers encounter, including income, education, social position in the community, social networks, migration, exploitation, and working and living conditions. The DEMATEL approach identifies the barriers and analyzes the interrelationships among those that impact social barriers among plantation workers. This paper identified seven barriers of impact from a literature review followed by interviews with experts to interpret the interconnection of barriers and investigate the interrelationships. The result says that income and education are the key barriers impacting the lives of plantation workers in their society. The present study incorporates the DEMATEL approach model to analyze the critical barriers in mapping the social barriers of plantation workers. The DEMATEL approach model is the first attempt to study the interrelationship among the barriers. The research overviews the prevailing issues through field surveys, interviews, and literature reviews. The paper will conclude with actionable recommendations aimed at policymakers, community leaders, and stakeholders to mitigate these barriers and promote a more inclusive and equitable environment for plantation workers.
Paper Presenter
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room E Pune, India

9:30am IST

Sentiment Insight: Leveraging NLP for Real-Time Feedback Analysis
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Aishani Das, Sobitha Ahila, Sreyashi Dey
Abstract - Sentiment analysis within the food industry offers essential insights into customer satisfaction, product perception, and emerging concerns. A novel sentiment classification model is developed for Amazon food reviews, leveraging Sentiments are categorized as positive, neutral, or negative using techniques from Natural Language Processing and Machine Learning. Traditional ML algorithms, such as Logistic Regression, Naive Bayes, and Support Vector Machines, are combined with the BERT deep learning model to enhance classification accuracy. With a dataset of over 500,000 reviews sourced from Kaggle, the methodology includes data cleaning, feature extraction, exploratory data analysis, model training, and evaluation. Initial findings demonstrate SVM’s high predictive accuracy in sentiment classification, while BERT’s advanced contextual understanding suggests further enhancements. Applications of this model extend to real-time feedback systems that assist businesses in identifying and addressing customer sentiments promptly. Future developments aim to improve accuracy, incorporate a diverse range of datasets, and integrate real-time processing and multilingual analysis for broader, more effective sentiment analysis capabilities.
Paper Presenter
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room E Pune, India

9:30am IST

Solar Powered DC-DC Converter fed Electronically Commutated Motor Driven Electric Bike
Friday January 31, 2025 9:30am - 11:30am IST
Authors - Tinoy Santra, Sahil Neekhra, Ritik Gupta, Gunabalan Ramachandiran
Abstract - With the rising environmental degradation and increasing global warming, electric vehicles are the promising concept in the automobile industry. Different sources of energy are available for giving power to drive the vehicle. Sunlight being an efficient and abundant resource, the world is moving towards solar energy leaving behind conventional power resources. Moreover, battery based electric vehicles have short driving range and speed which is not acceptable in the dog-eat-dog market. This paper discusses a simple approach for BLDC motor driven electric vehicle powered by buck-boost converter. The primary energy source is solar energy, and the PI controller holds the DC-DC converter's output constant. A 660 W, 48 V BLDC motor driven electric bike system is worth an alternative when it is solar powered which solves utmost all the problems faced in usage of EVs. The circuit is simulated in MATLAB environment and output parameters are observed for different load conditions. Overall, the motive is to prove that electric vehicles are more efficient and cost effective than the conventional ones.
Paper Presenter
Friday January 31, 2025 9:30am - 11:30am IST
Virtual Room E Pune, India

11:15am IST

Session Chair Remarks
Friday January 31, 2025 11:15am - 11:20am IST
Invited Guest/Session Chair
avatar for Dr. Killol Vishnuprasad Pandya

Dr. Killol Vishnuprasad Pandya

Associate Professor, CHARUSAT University, Gujarat, India.
Friday January 31, 2025 11:15am - 11:20am IST
Virtual Room A Pune, India

11:15am IST

Session Chair Remarks
Friday January 31, 2025 11:15am - 11:20am IST
Invited Guest/Session Chair
avatar for Prof. Priteshkumar Prajapati

Prof. Priteshkumar Prajapati

Assistant Professor, Charotar University of Science and Technology (CHARUSAT), Gujarat, India
Friday January 31, 2025 11:15am - 11:20am IST
Virtual Room B Pune, India

11:15am IST

Session Chair Remarks
Friday January 31, 2025 11:15am - 11:20am IST
Invited Guest/Session Chair
avatar for Dr. Bimal Patel

Dr. Bimal Patel

Associate Professor, KDPIT, CSPIT, CHARUSAT University, Gujarat, India.
Friday January 31, 2025 11:15am - 11:20am IST
Virtual Room C Pune, India

11:15am IST

Session Chair Remarks
Friday January 31, 2025 11:15am - 11:20am IST
Invited Guest/Session Chair
avatar for Amit Thakkar

Amit Thakkar

Professor & Head , CHAROTAR UNIVERSITY OF SCIENCE AND TECHNOLOGY, Gujarat, India.
Friday January 31, 2025 11:15am - 11:20am IST
Virtual Room D Pune, India

11:15am IST

Session Chair Remarks
Friday January 31, 2025 11:15am - 11:20am IST
Invited Guest/Session Chair
avatar for Dr. Vishvjit Thakar

Dr. Vishvjit Thakar

Professor, Indrashil University, Mahesana, India
Friday January 31, 2025 11:15am - 11:20am IST
Virtual Room E Pune, India

11:20am IST

Closing Remarks
Friday January 31, 2025 11:20am - 11:30am IST
Moderator
Friday January 31, 2025 11:20am - 11:30am IST
Virtual Room A Pune, India

11:20am IST

Closing Remarks
Friday January 31, 2025 11:20am - 11:30am IST
Moderator
Friday January 31, 2025 11:20am - 11:30am IST
Virtual Room B Pune, India

11:20am IST

Closing Remarks
Friday January 31, 2025 11:20am - 11:30am IST
Moderator
Friday January 31, 2025 11:20am - 11:30am IST
Virtual Room C Pune, India

11:20am IST

Closing Remarks
Friday January 31, 2025 11:20am - 11:30am IST
Moderator
Friday January 31, 2025 11:20am - 11:30am IST
Virtual Room D Pune, India

11:20am IST

Closing Remarks
Friday January 31, 2025 11:20am - 11:30am IST
Moderator
Friday January 31, 2025 11:20am - 11:30am IST
Virtual Room E Pune, India

12:15pm IST

Opening Remarks
Friday January 31, 2025 12:15pm - 12:20pm IST
Moderator
Friday January 31, 2025 12:15pm - 12:20pm IST
Virtual Room A Pune, India

12:15pm IST

Opening Remarks
Friday January 31, 2025 12:15pm - 12:20pm IST
Moderator
Friday January 31, 2025 12:15pm - 12:20pm IST
Virtual Room B Pune, India

12:15pm IST

Opening Remarks
Friday January 31, 2025 12:15pm - 12:20pm IST
Moderator
Friday January 31, 2025 12:15pm - 12:20pm IST
Virtual Room C Pune, India

12:15pm IST

Opening Remarks
Friday January 31, 2025 12:15pm - 12:20pm IST
Moderator
Friday January 31, 2025 12:15pm - 12:20pm IST
Virtual Room D Pune, India

12:15pm IST

Opening Remarks
Friday January 31, 2025 12:15pm - 12:20pm IST
Moderator
Friday January 31, 2025 12:15pm - 12:20pm IST
Virtual Room E Pune, India

12:15pm IST

Opening Remarks
Friday January 31, 2025 12:15pm - 12:20pm IST
Moderator
Friday January 31, 2025 12:15pm - 12:20pm IST
Virtual Room F Pune, India

12:15pm IST

Advancing Farming with AI - Machine Learning for Precision Crop Advisory and Sustainability
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Shruti Anghan, Tirth Chaklasiya, Priyanka Patel
Abstract - Technology is an indispensable tool that many industries use to transcend and arrive at the best possible results. A very significant part of the Indian economy constitutes the agricultural sector. Half of the country's workforce is still employed by the agriculture industry. What plays a critical role in affecting the agricultural sector is the natural environment within which it operates, and it throws up many challenges in real farming operations. Most agricultural processes in the country have been old-fashioned and the industry is not ready to step into new technologies. Effective technology can enhance production and reduce the greatest barriers in the field. Today, farmers mostly plant crops not based on soil quality but the market value of the crop and what the crops can return to them. This might impact the nature of the land and the farmer also. Properly applied, modern technologies such as machine learning and deep learning can help revolutionize these industries. It shall show how to apply these technologies properly to give the farmer maximum support in the crop advice field.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room A Pune, India

12:15pm IST

Beyond the Dashboard: Examining Tableau's Attributes, Sector-Specific Applications, and Addressing Data Visualization Challenges
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Bimal Patel, Ravi Patel, Jalpesh Vasa, Mikin Patel
Abstract - The study delves into Tableau's unique characteristics, including its intuitive interface, robust analytics capabilities, and advanced visualization features. By leveraging these features, Tableau empowers users to transform complex datasets into actionable insights, facilitating data-driven decision-making across various domains. The paper explores the extensive applications of Tableau in key industries such as finance, healthcare, retail, and education. In finance, Tableau aids in risk management and performance analysis, while in healthcare, it enhances patient care and operational efficiency through detailed data visualizations. The retail sector benefits from Tableau's ability to analyze sales performance and customer behavior, and in education, it tracks student performance and engagement metrics. Additionally, this research identifies and addresses common challenges associated with data visualization using Tableau, such as handling large datasets, ensuring data accuracy, and maintaining user engagement. The paper provides practical solutions and best practices to overcome these hurdles, ensuring optimal use of Tableau's capabilities. The paper shows how Tableau can be used to help different industries with their specific needs and problems using real-life examples. This study serves as a valuable resource for professionals and researchers seeking to maximize the potential of Tableau in their respective fields.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room A Pune, India

12:15pm IST

Collaborative Robot – Automated Task Optimization
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Aditi Zeminder, Vaibhav Patil, Prathamesh Raibhole, S V Gaikwad
Abstract - This paper presents a part of the study of a collaborative robot (cobot) designed for optimization of work tasks, focusing on selection and workplace. This project investigates best practices by developing a kinematic editing library and using ROS and RViz to perform simulations to analyze and improve motion planning. Conducted an exhaustive review of the existing research literature on collaborative robot control and efficiency and will examine the usage of commercial collaborative software, such as Elephant Robotics' myCobot and Dobot, in introducing the interface design. The Kivy-based control interface was designed to allow users to effectively interact with the robots and adjust parameters to complete tasks. This paper provides an overview of the process adopted, the challenges encountered during development and initial testing, and lays the groundwork for future developments including hardware integration and additional kinematic optimization.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room A Pune, India

12:15pm IST

Denoising Techniques of Audio Signals – A Review
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Eshwari Khurd, Shravani Kamthankar, Avani Kelkar, Ravinder B. Yerram
Abstract - One of the major challenges encountered when it comes to speech recognition, medical imaging, and multimedia processing for radar or weather forecasting applications, is noise interference in audio and image signals that invariably affect algorithmic precision and dependability. Denoising is responsible for removing unwanted noise while keeping intact the necessary details in the signal. An effective denoising method for audio and image signals is under continuous research across multiple parameters taken into consideration giving priority to signal-to-noise ratio (SNR). In this paper, we have surveyed various such denoising methods with a focus on the ones using Principal Component Analysis (PCA) and Ensemble Empirical Mode Decomposition (EEMD).
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room A Pune, India

12:15pm IST

Digital Transformation and the Waste Management Revolution – Application of Innovative Technologies for Smart City
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Renuka Deshmukh, Babasaheb Jadhav, Srinivas Subbarao Pasumarti, Mittal Mohite
Abstract - In response to the issue of growing garbage, researchers, foundations, and businesses worldwide developed concepts and created new technology that sped off the procedure. Trash comes from a variety of sources, including municipal solid trash (such as discarded food, paper, cardboard, plastics, and textiles) and industrial garbage (such as ashes, hazardous wastes, and materials used in building and demolition). Contemporary waste management methods often take sociological factors into account in addition to technological ones. This review paper's goal is to talk about the potential applications of cutting-edge digital technology in the waste disposal sector. With reference to smart cities, this study aims to comprehend the environment, including the opportunities, barriers, best practices at present, and catalysts and facilitators of Industry 4.0 technologies. An innovative approach for examining the use of digital technology in smart city transformation is put out in this study. Analysis of the suggested conceptual framework is done in light of research done in both developed and developing nations. The study offers case studies and digital technology applications in trash management. This article will examine the ways in which waste management firms are utilizing cutting-edge technology to transform waste management and contribute to the development of a healthier tomorrow.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room A Pune, India

12:15pm IST

Emotion Recognition on Electroencephalogram data using Dynamic Graph Convolutional Neural Networks
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Arvin Nooli, Preethi P
Abstract - Recognizing emotional states from electroencephalogram, or Electroencephalogram (EEG), signal data is challenging due to its large dimension and intricate spatial dependencies. Our project illustrates a novel approach to Electroencephalogram (EEG) data analysis in emotion recognition tasks that employ Dynamic Graph Convolutional Neural Networks (DGCNN). Our novel architecture takes advantage of the inherent graph structure of Electroencephalogram (EEG) electrodes to effectively capture spatial relationships and dependencies. Our approach used a refined DGCNN model to process and classify the data into four primary emotional states- Happy, Sad, Fear, and Neutral, we configured the DGCNN with 20 input features per electrode, optimized across 62 electrodes, and utilized multi-layered graph convolutions. The model achieved an overall classification accuracy of 97%, with similarly high macro and weighted average scores for precision, recall, and F1-score, demonstrating its resilience and accuracy.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room A Pune, India

12:15pm IST

Estimating Instagram Post Engagement using Cutting-Edge Machine Learning Algorithms
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Chitraksh Madan Singh, Yash Kumar, Lakshya Gattani, A.Anilet Bala, Harisudha Kuresan
Abstract - This study presents an analysis of Instagram reach using Passive Aggressive, Decision Tree, Random Forest, and Linear Regression models. The goal is to predict the impressions generated by posts based on features like likes, saves, comments, shares, profile visits, and follows. Using Instagram data, machine learning algorithms are applied to forecast the post reach, helping marketers optimize content strategies. Quantitative metrics such as Mean Squared Error (MSE) and R-squared (R2) are used to evaluate model performance, with Random Forest showing superior accuracy compared to other models.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room A Pune, India

12:15pm IST

Handwritten English Character Recognition and Colorization
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Shreyas Shewalkar, Shweta Autade, Aditi Sonje, M.R. Kale
Abstract - With the growing need for automated text recognition and image processing, we have explored techniques that enhance the accuracy of handwritten character recognition while simultaneously addressing image restoration challenges. Handwritten English Character Recognition leverages deep learning (DL) techniques to classify and accurately identify characters from scanned or photographed documents. A deep learning-based approach is employed to recognize the patterns in handwritten text, ensuring high precision in distinguishing between characters despite variances in writing styles. In addition to recognition, colorization of grayscale images has gained attention, where DL models predict and apply realistic colors to black and white images. The recognition process applies CNN (Convolutional Neural Networks) for character identification.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room A Pune, India

12:15pm IST

ICT-Driven Financial Literacy Programs: Empowering Citizens for Better Financial Governance
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Sangjukta Halder, Renuka Deshmukh
Abstract - This study scrutinizes the impact of ICT-driven financial literacy agendas in India, focusing on their role in promoting financial inclusion and enhancing governance. By leveraging digital tools such as mobile apps, online courses, and e-governance platforms, these programs have effectively increased financial literacy, particularly among underserved populations. The research highlights that while challenges such as the digital divide, language barriers, and varying levels of digital literacy persist, these programs significantly empower citizens to make conversant financial choices and participate more actively with public fiscal management. The incorporation of financial literateness into digital platforms also fosters greater transparency and accountability in governance. For the purpose of improving these programs, legislators, educators, and tech developers may benefit greatly from the insights this research offers. Additionally, it makes recommendations for future research topics to investigate the long-term effects of financial literacy programs powered by ICT on financial behaviours and governance in various socioeconomic situations across India.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room A Pune, India

12:15pm IST

Speech Emotion Recognition
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Yasharth Sonar, Piyush Wajage, Khushi Sunke, Anagha Bidkar
Abstract - Emotion recognition from speech is a crucial part of human-computer interaction and has applications in entertainment, healthcare, and customer service. This work presents a speech emotion recognition system that integrates machine learning and deep learning techniques. The system processes speech data using Mel Frequency Cepstral Coefficients (MFCC), Chroma, and Mel Spectrogram properties that were extracted from the RAVDESS dataset. A variety of classifiers are employed, including neural network-based multi-layer percept, Random Forest, Decision Trees, Support Vector Machine, and other traditional machine learning models. We have created a hybrid deep learning system to record speech signals' temporal and spatial components. a hybrid model that combines convolutional neural networks (CNN) with long short-term memory (LSTM) networks. With an accuracy of identifying eight emotions—neutral, calm, furious, afraid, happy, sad, disgusted, and surprised—the CNN-LSTM model outperformed the others. This study demonstrates how well deep learning and conventional approaches may be used to recognize speech emotions.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room A Pune, India

12:15pm IST

A Survey on Generative AI and Encoders for Video Generation using multimodal inputs
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Satya Kiranmai Tadepalli, Sujith Kumar Akkanapelli, Sree Harsha Deshamoni, Pranav Bingi
Abstract - This paper in detail analyzes how generative AI and encoder-based architectures are drastically changing the realm of video generation with multimodal inputs such as images and text. The application of CNNs, RNNs, and Transformers so neatly serves to encode divergent modalities that blend into the seamless synthesis of realistic video sequences. It is based on the up-and-coming fields of generative models like GANs and VAEs, in bridging from static images to video generation. However, this represents a significant leap forward in the technology of video creation. It also goes into great detail on the complexities of multimodal input, working to balance coherence over time as well as semantic alignment of what's being produced. In the above-described context, it can now be realized how the role of encoders translates visual and textual information into actionable representations for generating video. What follows is a survey on recent progress in adopting Generative AI and multimodal encoders, discussions on what challenges are encountered today, and possible future directions that ultimately lay emphasis on their potential to assist video-related tasks and change the multimedia and AI communities.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room B Pune, India

12:15pm IST

Accelerated Facial Aging using GAN
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Vathsal Tammewar, Bharat Sharma, Dharti Sorte, R. Sreemathy
Abstract - Accelerated facial aging using GANs has been the key interest area in generative modeling and facial analysis fields, which offers significant breakthrough in age progression and regression solutions. This survey conducted an extensive review on techniques of GAN- based approaches for accelerated facial aging, emphasizing highly realistic and controllable aging transformations. Many of these methods applied in forensic investigations, entertainment industries, or age-invariant facial recognition systems, which are vivid demonstrations of the versatility and practical relevance of such methods. While such recent breakthroughs hold great promises, several issues remain; namely high-fidelity transformations to preserve important facial details do not fully diminish biases due to imbalanced datasets, and temporal consistency when age progressions or regressions consist of sequential ages is also critical. Computational efficiency and real-time applicability are still the most critical areas of focus. This paper probes into the strengths, limitations, and open challenges of existing approaches, while emphasizing the importance of innovations such as improved loss functions, diverse and representative training datasets, and hybrid architectures. Thus, this survey contributes to synthesizing current progress and outlining future research directions for advancing the field of GAN-based facial aging technologies.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room B Pune, India

12:15pm IST

Bone Fracture Detection Using Machine Learning
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Khushi Mantri, Abhishek Masne, Shruti Patil, Girish Mundada
Abstract - In medical diagnostics, identifying bone fractures is a crucial task that is traditionally dependent on radiologists deciphering X-ray pictures. However, human factors like experience or exhaustion can occasionally cause delays or inaccuracies in diagnosis. The construction of an automated system for bone fracture identification utilizing Convolutional Neural Networks (CNN), a deep learning method that performs especially well in picture processing, is examined in this research. With the use of a tagged dataset of X-ray pictures, the suggested method can efficiently and accurately detect fractures. Prior to feature extraction using CNN layers which are trained to distinguish between fractured and non-fractured bones the images are pre-processed to improve clarity. In order to assist medical practitioners in making prompt, correct judgments, the final classification attempts to increase diagnostic accuracy while decreasing the amount of time needed for analysis. The potential of incorporating machine learning into healthcare to lower diagnostic errors and enhance patient outcomes is also discussed in this overview paper, which includes examines recent developments in CNN-based medical picture categorization.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room B Pune, India

12:15pm IST

ComPAD in Deepfake Image Detection: Techniques, Comparisons and Challenges
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Shradha Jain, Sneha Suman, Insha Khan, Ashwani Kumar, Surbhi Sharma
Abstract - With continual advancements in deep learning, the potential misuse of deep fake is increasing and its detection is in a major scope of work. A model is trained to recognize the patterns in input data, deep fake recognize those patterns in a fabricated way. Sometimes a small, intentional change is added in the data points, these changes are undetectable to humans and confuse the learning model. Those changes are called adversarial perturbations. Compressive adversarial perturbations aim to make those changes even smaller and harder to detect. Authors explore a sophisticated framework - ComPAD (Compressive Adversarial Perturbations and Detection) which is used to detect adversarial attacks. This paper explores the strategies, and provides comparative analysis of methods used by different researchers. Various datasets including UADFV, DeepfakeTIMIT, LFW, FF++, and Deeperforensics are evaluated to achieve the highest metrics. Methods based on convolution neural networks, particle swarm optimization, genetic algorithm and D4 (Disjoint Diffusion Deep Face Detection) are used for detection. Authors also discuss the challenges such as generalization of models across the new data, the continuous evolution of adversarial perturbations that leads to consistent attacks, and the scalability issues for the real time deep fake. Concluding that models can significantly improve the accuracy, robustness and generalization.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room B Pune, India

12:15pm IST

Comparative Study of Object Detection Models for Enhanced Real-Time Mobile Phone Usage Monitoring in Restricted Zones
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Krisha Zalaria, Jaitej Singh, Priyanka Patel
Abstract - The ubiquitous use of mobile phones in modern society has sparked increasing concern in environments where their usage is restricted, such as hospitals, schools, religious sites, and hazardous zones. Mobile phones, although integral to daily life, pose risks such as privacy breaches, interference with sensitive equipment, and even serious safety hazards. In response, this paper investigates the efficacy of various state-of-the-art object detection models for real-time mobile phone detection in restricted areas. We benchmarked YOLOv8, YOLOv9, EfficientDet, Faster R-CNN, and Mask R-CNN to identify optimal solutions balancing speed, accuracy, and adaptability. This study introduces a two-class detection framework to distinguish between individuals texting or talking on the phone, catering to differing levels of restriction. Evaluations using a customized, diverse dataset reveal YOLOv8 and YOLOv9 as superior, achieving high precision and recall, thus positioning these models as effective solutions for scalable, real-time surveillance systems in sensitive environments. Our research contributes significant insights into mobile phone detection, paving the way for enhanced safety and privacy in restricted zones.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room B Pune, India

12:15pm IST

EchoCart: Voice based chatbot for e-commerce
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Aniket Gupta, Chris Dsouza, Sarah Pradhan, Amiya Kumar Tripathy, Phiroj Shaikh
Abstract - This paper is based on the development of voice chatbots and their configuration to make sure that e-commerce websites are in compliance with all the customer care requirements. The authors talk about the introduction of natural language processing in an e-commerce company and provide a review of recent developments in that area. The research specifically focuses on natural language processing techniques, steps involved in developing a chatbot, problems encountered during design, and functions and benefits of voice-based chatbot in e-commerce. This A study emphasizes chatbots as tools to support customer service systems. Keywords: Machine Learning, Natural Language Processing, Data Analysis, Customer support system, and CHATBOT.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room B Pune, India

12:15pm IST

Empathetic Response Generation Using Big Five Ocean Model and Generative AI
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Siddharth Lalwani, Abhiram Joshi, Atharva Jagdale, M.V.Munot, R. C. Jaiswal
Abstract - Empathetic response generation is a rapidly evolving field focused on developing AI systems capable of recognizing, understanding, and responding to human emotions in a meaningful way. This paper investigates the integration of the Big Five OCEAN personality model with generative AI to generate emotionally relevant, personalized responses tailored to individual users' personality traits. The Big Five model categorizes individuals into five core personality dimensions—Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. By combining this model with advanced generative AI techniques, the system can deliver empathetic responses aligned with users' emotional states and personality profiles. Through the use of various machine learning algorithms, the study demonstrates that incorporating personality traits significantly improves the quality, accuracy, and emotional resonance of AI-generated responses, leading to more effective human-AI interactions.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room B Pune, India

12:15pm IST

Federated and Deep Learning Techniques in Medical Imaging: A State-of-the-art Innovative Approaches for Brain Tumor Segmentation
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Shaga Anoosha, B Seetharamulu
Abstract - Brain tumor segmentation is a critical task in medical imaging, essential for accurate diagnosis and treatment planning. Recent advancements in federated learning (FL) and deep learning (DL) offer promising solutions to the challenges posed by traditional centralized learning methods, particularly regarding data privacy and security. This review paper delves into state-of-the-art approaches that FL and DL to enhance brain tumor segmentation. Each institution trains a deep learning model, typically a Convolutional Neural Network (CNN) or a specialized architectures like U-Net on its local dataset. U-Net, particularly effective for image segmentation tasks, consists of an encoder that extracts hierarchical features from MRI scans and a decoder that reconstructs the segmented output, creating a segmentation map outlining tumor boundaries. Instead of sharing raw MRI scans, federated learning allows each institution to share model updates with a central server. The central server aggregates the updates from all participating institutions to create a global model using Federated Averaging, which averages the weights of the local models. The updated global model is then sent back to each institution, which continues training on their local data using this improved model. This iterative process ensures high accuracy, robustness, and privacy preservation, making it a promising approach for collaborative brain tumor detection and segmentation. By combining the strengths of federated learning and deep learning, these state-of-the-art methodologies provide a powerful solution to the challenges posed by traditional centralized models. This integration not only improves segmentation performance but also ensures that sensitive patient data remains secure. As advancements in this field progress, the collaborative use of these state-of-the-art techniques is poised to significantly enhance diagnostic accuracy and improve patient outcomes in medical imaging.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room B Pune, India

12:15pm IST

Hinglish Sentiment Analysis using LSTM-GRU with 1D CNN
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Adarsh Singh Jadon, Rohit Agrawal, Aditya A. Shastri
Abstract - This study investigated the efficacy of various deep learning models in performing sentiment analysis on code-mixed Hinglish text, a hybrid language widely used in digital communication. Hinglish presents unique challenges due to its informal nature, frequent code-switching, and complex linguistic structure. This research leverages datasets from the SemEval-2020 Task 9 competition and employs models such as RNN (LSTM), BERT-LSTM, CNN, and a proposed Hybrid LSTM-GRU with 1D-CNN model. Combining the strengths of LSTM and GRU units along with 1D-CNN, demonstrated superior performance with an accuracy of 93.21%, precision of 93.57%, and recall of 93.02%, along with Sensitivity & Specificity of 93.62% and 93.24% respectively. It also achieved F1 Score of 93.44%. We also evaluated the model on some other parameters, such as PPV, PNV, RPV, and RNV. This model outperformed existing approaches, including the HF-CSA model from the SemEval-2020 dataset, which achieved an accuracy of 76.18%.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room B Pune, India

12:15pm IST

Multimodal Emotion Recognition: Review Paper
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Chetana Shravage, Shubhangi Vairagar, Priya Metri, Akanksha Madhukar Pawar, Bhagyashri Dhananjay Dhande, Siddhi Vaibhav Firodiya, Tanmay Pramod Kale
Abstract - Emotion Recognition has gained significant popularity, driven by its wide range of applications. Emotion recognition methods use various human cues such as use of speech, facial expressions, body postures or body gestures. The methods built for emotion recognition use combinations of different human cues together for better accuracy in results. This paper explores different methods which use different human cues.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room B Pune, India

12:15pm IST

A Study on Automation of Traffic Violation Detection
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Keesari Abhinav Reddy, Vanaparthi Sai Charan, Md. Sufiyan, Puvula Kiranmai, Madhuri. T, M. Venugopala Chari
Abstract - The major challenge for road safety and traffic regulation continues to be categorized traffic offenses that include speeding, running of red lights, improper parking, and distracted driving. Recent innovations in artificial intelligence (AI) and machine learning (ML) have made it possible to develop automated systems that can detect and classify varied traffic violations in detail. This paper analyzes studies that have emerged recently, focusing on advanced technologies, including those such as YOLO-based object detection, OCR, integration with IoT, and real-time monitoring. The paper evaluates datasets, performance metrics, and methodologies covering violations including helmet use, lane changing, and the use of a mobile phone while driving. Significant challenges that have been touched upon in the review include issues of data privacy, high computational requirements, and environmental limitations. Some of the encouraging solution includes use of sophisticated deep learning models, big data analytics, sensor fusion, and edge computing as pathways to enhance scalability and reliability. Future effort will include improvement of real-time systems, reduction of false positives, and addressing socio-technical problems. Using approaches that merge existing advances, this paper has suggested some pathways for using AI-driven systems towards the improvement of road safety and adherence to traffic rules.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room C Pune, India

12:15pm IST

A Survey on the Lung Diseases Prediction in an Indian Environment Using Machine Learning
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Indushree Shetty, Prerna Agrawal, Savita Gandhi
Abstract - The Chronic respiratory diseases, including Chronic Obstructive Pulmonary Disease (COPD), Cystic Fibrosis, Chronic Bronchitis, Interstitial Lung Disease (ILD), Pleural Effusion, Pneumothorax, and Mesothelioma contribute significantly to global mortality and morbidity. The lung diseases in India are influenced by various demographic, environmental, and lifestyle factors like air pollution, high smoking rates, climate change and weather patterns, genetic and hereditary factors, etc. This paper highlights the current scenario of various lung diseases affecting Indian population, highest incident being of COPD to the extent of 89%. The study in this paper surveys the comparison of detection of different lung diseases using machine learning in an Indian Scenario with respect to different parameters like diseases predicted, dataset used, source of dataset, findings, limitations, future score, methods used and accuracy. Based on the comparative study, this paper also highlights various research gaps for future scope in an Indian Scenario. By prioritizing the solutions to the identified research gaps, medical practitioners would be able to handle better India's high respiratory disease burden, increasing the likelihood of more dependable and inclusive healthcare solutions.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room C Pune, India

12:15pm IST

Development of AI based Fashion Recommender System for E-commerce Business
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Siddhi Mulewar, Abhijay Patil, Gauri Patil, Nikhil Chame, Smita Kulkarni
Abstract - E-commerce has completely transformed traditional retail by lowering operating expenses and enabling worldwide access. Online shopping experiences have been further changed by the integration of artificial intelligence (AI) and machine learning (ML), especially with the advent of Fashion Recommendation Methods (FRM) that employ deep learning techniques. This research introduces a unique FRM that uses a single image input to provide tailored fashion suggestions based on user preferences, improving the quality of the shopping experience. Collaborative filtering (CF) is preferred method in this research work, which encourages users to explore a wider range of content and become more engaged. In this research work ResNet50 pre-trained neural networks proposed to extract information from photos, enabling precise and customized fashion recommendations. Comparative studies show that ResNet50 performs better than other CNN models, leading to increased personalization and accuracy. In the highly competitive world of e-commerce, this study emphasizes the potential of AI-driven suggestions to improve the online shopping experience, stimulate user engagement, and foster loyal consumers. VITON is a Virtual Try-On Network that uses images instead of 3D data to overlay clothes on a person’s image. It creates and refines photo-realistic images with natural clothing deformation using a coarse-to-fine strategy.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room C Pune, India

12:15pm IST

Efficient KYC for DAO using Blockchain
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Manasa S Desai, Nirmala M B, Veeresh Kumar Y M, Varsha G C, Vinnet Gokhale, Sushma E Roa
Abstract - Electronic Know Your Customer (e-KYC) system is essential for banking and identity providers to verify customer identities efficiently. With the widespread adoption of cloud computing, due to its resource efficiency and high accessibility, many sectors have implemented their e-KYC systems on the cloud. This shift, however, raises significant concerns about the security and privacy of e-KYC documents stored in the cloud. Blockchain technology, a recent innovation, offers potential solutions to enhance various application domains, including digital identity verification. This project proposes a Blockchain-based e-KYC system to address these concerns. This system provides a secure, efficient, and reliable method for identity authentication, which is particularly beneficial in sectors such as banking, tele communications, and government services. By utilizing a distributed ledger to store and verify customer data, the proposed e-KYC framework ensures data integrity and minimizes fraudulent activities. In this framework, customer data is stored on a distributed ledger and encrypted to enhance security. This encryption safeguards sensitive personal information from unauthorized access and cyber threats. This project combines the Ethereum blockchain with Zero-Knowledge Proof (ZKP) technology to provide strong digital identity verification, maintain data integrity, and reduce fraud. The decentralized nature of proposed e-KYC system not only boosts security but also reduces reliance on central authorities, thereby accelerating the verification process and lowering operational costs. This approach offers arobust solution for secure digital identity verification.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room C Pune, India

12:15pm IST

Enhanced Safety Stun Gun with GPS and GSM for Self-Defense
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Chilakala Sudhamani, Akula Spoorthi, B. Srilatha
Abstract - In today’s world, women face numerous safety challenges, including harassment and molestation. In this paper, we proposed a self-defense stun gun as an effective and efficient solution for women’s safety. This portable device contains a high-voltage generator, GSM and GPS module, panic and taser button and an Arduino Uno with Atmega328 AVR microcontroller. When the device is activated in a dangerous situation, it immediately sends an SMS with the user’s location and distress signal to pre-selected contacts. It also generates a 1000kV electric shock to temporarily immobilize an attacker, allowing the user to escape or seek help. This device aims to enhance the safety and security of women in urgent need or dangerous circumstances for proactive measures against gender-based violence.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room C Pune, India

12:15pm IST

F² (Familiar Faces): A Novel Approach to Persona Classification Using Facial Recognition and Digital Footprints
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Riddhi Sonawane, Ganesh Bhutkar, Swarup Vishwas, Vivek Badade, Akshay Shingote
Abstract - Traditional persona classification methods rely on static, time consuming techniques like surveys and interviews. To address this limitation, we propose F², a novel approach that leverages facial recognition and digital footprint analysis for dynamic persona classification. By integrating real-time data from various digital platforms, F² creates more accurate and up-to-date user profiles. Our system prioritizes user privacy and adheres to relevant data protection regulations. Through robust facial recognition and advanced machine learning algorithms, F² effectively categorizes users into distinct personas, enabling tailored experiences and personalized interactions. This innovative approach has the potential to revolutionize user modeling and enhance digital experiences across diverse domains.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room C Pune, India

12:15pm IST

Green IT: Exploring Sustainable Technologies for Reducing the Carbon Footprint of IT Operations
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Aafiya Anjum Abdul Rafique, Martin H Mollay, Shailesh Gahane, Deepak S. Sharma, Pankajkumar Anawade
Abstract - There have been remarkable adoptions and uses of Information Technology (IT); therefore, there has been a significant surge in energy consumption and carbon emissions in recent times. While most industries are increasingly relying on digital technologies, IT operations are also increasing their impact on the environment, thereby making green IT a vital necessity. Green IT is an all-encompassing method of managing the environmental footprint of IT through the reduction of energy consumption, electronics waste, and optimum resource efficiency. This paper discusses, from a critical perspective, the role of Green IT in reducing the carbon footprint of IT operations through sustainable technologies and practices. Beyond this, it also discusses challenges and potential solutions for a more green IT landscape in the data center, cloud computing, virtualization, energy-efficient hardware, and new sustainable development practices in software. To sum up, this paper focuses attention on some of the critical factors for driving the adoption of sustainable IT solutions: policy, education, and cross-sector collaboration.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room C Pune, India

12:15pm IST

Multi-Scale Forecasting of Electricity Demand in Telangana Using Time Series and Machine Learning Models
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Dheekshitha Bazar, Gajelli Sai Susmitha, Shreshta Myana, Ramu Kuchipudi, Ramakrishna Kolikipogu, P. Ramesh Babu, K. Gangadhara Rao
Abstract - Strategic planning, grid management, and lessening the financial burden on Telangana’s power sector all depend on accurate demand forecasts for electricity. Currently, forecasting methods rely primarily on traditional approaches, but these models often fall short in capturing complex demand patterns at multiple time intervals, especially in dynamic sectors like agriculture. Existing forecasting methods, focused mainly on traditional approaches, often fall short in capturing complex demand patterns across multiple time scales, particularly in sectors like agriculture. This study introduces a comprehensive multi-scale forecasting model for Telangana’s electricity consumption over the next five years, targeting yearly, monthly, weekly, and daily intervals, with a focus on peak load forecasting. Time series techniques such as ARIMA, Prophet, Weighted Moving Average (WMA), and Error Trend Seasonality (ETS) are leveraged to capture seasonality, trends, and short-term fluctuations in demand, providing actionable insights for the Telangana SLDC. Methods for machine learning such as Long Short-Term Memory (LSTM) networks and Gradient Boosting Machines (GBM), are integrated to capture complex temporal patterns and improve predictive accuracy. This study offers a scalable framework for electricity demand forecasting, adaptable to other regions and utilities, advancing methodologies in the power sector. The suggested approach uses metrics to assess the model’s performance such as Root Mean Square Error, Mean Absolute Error (MAE), Both Mean Absolute Percentage Error (MAPE) and RMSE are used to choose the most precise model for every period.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room C Pune, India

12:15pm IST

Predictive Modelling of Childhood Fever Prevalence: Leveraging Machine Learning in Maternal and Child Health
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Hemal S, Sohana R, M Shahina Parveen, Tarun Pradeep Kumar
Abstract - Childhood fever poses a significant health concern in India, necessitating timely intervention and effective healthcare strategies. However, predicting fever prevalence accurately remains a challenge due to the diverse healthcare landscape and maternal-child health indicators. This research aims to develop a systematic methodology for predicting childhood fever prevalence based on maternal and child healthcare indicators in India. Leveraging machine learning algorithms, particularly Support Vector Regression (SVR), the study seeks to provide an effective tool for early detection and intervention in infant fever cases. Using data from the "India - Annual Health Survey (AHS) 2012-13" dataset, specific maternal and child healthcare indicators relevant to childhood fever prevalence are identified. These indicators encompass ante-natal care, delivery care, immunization, breastfeeding, and supplementation practices. Various regression algorithms, including SVR, are trained and evaluated to accurately predict childhood fever prevalence. Experimental results demonstrate that SVR outperforms other regression algorithms, showcasing its effectiveness in capturing non-linear relationships and handling outliers. This study offers a structured framework for early detection and intervention in childhood fever cases, leveraging machine learning algorithms and maternal-child health indicators. By accurately predicting fever prevalence, healthcare practitioners can implement timely interventions, ultimately improving healthcare outcomes for infants in India.
Paper Presenter
avatar for Hemal S

Hemal S

India
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room C Pune, India

12:15pm IST

Visionary Assistance: Where Smart outsmarts Sight
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Shivam Kumar Singh, Sindhu Chandra Sekharan, Aishwarya Mondal, Nitin Nagar, Shruti Shreya, Yuting Zhu
Abstract - This work presents a comprehensive IoT-based smart assistant device aimed at providing essential navigation and safety support for physically challenged individuals, especially those with visual impairments. The device is equipped with advanced functionalities, including GPS tracking for real-time location monitoring, MobileNet-based object and face recognition, OCR capabilities for reading printed text, and ultrasonic sensors for detecting obstacles, which trigger an alarm to alert the user. Its design prioritizes energy efficiency, allowing it to run effectively on low power while offering reliable real-time processing. By combining multiple assistive features into a single, cost-effective, and portable device, this solution sets itself apart from traditional options that often focus on one functionality or rely on expensive hardware. The modular and scalable architecture not only makes it an affordable and practical solution but also allows for easy customization and potential wireless enhancements. This flexibility opens up possibilities for broader applications in fields like assistive healthcare, autonomous navigation, and consumer electronics, making it a pioneering tool in inclusive technology that enhances mobility, security, and overall independence for its users.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room C Pune, India

12:15pm IST

AI-powered chatbot for Mental Health Assistance
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Sakshi Limkar, Chhandavi Gowardhan, Devyani Dahake, Sneha Naik, Arti Vasant Bang
Abstract - Chatbots powered by artificial intelligence (AI) are becoming more and more inventive tools in the field of mental health treatment. They provide scalable, affordable, and easily accessible support for people struggling with stress, anxiety, depression, and other mental health conditions. These conversational bots provide real-time therapeutic interventions, such as promoting emotional well-being by mimicking human interaction through Natural Language Processing (NLP) and Machine Learning (ML) techniques. We have therefore developed a chatbot for mental health assistance named CalmConnect. It is designed to assist users in identifying and addressing mental health concerns.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room D Pune, India

12:15pm IST

Contemporary Cryptography Against Quantum Computing and LBC on Embedded Systems
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Akshar Thakor, Tanya Khunteta, Kaushal Shah, Hargeet Kaur
Abstract - It is essential to control access to data. Since the beginning of digital communication, scholars have been at work figuring out ways to prevent eavesdropping on data. They have been successful in doing so through cryptography techniques such as RSA, and AES. Modern Quantum computing is shown to be able to break them. Thus, research and development in this field have been rapid in the last decade. It is better to take precautions in its infancy and develop a future-proof cryptography technique. The following article describes the issues with contemporary ciphers and how they are vulnerable against quantum computers, then goes on to suggest lattice-based cryptography as a strong contender for the solution to this vulnerability by providing its benefits and properties synergizing with certain domains in digital technology. It explains why it is a strong contender by providing examples of its performance in IoT devices that are prevalent today and are only going to increase as this era progresses. By providing a comprehensive overview of developments in this realm, it presents to, new researchers in this field, the importance of Lattice-Based Cryptography, by suggesting why Lattice-Based Cryptography should be the focus of the field of cryptography in the future.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room D Pune, India

12:15pm IST

Cyber Attack Network Investigation System Using ML
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Nitin Pandit, Sandeep Chaware, Adit Bagati, Yashraj Shegokar, Omkar Jadhav, Om Nikam
Abstract - DOS attacks or denial of service have become common among hackers who use them as a way to gain reputation and respect in the cyber underground. A denial-of-service attack essentially means denying legitimate and user network services to a target network or server. Its main purpose is to attack so that legitimate users are temporarily unable to use the services on the network. In other words, we can define a DOS attack as an attack that clogs the target’s memory, making legitimate users unable to help. Or, you send packets that the target cannot process, causing the target to fail, reboot, or deny service to legitimate users. We develop an online DOS protection software that can protect web servers.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room D Pune, India

12:15pm IST

Differentiating between AI Generated Faces and Human Faces
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Vedant Patil, Bhargavi Bhende, Omkar Jadhav, Gitanjali Shinde, Kavita Moholkar
Abstract - The increasing realism of AI-generated faces, driven by advancements in Generative Adversarial Networks (GANs) like StyleGAN and ProGAN, poses significant challenges in security, identity verification, and digital forensics. Current detection methods, primarily relying on Convolutional Neural Networks (CNNs), struggle to identify subtle artifacts in high-quality synthetic imagery. This paper proposes a hybrid model combining Vision Transformers (ViT) and XceptionNet in a soft-voting ensemble framework. ViT captures global spatial patterns, while XceptionNet excels in detecting localized texture inconsistencies. The ensemble achieves 92.3% accuracy, 92.5% precision, and an F1-score of 0.922 on a dataset of 188,800 real and AI-generated faces. Extensive experiments demonstrate the model’s robustness against diverse deepfake architectures, including those with minimal artifacts. This approach offers a state-of-the-art solution for differentiating real and AI-generated faces, with significant implications for fraud prevention, content moderation, and digital forensics.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room D Pune, India

12:15pm IST

ENHANCING EVENT CLASSIFICATION ACCURACY AND RELIABILITY THROUGH REDPANDA-OPTIMIZED FEATURE INTEGRATION IN PREDICTIVE SYSTEMS
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Rupali Ramdas Shevale, Monika Sharad Deshmukh
Abstract - For efficient real-time decision-making in a variety of domains, including cybersecurity, finance, and the Internet of Things, accurate and trustworthy event categorization is crucial. By maximizing feature integration, this study explores how incorporating Redpanda, a real-time data streaming platform, into predictive algorithms might improve event categorization. Continuous, high-throughput data processing is made possible by Redpanda's low-latency, fault-tolerant architecture, which enables the real-time extraction of a variety of accurate attributes. Predictive models may use Redpanda's capability to access current, augmented feature sets, which will greatly increase classification accuracy and dependability. The integration process is thoroughly examined in the research, along with its effects on feature variety, model accuracy, and system robustness. The benefits of real-time data streaming in predictive analytics are demonstrated by empirical results, which indicate a significant boost in event categorization performance. By improving feature extraction and enhancing the dependability of predictive systems in dynamic contexts, the results establish Redpanda as a scalable and robust solution.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room D Pune, India

12:15pm IST

Exploring Technologies for Grape Disease Detection : A Comprehensive Survey
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Jayashri D.Palkar, Anuradha S. Deshpande
Abstract - The crop protection plays vital roles in the food supply and depends on how healthy the crops are, which influences the agricultural production; any adverse condition on crops will be leading to economic loss. Grapes find much use, being important and widely cultivated crops primarily in the Mediterranean regions that control an outgoing market of over 189 billion United States dollars. They are grown for consumption as fresh fruits, as well as in various processed forms such as drinks and sweets. These would be grapes, which, unlike many other plants, thrive and develop despite sickness, thus their control mechanisms must also function well. At the same time, many instances of diagnosis of these infections being wrong can lead to inadequate treatments for the known diseases, inducing even more generalized losses amounting from 5-80% on the crop under inspection. Current computer-based solutions may not be precise enough, leading to high running costs, operational difficulties, and image quality issues due to distortions. The body of literature based on different algorithms for the detection and classification of grape crop diseases remains vast and continues to grow rapidly with the newly emerging algorithms. It presents the overview of different disease-detection algorithms for optimizing grape disease detection, thereby aiding farmers in choosing the appropriate algorithm based on particular diseases and weather condition. This study presents a systematic review of various methods implemented in literature and provides a framework for use of AI-ML for effective detection of disease.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room D Pune, India

12:15pm IST

Forecasting Food Demand in Supply Chains: A Comprehensive Comparison of Regression Models and Deep Learning Approaches
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Shilpa M Katikar, Vikas B Maral, Nagaraju Bogiri, Vilas D Ghonge, Pawan S Malik, Suyash B Karkhele
Abstract - Effective forecasting and modeling in food demand supply chains are critical to minimizing waste, reducing costs, and ensuring product availability. This paper explores a comprehensive approach to forecasting food demand by leveraging regression-based models for analysis. We investigate how various machine learning regressors can predict food demand more accurately by examining key supply chain factors such as seasonal trends, price fluctuations, and consumer behavior. The study implements and compares multiple regressors to assess their performance in predicting demand. Metrics Evaluation is done by predicting various models which are Ensemble Learning Models and Neural Network Models to calculate the model’s accuracy. By doing prediction, we identified that Gradient Boosting and XGBoost have overall good accuracy in forecasting and it has provided optimized solutions in the supply food chain. This research mainly focuses on using the best modeling techniques which will help the end users to make proper decisions and bring efficiency in food demand management.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room D Pune, India

12:15pm IST

Forecasting Health Insurance Expenses Using Machine Learning
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - S. B. Hema Anjali, Manikanta Sai Sumeeth, Sushama Rani Dutta
Abstract - This study makes use of a machine learning system that predicts health insurance costs, a relevant issue given the increasing need for such estimates in a post-COVID-19 world. Using the Medical Cost Personal Dataset available at Kaggle offering 1,338 entries, we applied various ensemble models, notably XGBoost, Gradient Boosting Machine (GBM), Random Forest, and Support Vector Machines (SVM). Among our results, XGBoost gives out the best accuracy of the estimates, but the implementation of this technique was expensive. Random Forest was non-intrusive and went on to be of high efficacy. We also discussed how the big data paradigm was implemented using Spark as a means to enhance performance in working on large datasets. As a whole, this work positions XGBoost the ban for the cost of health insurance prediction claiming that there exists scope for improvement by deploying ML methods in decision making in healthcare processes.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room D Pune, India

12:15pm IST

Machine Learning for Cardiovascular Disease Prediction: A Comparative Analysis of Models
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Shrikant Bhopale, Tahseen Mulla, Madhav Salunkhe, Sagarkumar Dange, Sagar Patil, Rohit Raut
Abstract - Cardio-Vascular Disease (CVD) continues to be a prominent issue in worldwide health, emphasizing the crucial importance of accurate forecasting and timely prevention. Machine learning (ML) has become a vital tool in the quest to improve CVD diagnosis. The present study aims to conduct a comparative analysis of various machine learning (ML) algorithms in terms of their performance, which includes Naïve Bayes, Logistic Regression, Random Forest, Decision Tree, Artificial Neural Network, Support Vector Machine and XGBoost, in the prediction of CVD. Our results reveal that XGBoost outshines other models, achieving outstanding accuracy, precision, recall, and F-measure. Its exceptional ability to balance precision and recall makes it an excellent choice for the early identification of CVD. This study makes a valuable addition to the expanding field of study on CVD prediction. It underscores the significance of employing advanced ML algorithms, that have the possibility to significantly influence public health outcomes.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room D Pune, India

12:15pm IST

Photovoltaic Cell Power Forecasting Using LSTM With XAI Integration
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Yathin Reddy Duvuru, Seshank Mahadev, Saranya P
Abstract - In this paper, we implement a deep learning model for photovoltaic (PV) power forecasting using Global Horizontal Irradiance (GHI) values which are the major determiner of photovoltaic cell power output. We use a multilayer Long Short-Term Memory (LSTM) model combined with explainable AI (XAI) techniques, aimed at improving the interpretability of predictions across various forecasting horizons. The model utilizes global horizontal irradiance (GHI) data, which undergoes thorough pre-processing, including cleaning and downsampling to ensure data quality and computational efficiency. The LSTM model is designed with multiple layers to capture temporal dependencies and nonlinearities, which are crucial for accurately forecasting PV power under variable environmental conditions. To evaluate model performance, multiple error metrics such as R², MAE, RMSE, and MAPE are utilized. In addition, a benchmark model is built as a reference to compare against the LSTM-based model, providing a baseline for assessing performance improvements. The use of XAI further enables the interpretation of the LSTM model’s predictions, providing an understanding of feature importance and model behavior. We use the SHAP library to perform XAI analysis by calculating Shapley Values. We demonstrate how the SHAP library can be used on 3D LSTM data. Furthermore, the SHAP graphs provide a sense of the importance of each feature’s role in the prediction.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room D Pune, India

12:15pm IST

A Multilingual Deep Learning Approach for Sign Language Recognition
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - A.Kousar Nikhath, J.Ananya Reddy, P.Aishwarya, S.Maadhurya Sri, P.Gowthami
Abstract - Sign language is the medium between the people who can hear and speak and those who cannot. This project is set to be used in the development of technologies that are beneficial for the lives of individuals with disabilities. The project studies in-depth the use of computer vision and deep learning. The accurate and the regional language translation began with the gestures of the sign languages as the input information, and finally the software produced the accurate translation in the regional language. I am inspired by the prospect of using Artificial Intelligence technology in developing hereditary transmission from a worldwide venue and health diagnosis in a timely manner. Convolutional Neural Network (CNN) is employed to pick up characteristics from hand movement that belongs to the sign language. These attributes are used in the training set as features, themselves in the classification of the gesture, and the process is the learning of this model for the recognition of gestures in real-time. Further, there is an inclusion of computer vision for preprocessing and the sake of accuracy prediction of the recognition process. The functionality of the sign language recognition system is assessed by using a variety of experiments, including accuracy and speed. In general, the developed Sign Language Recognition System with integration of deep learning and computer vision techniques facilitates the precise and quick recognition of sign language gestures. Integration with a translator in addition to this not only makes it multi-language support but also guarantees the correct translation into regional languages.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room E Pune, India

12:15pm IST

A review on Imagify and Image NFT Marketplace
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Hritesh Kumar Shanty, Padirolu Moses, Tulasiram Nimmagadda, Samson Anosh Babu Parisapogu
Abstract - In today’s digital world, combining image editing with secure NFT trading is essential. Imagify addresses this need by offering a unified platform with advanced artificial intelligence tools for image enhancement, recoloring, restoration, and object removal, empowering users to customize images to their preferences. Imagify also simplifies the NFT creation process, allowing users to seamlessly transform their edited images into NFTs that can be bought and sold on a blockchain-secured marketplace. This ensures transparent and secure transactions, providing peace of mind for both creators and buyers. With a flexible, credit-based system, users pay only for the features they choose, making it a cost-effective option. By merging intuitive image editing with a streamlined NFT marketplace, Imagify offers an accessible, user-friendly platform where creators and collectors can engage in digital image trading confidently. This integration creates an efficient and transparent process, supporting both casual creators and seasoned collectors seeking a secure, comprehensive solution for managing and trading digital images.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room E Pune, India

12:15pm IST

AI and Computer Vision Techniques for Fitness Training and Form Analysis: A Comprehensive Review
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - R V S S Surya Abhishek, T Sridevi
Abstract - This paper provides an overview of the current state of AI-based approaches in virtual fitness coaching, focusing on posture estimation and exercise tracking along with real-time feedback. Advances in pose estimation models, including OpenPose, MediaPipe, and AlphaPose, are boosting personalized exercise correction and injury prevention within the sphere of fitness applications. Current literature varies from 2D to 3D pose estimation that includes action recognition and deep learning framework for specific inputs toward movement analysis and user engagement. There is still much room for improvement in current models, with regards to adaptation to individual needs and environments, such as the real-time accuracy that often has not been matched by the personal feedback and robustness of exercise variations. It discusses the approaches currently in use, their applications, and challenges, and by looking at the topic, this paper insinuates the improvement in the adaptability and customization of AI fitness solutions to perfectly emulate human trainers.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room E Pune, India

12:15pm IST

Comparative Analysis of Deep Learning Models for Speech-to-Text and Text-to-Speech conversion
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Mrudul Dixit, Rajiya Landage, Prachi Raut
Abstract - The paper presents a comprehensive comparison of Speech-to-Text (STT) and Text-to-Speech (TTS) models, two foundational technologies in the field of natural language processing and human-computer interaction. The paper examines the evolution of these models, focusing on state-of-the-art approaches such as Whisper Automatic Speech Recognition (ASR), DeepSpeech, and Wav2vec, Kaldi, SpeechBrain for STT, and Tacotron, WaveNet, gTTS and FastSpeech for TTS. Through an analysis of architectures, performance metrics, and applications, the paper highlights the strengths and limitations of each model, particularly in domains requiring high accuracy, multilingual support, and real-time processing. The paper also explores the challenges faced by STT and TTS systems, including handling diverse languages, background noise, and generating natural-sounding speech. There are recent advances in end-to-end models, transfer learning, and multimodal approaches that are pushing the boundaries of both STT and TTS technologies. By providing a detailed comparison and identifying future research directions, this review aims to guide researchers and practitioners in selecting and developing speech models for various applications, particularly in enhancing accessibility for specially-abled individuals.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room E Pune, India

12:15pm IST

Deep Learning Model for Lip-Based Speech Synthesis
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - A.Kousar Nikhath, Aanchal Jain, Ananya D, Ramana Teja
Abstract - The project focuses on creating an advanced system for visual speech recognition by performing lipreading at the sentence level. Traditional approaches, which were limited to word-level recognition, often lacked sufficient contextual understanding and real-world usability. This work aims to overcome those limitations by utilizing cutting-edge deep learning models, such as CNNs, RNNs, and hybrid architectures, to effectively process visual inputs and generate coherent speech predictions. The system's development follows a systematic approach, beginning with a review of existing solutions and their shortcomings. The proposed framework captures both temporal and spatial dynamics of lip movements using specialized neural networks, significantly enhancing the accuracy of sentence-level predictions. Extensive testing on diverse datasets validates the system’s efficiency, scalability, and practical applications. This study underscores the critical role of robust feature extraction, sequential data modeling, and hierarchical processing in achieving effective sentence-level lipreading. The results demonstrate notable improvements in performance metrics. Additionally, the project outlines future advancements, including optimizing the system for real-time processing and resource-constrained environments, paving the way for practical implementation in multiple fields.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room E Pune, India

12:15pm IST

Exploration of Galactic Redshift and Its Impact on Galaxy Properties Using Machine Learning
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Randeep Singh Klair, Gurkunwar Singh, Ritik Verma, Satvik Rawal, Rajan Kakkar, Agamnoor Singh Vasir, Nilimp Rathore
Abstract - The most accurate way to measure galaxy redshifts is using spectroscopy, but it takes a lot of computer power and telescope time. Despite their speed and scalability, photometric techniques are less precise. Thanks to large astronomical datasets, machine learning has become a potent technique for increasing cosmology research’s scalability and accuracy. On datasets such as the Sloan Digital Sky Survey, algorithms such as k-Nearest Neighbors, Random Forests, Support Vector Machines, Gradient Boosting, and Neural Networks are assessed using metrics like R-squared, Mean Absolute Error, and Root Mean Square Error. Ensemble approaches provide reliable accuracy, whereas neural networks are excellent at capturing non-linear correlations. Improvements in feature selection, hyperparameter tuning, and interpretability are essential to improving machine learning applications for photometric redshift estimation and providing deeper insights into cosmic structure and development.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room E Pune, India

12:15pm IST

Innovative Bow Tie Antenna Design for Enhanced MRI Imaging
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Sudha K L, Navya Holla K, Kavita Guddad
Abstract - The antenna is a vital component of the Magnetic Resonance Imaging (MRI) machine which receives the radio frequency signals emitted by the protons in the body after the RF pulse is turned off. Specialized high frequency antennas can improve the quality, clarity, and resolution of the resulting MRI images. This paper deals with the design of Bow Tie antenna for X-Band in the frequency range 8–12 GHz, used in ultra-high field MRI systems. Using the Ansys HFSS tool, the antenna is designed and simulated and analysed. The fabricated antenna with the design specifications is tested in anechoic chamber for its working. Reflection coefficient at 10.5GHz is found to be around -14 dB for simulated antenna and -12 dB for fabricated antenna, which is satisfactory for practical application. Differences between the measured and simulated values were seen in results which are caused by cable loss in the measuring apparatus.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room E Pune, India

12:15pm IST

Integrating Federated Transfer Learning and Blockchain to Enhance IoT Security: A Comprehensive Survey
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Bharati B Pannyagol, S.L Deshpande, Rohit Kaliwal, Bharati Chilad
Abstract - The Internet of Things has revolutionized markets by connecting previously isolated devices, but this integration raises security risks from malicious nodes that can corrupt data or disrupt operations. This evaluation of Federated Learning's possible application as a decentralized node identification technique highlights its advantages over standard machine learning approaches. Internet of Thing devices may collaborate on model training while protecting sensitive data and reducing network use. Federated Learning and Blockchain interactions creates a robust framework addressing critical IoT challenges like data privacy, security, and trust. Blockchain enhances this system by providing a decentralized, tamper-resistant ledger that ensures data integrity and transparency. Automated processes, including model validation and incentive distribution, are facilitated by smart contracts. While this integrated approach improves data protection and scalability, challenges such as computational demands and consensus delays remain. The survey discusses practical applications, challenges, and future research directions for combining Federated Learning and Blockchain in IoT systems.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room E Pune, India

12:15pm IST

Power Electronics: A Pivotal Role in Strengthening Cybersecurity
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Bhadouriya Khushi Mukeshsingh, Rajput Adityasingh Shashikantsingh, Patel Swayam Vinodkumar, Ashish P. Patel, Nirav D. Mehta, Anwarul M. Haque
Abstract - As digital infrastructure becomes more interconnected, effective cyber security has never been more important. This article explodes how advances in power electronics technology can support and improve cyber security frameworks. Energy management strategies, control systems, and semiconductor technologies can be used to increase the systems resilience to potential vulnerabilities that serve as possible entry points for cyber attackers. The research discussed in this article seeks to demonstrate that optimized distribution systems with adaptive control techniques can improve the stability and reliability of critical infrastructure, even in the face of cyber threats. This article discusses the inter-relationship between energy management and cyber security, showing the reader how power electronics can be important in developing a holistic security strategy. It describes a proposed approach to integrating power electronics into cyber security to create an adaptive, robust defence mechanism. This study provides valuable insights into the design of systems that are not only efficient but also fortified against evolving cyber threats, contributing to the broader understanding of how technology convergence can enhance overall infrastructure security.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room E Pune, India

12:15pm IST

YOLO Algorithm-Based Effective Orange Detection and Localization with Improved Data Augmentation
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Madhura Shankarpure, Dipti D. Patil
Abstract - This paper presents a robust framework for YOLO (You Only Look Once) algorithm- based orange detection and localization in photos and videos is presented. The system combines contour-based bounding box localization with deep learning-based item recognition for increased accuracy. Transfer learning was used to refine a pre-trained YOLOv10 model on a Fruit 360 dataset. Data augmentation techniques such as random rotations, brightness changes, and scaling were applied to improve the model's resilience. Bounding boxes are created around identified oranges with a confidence threshold greater than 0.5 as part of the real-time video processing methodology. The model performed well on a balanced test dataset, achieving 95% accuracy, 92% precision, and 90% recall. These findings show how well YOLO works when combined with conventional computer vision methods for real-world uses like automated fruit sorting, fruit harvesting, and real-time market monitoring. The processed video output confirms the system's suitability for real-world situations.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room E Pune, India

12:15pm IST

A Classification of Persuasive Features in Video Games: A Structured Literature Review
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Nomusa Vumisa, Hendrik Pretorius, Marie Hattingh
Abstract - Persuasive features in video games play an important role in encour-aging continuous indulgence and behavior change. Research has been conducted to investigate the different motives, game design elements and features that en-hance the gaming experience. However, there is a gap in understanding how per-suasive features in video games have an impact on individuals, resulting in be-havioral changes. An understanding of the role each feature plays in a video game is crucial for the successful creation and design of a video game aimed to “per-suade” players to change their behavior. This paper presents a systematic review that covers 30 publications and is aimed at investigating the persuasive features in video games and further providing a classification of those features. The results of the study provide a guide to the main theories on behavior change and a clas-sification of the identified persuasive features. Additionally, this study provides a reference for video game designers and developers to utilize when undertaking persuasive projects.
Paper Presenter
avatar for Hendrik Pretorius

Hendrik Pretorius

South Africa
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room F Pune, India

12:15pm IST

A Decentralized Cloud-Based CCTV Surveillance System Using AWS S3 and Block chain for Secure Logging
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Sahil Thakur, Saloni Mahadule, Palash Singh Chandel, Sudhanshu Maurya, Firdous Sadaf M. Ismail, Rachit Garg
Abstract - As CCTV technology has continued to mature quickly, important and fundamental questions about secure, scalable and transparent storage are posed. Most traditional stored-concentrated models can have challenges on the data’s integrity since other unauthorized users may easily manipulate or delete the video data. This paper explores the design of a decentralized CCTV surveillance system and with motion detection and preprocessing and cloud computing technology. Focused on motion detection, video frames are recorded and compressed with the help of OpenCV and then stored on AWS S3 for further instant access and in AWS Glacier as final storage. Each of the defined operations—upload, deletion, and modification—of the stored video frames is logged transparently on the Ethereum block chain. AWS provides scale and security to the cloud, and block chain provides for the possibility of non-tamperable records. This architecture does not only secure the video data from other people’s violation as well as prevent themselves from being permanently erased but also manage massive video data. The findings presented here show that integration of AWS cloud services with block chain could provide a highly secure, scalable, and transparent solution for today’s CCTV systems.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room F Pune, India

12:15pm IST

Adaptive Ensemble Classifier for data stream analysis-Flight Stream data
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Shailaja B. Jadhav, D. V. Kodavade, Suhasini S. Goilkar
Abstract - Data centric applications are increasing worldwide, inspiring data scientists to devise more sophisticated methods capable of modelling highly dynamic, extremely speedy data. There are existing approaches which adopt concept learning, dynamism, combining different approaches and heterogeneous classifiers. But, very few of them consider real time data generated through live data savvy applications. This necessitates Streaming data analytics as emerging area of research traditional data mining is not sufficient to achieve desired efficacy. This research aims to focus on streaming data classification particularly flight stream data and presents a comprehensive design framework of multi-layered ensemble built through pool of classifiers selected with prequential evaluation. The model is experimented with various known platforms of streaming data analysis like scikit multiflow, MOA etc. through systematic experimental work. Also, considering the volume of streaming data the experiments have also utilised GPU environments and Google TensorFlow wherever necessary. This Research addresses data streaming analytics majorly, as it needs more attention from research community. There is still scarcity of established benchmarks and standardized frameworks. Major observations, evaluation of design finds that the designed model is able to capture the dynamic nature and improves the classification accuracy as compared with that of the available traditional ensemble models.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room F Pune, India

12:15pm IST

Advancing Energy Efficiency in 6G Networks Through Empirical Analysis of Intelligent Configuration Models
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - S. P. Vibhute, S. C. Patil, S. A. Bhisikar
Abstract - The relentless advancement of wireless communication technologies has ushered in the era of 6G networks, necessitating innovative strategies to enhance their energy efficiency without compromising performance. This study addresses the critical need for sustainable and efficient 6G networks, particularly in the context of growing environmental concerns and escalating energy demands. Existing models, while foundational, often fall short in optimizing energy consumption, grappling with issues such as high latency, increased complexity, substantial costs, and limited scalability. To bridge this gap, our work systematically reviews and analyses various models, including Sleep Scheduling and Intelligent Routing, among others, to augment the energy efficiency of 6G networks. The review process is comprehensive and multidimensional, comparing these models across key performance metrics such as delay, complexity, cost, energy efficiency, and scalability. By employing a meticulous and structured approach, this study elucidates the strengths and limitations of each model, providing a holistic understanding of their applicability in real-world scenarios. The implications of this work are far-reaching, offering invaluable insights for stakeholders in the wireless communication domain. It equips practitioners and researchers with empirical evidence to identify and implement the most optimal models, thereby significantly enhancing the efficiency and sustainability of 6G networks. The findings from this study are poised to contribute substantially to the development of more robust, energy-efficient, and scalable wireless communication systems, aligning with the global drive towards sustainable technological advancements.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room F Pune, India

12:15pm IST

An Approach for Real-Time Object Tracking Integration with Adaptive Occlusion Handling on the Elderly-Care Robot
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - The Tung Than, Thi Phuong Nhi Le, Dong Thanh Vo, Minh Son Nguyen
Abstract - Object tracking is crucial in computer vision, particularly in robotics, but Visual Object Tracking (VOT) faces significant challenges, with occlusion being the most critical. Occlusion disrupts tracking accuracy and poses difficulties when integrating VOT algorithms into embedded robotic systems due to computational and real-time constraints. To address this, we propose a robust method tailored for resource-limited systems, combining the Kernelized Correlation Filter (KCF) and Kalman Filter (KF). By leveraging the Average Peak-to-Correlation Energy (APCE) index, our method detects occlusion, dynamically adjusts the model’s learning rate, and improves performance under challenging conditions. Experimental results on the OTB-100 benchmark highlight our tracker’s effectiveness in handling occlusion, achieving a success rate of 0.602. This demonstrates the method’s robustness under challenging conditions while maintaining real-time processing at 30 FPS (Frame per Second) on Jetson Nano, making it an ideal solution for embedded robotic systems.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room F Pune, India

12:15pm IST

Comparative Evaluation of LLAMA2 in Medical Applications
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Pulipaka Hrishitha, Hima Atluri, Kovvur Ram Mohan Rao
Abstract - In this study, we evaluate two distinct chatbot models integrated into a comprehensive healthcare platform, with a focus on addressing medical and mental health inquiries. The chatbot, driven by LLAMA 2, equipped with an inbuilt Retrieval-Augmented Generation (RAG) mechanism, specializes in retrieving and generating precise responses to medical queries and is tailored to offer personalized and empathetic support for mental health concerns. Through meticulous analysis, we assess the effectiveness of these chatbots against a spectrum of functional and non-functional requirements, encompassing usability, security, scalability, accuracy, and empathy. Our investigation delves into LLAMA 2's performance across four distinct scenarios related to mental health inquiries. These scenarios involve variations in fine-tuning and the provision of custom prompts to the chatbot. We scrutinize LLAMA 2's responsiveness in both finetuned and non-fine-tuned states, as well as with and without custom prompts, aiming to discern the impact of these optimization strategies on the chatbot's capacity to deliver empathetic and supportive responses. Our findings provide valuable insights into the nuanced intricacies of LLAMA 2's role in mental health support within AI-driven healthcare solutions, offering implications for further development and refinement in this critical domain.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room F Pune, India

12:15pm IST

EduShift: AI-driven web-based Application for Personalized Learning
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - V.S.N. Murthy, S. Sai Nikitha, T. Nithya Sri, P. Sushma, V. Kavya Harshitha, M. Hema Lalitha
Abstract - Users today often have to navigate multiple websites to find information that matches their preferred level of understanding and format, whether it's text, audio, or video. This fragmented approach can be time-consuming and frustrating, especially when seeking information suited to specific needs. The challenge is to find a streamlined solution that provides comprehensive, and customizable content in one application eliminating the need for users to jump from one site to another. Our project resolves this issue by developing a unified web-based application that allows users to input a topic and select their preferred content format and level of understanding. The platform uses LLM to generate detailed information and seamlessly convert it into the chosen format, whether it be text, audio, or video. This integrated approach ensures that users receive the information they need in their desired format, all in one convenient location. By simplifying the process, our platform provides a more efficient and user-friendly way to access information suited to their preferences, making it easier for users to learn.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room F Pune, India

12:15pm IST

Electric Vehicle Sales Prediction using Machine Learning and Statistical Models
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Lakshya Khanna, Shriniwas Mahajan, Varun Kadu, Sudhanshu Maurya, Firdous Sadaf M. Ismail, Rachit Garg
Abstract - Sales forecasting assumes a significant part in essential navigation and asset allotment for organizations across different businesses. Realizing the patterns can change and help in the plan of market procedure, particularly now, when the Electronic Vehicles (EV) market is at its pinnacle. In this paper, we investigate the utilization of measurable models and some high-level AI procedures, specifically Random Forest and Long Short-Term Memory (LSTM) models, for anticipating sales information patterns. The review plans to assess the exhibition and dependability of these models in estimating sales data, utilizing genuine world datasets spreading over several years. Execution assessment of the models is led utilizing measurements like Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and R-squared. Also, stability analysis is performed to evaluate the unwavering quality of each model in catching and foreseeing exact patterns. The discoveries of the exploration feature the viability of the measurable models and ML models in anticipating sales data patterns. The two kinds of models show promising execution, with the LSTM model areas of strength for displaying in catching transient conditions and long-haul designs in the sales data. In any case, contrasts in execution and strength between the models are noticed, giving important experiences to choosing the most appropriate determining approach in view of explicit business prerequisites.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room F Pune, India

12:15pm IST

Enhanced Dental Cavity Detection Using Riemannian Residual Networks and Improved Sooty Tern Optimization
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Ravi Kumar Suggala, Penumala Syamya, Pokuri Venkata Naga Rohitha, Nunna Reshma Sri Hanu, Vuyyuri Gnana Prasuna, Vegesana Naga Sai Pujitha
Abstract - Dental cavity identification using advanced image processing and machine learning techniques, especially through X-rays, plays a crucial role in early diagnosis and treatment planning. Traditional detection systems often suffer from high error rates and low accuracy. To address these challenges, a sophisticated model based on Riemannian Residual Neural Networks with Improved Sooty Tern Optimization (RR2Net-ImSTOpt) is proposed. The model uses the DENTEX dataset for analysis, incorporating noise reduction and image enhancement using Guided Box Filtering (GBF). Feature extraction is performed using the Inception Vis-Transformer, followed by optimization of RR2Net's weight parameters via the Improved Sooty Tern Optimization Algorithm. This approach achieves impressive results with a recall of 99.8% and an accuracy rate of 99.9%, surpassing current methods in accuracy and reducing false positives. RR2Net-ImSTOpt’s capability to handle large medical datasets makes it an ideal solution for clinical dental cavity detection, enhancing diagnostic efficiency and precision.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room F Pune, India

12:15pm IST

Predicting Problematic Internet Use Severity: A Machine Learning Approach Using Physical Activity and Behavioral Data
Friday January 31, 2025 12:15pm - 2:15pm IST
Authors - Aisha Karigar, Mohammed Qadir Ternikar, Harsh Nesari, Vanashree N, Prema T. Akkasaligar
Abstract - Problematic Internet Use (PIU) is a growing concern, especially among adolescents, with significant impacts on mental and physical health. This study aims to predict the severity of PIU, measured by the Severity Impairment Index (SII), using a combination of physical activity, demographic, and behavioral data. Machine learning models, including XGBoost, CatBoost, TabNet, and LightGBM, were employed to classify participants into SII categories: none, mild, moderate, and severe. Data were sourced from the Healthy Brain Network (HBN) dataset, which includes accelerometer data, internet usage, fitness assessments, and physiological measures from over 3,000 participants aged 5 to 22 years. Key feature engineering steps included creating interaction terms (e.g., BMI × Age) and applying Autoencoders for dimensionality reduction on the high-dimensional actigraphy data. The results indicated that CatBoost performed best in predicting minority SII categories, handling imbalanced data effectively. XGBoost and LightGBM demonstrated stable performance, while TabNet provided interpretability but lower overall predictive power. Evaluation metrics, particularly Quadratic Weighted Kappa (QWK), were used to assess model performance, with QWK offering insights into the ordinal nature of misclassifications. This study highlights the value of combining physical activity and behavioral data in predicting PIU severity. The findings underscore the potential of machine learning in identifying individuals at risk for severe PIU and suggest avenues for future interventions to reduce the negative impacts of excessive internet use.
Paper Presenter
Friday January 31, 2025 12:15pm - 2:15pm IST
Virtual Room F Pune, India

2:00pm IST

Session Chair Remarks
Friday January 31, 2025 2:00pm - 2:05pm IST
Invited Guest/Session Chair
avatar for Mrs. Sharmila Kunde

Mrs. Sharmila Kunde

Technology Advisor, Vidya Vikas Mandal, Margao, Goa, India
Friday January 31, 2025 2:00pm - 2:05pm IST
Virtual Room A Pune, India

2:00pm IST

Session Chair Remarks
Friday January 31, 2025 2:00pm - 2:05pm IST
Invited Guest/Session Chair
avatar for Dr. Shalini Puri

Dr. Shalini Puri

Associate Professor, Manipal University Jaipur, India.
Friday January 31, 2025 2:00pm - 2:05pm IST
Virtual Room B Pune, India

2:00pm IST

Session Chair Remarks
Friday January 31, 2025 2:00pm - 2:05pm IST
Invited Guest/Session Chair
avatar for Dr. Sopan A Talekar

Dr. Sopan A Talekar

Associate Professor, Karmaveer Adv. Baburao Ganpatrao Thakare College of Engineering, Nashik, India.
Friday January 31, 2025 2:00pm - 2:05pm IST
Virtual Room C Pune, India

2:00pm IST

Session Chair Remarks
Friday January 31, 2025 2:00pm - 2:05pm IST
Invited Guest/Session Chair
avatar for Dr. Lokendra Singh Umrao

Dr. Lokendra Singh Umrao

Associate Professor, Department of Computer Science and Engineering, Madan Mohan Malviya University of Technology, Gorakhpur, India
Friday January 31, 2025 2:00pm - 2:05pm IST
Virtual Room D Pune, India

2:00pm IST

Session Chair Remarks
Friday January 31, 2025 2:00pm - 2:05pm IST
Invited Guest/Session Chair
avatar for Dr. Vandna Rani Verma

Dr. Vandna Rani Verma

Associate Professor,nCSE department,nGalgotias College of Engineering and Technology, Greater Noida, India
Friday January 31, 2025 2:00pm - 2:05pm IST
Virtual Room E Pune, India

2:00pm IST

Session Chair Remarks
Friday January 31, 2025 2:00pm - 2:05pm IST
Invited Guest/Session Chair
avatar for Dr. Aneri Killol Pandya

Dr. Aneri Killol Pandya

Assistant Professor, CSPIT, CHARUSAT University, Gujarat, India.
Friday January 31, 2025 2:00pm - 2:05pm IST
Virtual Room F Pune, India

2:05pm IST

Closing Remarks
Friday January 31, 2025 2:05pm - 2:15pm IST
Moderator
Friday January 31, 2025 2:05pm - 2:15pm IST
Virtual Room A Pune, India

2:05pm IST

Closing Remarks
Friday January 31, 2025 2:05pm - 2:15pm IST
Moderator
Friday January 31, 2025 2:05pm - 2:15pm IST
Virtual Room B Pune, India

2:05pm IST

Closing Remarks
Friday January 31, 2025 2:05pm - 2:15pm IST
Moderator
Friday January 31, 2025 2:05pm - 2:15pm IST
Virtual Room C Pune, India

2:05pm IST

Closing Remarks
Friday January 31, 2025 2:05pm - 2:15pm IST
Moderator
Friday January 31, 2025 2:05pm - 2:15pm IST
Virtual Room D Pune, India

2:05pm IST

Closing Remarks
Friday January 31, 2025 2:05pm - 2:15pm IST
Moderator
Friday January 31, 2025 2:05pm - 2:15pm IST
Virtual Room E Pune, India

2:05pm IST

Closing Remarks
Friday January 31, 2025 2:05pm - 2:15pm IST
Moderator
Friday January 31, 2025 2:05pm - 2:15pm IST
Virtual Room F Pune, India

3:00pm IST

Opening Remarks
Friday January 31, 2025 3:00pm - 3:05pm IST
Moderator
Friday January 31, 2025 3:00pm - 3:05pm IST
Virtual Room A Pune, India

3:00pm IST

Opening Remarks
Friday January 31, 2025 3:00pm - 3:05pm IST
Moderator
Friday January 31, 2025 3:00pm - 3:05pm IST
Virtual Room B Pune, India

3:00pm IST

Opening Remarks
Friday January 31, 2025 3:00pm - 3:05pm IST
Moderator
Friday January 31, 2025 3:00pm - 3:05pm IST
Virtual Room C Pune, India

3:00pm IST

Opening Remarks
Friday January 31, 2025 3:00pm - 3:05pm IST
Moderator
Friday January 31, 2025 3:00pm - 3:05pm IST
Virtual Room D Pune, India

3:00pm IST

Opening Remarks
Friday January 31, 2025 3:00pm - 3:05pm IST
Moderator
Friday January 31, 2025 3:00pm - 3:05pm IST
Virtual Room E Pune, India

3:00pm IST

Opening Remarks
Friday January 31, 2025 3:00pm - 3:05pm IST
Moderator
Friday January 31, 2025 3:00pm - 3:05pm IST
Virtual Room F Pune, India

3:00pm IST

A Comprehensive Exploration of AI-Based Approaches and Various Machine Learning Techniques for Detecting Lung Cancer
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - K Sai Geethanjali, Nidhi Umashankar, Rajesh I S, Jagannathan K, Manjunath Sargur Krishnamurthy, Maithri C
Abstract - This survey provides a comprehensive review of the methods used for lung cancer detection through thoracic CT images, focusing on various image processing techniques and machine learning algorithms. Initially, the paper discusses the anatomy and functionality of the lungs within the respiratory system. The review examines image processing methods such as cleft detection, rib and bone identification, and segmentation of the lung, bronchi, and pulmonary veins. A detailed literature review covers both basic image enhancement techniques and advanced machine learning methods, including Random Forests (RF), Decision Trees (DT), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Gradient Boosting. The review highlights the necessity for reliable validation techniques, explores alternative technologies, and addresses ethical issues associated with the use of patient data. The findings aim to assist researchers and practitioners in developing more accurate and efficient diagnostic tools for lung cancer detection by providing a concise review, thereby helping to save time and focus efforts on the most promising advancements.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room A Pune, India

3:00pm IST

Advancing Brain Tumor Recurrence Prediction: Integrating AI and Advanced Imaging Technologies for Enhanced Prognosis
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Bijeesh TV, Bejoy BJ, Krishna Sreekumar, T Punitha Reddy
Abstract - Integrating artificial intelligence (AI) and advanced imaging technologies in medical diagnostics is revolutionizing brain tumor recurrence prediction. This study aims to develop a precise prognosis model following Gamma Knife radiation therapy by utilizing state-of-the-art architectures such as EfficientNetV2 and Vision Transformers (ViTs), alongside transfer learning. The research identifies complex patterns and features in brain tumor images by leveraging pre-trained models on large-scale image datasets, enabling more accurate and reliable recurrence predictions. EfficientNetV2 and Vision Transformers (ViTs) produced prediction accuracy of 98.1% and 94.85% respectively. The study’s comprehensive development lifecycle includes dataset collection, preparation, model training, and evaluation, with rigorous testing to ensure performance and clinical relevance. Successful implementation of the proposed model will significantly enhance clinical decision-making, providing critical insights into patient prognosis and treatment strategies. By improving the prediction of tumor recurrence, this research advances neuro-oncology, enhances patient outcomes, and personalizes treatment plans. This approach enhances training efficiency and generalization to unseen data, ultimately increasing the clinical utility of the predictive model in real-world healthcare settings.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room A Pune, India

3:00pm IST

ARK : A 2D Fighting Game with Rollback Netcode
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Melwin Lewis, Gaurav Mishra, Sahil Singh, Sana Shaikh
Abstract - This paper focuses on the development of a 2D Fighting Game, using Simple DirectMedia Layer 2 (“SDL2”), Good Game Peace Out (“GGPO”) and the Godot Game Engine. This project was made with the help of the Godot Engine and the prototype test was implemented in the C++ Language with the intent to showcase the GGPO library for the implementation of Rollback Networking in Fighting Games, a technology which makes seamless online-play possible without input delay.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room A Pune, India

3:00pm IST

Detecting the Unusual: Business Outlier Analysis as a Catalyst for Healthcare Innovation
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Anudeep Arora, Neha Tomer, Vibha Soni, Neha Arora, Anil Kumar Gupta, Lida Mariam George, Ranjeeta Kaur, Prashant Vats
Abstract - Improving patient outcomes, maximizing operational efficiency, and guiding strategic decision-making all depend on the capacity to analyze and interpret data effectively in the quickly changing healthcare sector. Finding and analyzing outliers is a major difficulty in healthcare analytics as it can have a big influence on the accuracy and dependability of data-driven conclusions. The significance of business outlier analysis in healthcare analytics is examined in this article, along with its methods, uses, and consequences for payers, providers, and legislators. Healthcare companies may improve their analytical skills, which will improve patient care by improving forecast accuracy and resource allocation. This can be achieved by detecting and resolving outliers.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room A Pune, India

3:00pm IST

Enhanced Obstacle Detection in Adverse Weather Condition for Autonomous Vehicles
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Aswini N, Kavitha D
Abstract - Obstacle detection is vital for safe navigation in autonomous driving; however, adverse weather conditions like fog, rain, low light, and snow can compromise image quality and reduce detection accuracy. This paper presents a pipeline to enhance image quality under extreme conditions using traditional image processing techniques, followed by obstacle detection with the You Only Look Once (YOLO) deep learning model. Initially, image quality is improved using Contrast Limited Adaptive Histogram Equalization (CLAHE) followed by bilateral filtering to enhance visibility and preserve edge details. The enhanced images are then processed by pre-trained YOLO v7 model for obstacle detection. This approach highlights the effectiveness of integrating traditional enhancement techniques with deep learning for robust obstacle detection, even under adverse weather, offering a promising solution for enhancing autonomous vehicle reliability.
Paper Presenter
avatar for Aswini N
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room A Pune, India

3:00pm IST

Implementation of SHA-256 used in Bitcoin Mining on FPGA
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Hetansh Shah, Himangi Agrawal, Dhaval Shah
Abstract - The paper outlines the design, implementation, and evaluation the SHA-256 cryptographic hash function on an FPGA platform, focusing on its use in Bitcoin mining. SHA-256 is a key part of the Bitcoin system, generating unique hash values from data to keep it secure and intact. The goal was to create a fast and low resource utilized, hardware-based version of SHA-256 using VHDL and implement it on the Zed- Board FPGA development platform. The main focus was on the VHDL implementation, making it modular and pipelined to improve speed and efficiency regarding resource utilization. The Zed-Board features the Xilinx Zynq-7000 SoC has been considered for hardware implementation. The design also included message buffering, preprocessing, and a pipeline for hash computation, allowing the system to handle incoming data in real time while producing hash outputs quickly. The algorithm’s functionality was verified using simulation tools in Xilinx Vivado, and the hardware implementation results were compared to previous works. It is clearly depicted the proposed method utilizes fewer resources as compared to the previous works while maintaining a throughput 27% greater than the software solution. The hardware design significantly outperforms software as well as SW/HW (HLS) versions in speed and energy use. The total on-chip power utilized was 12.898 W.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room A Pune, India

3:00pm IST

Predicting Blak Friday Sales: A Machine Learning Approach to Customer Purchase Behaviour
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Janwale Asaram Pandurang, Minal Dutta, Savita Mohurle
Abstract - Black Friday shopping event is one of the most awaited events worldwide now a day, it offers huge discounts and promotions of various products categories. For sellers, it’s important to know the customer purchasing behaviors during this period to predict sales, manage inventory and planning for marketing strategies. This research paper will focus on developing a machine learning model that will predict customer expenses capacity based on previous data from Black Friday, by considering factors such as demographics, product types and previous purchases. After collecting and processing a different dataset, exploratory data analysis was conducted to find important trends. Different machine learning models, like linear-regression, K-nearest-Neighbors (KNN) Regression, Decision-Tree-Regression and Random-Forest-Regression, were applied and tested. The Regression Forest Model with R2 value of 0.81, was found with strong predictive accuracy among those models. This study focuses on machine learning models which will help sellers to improve their productivity and will increase revenue.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room A Pune, India

3:00pm IST

Text-to-Braille Conversion System Using Microcontroller and Servo Motors
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Srikaanth Chockalingam, Saummya B. Gaikwad, Lokesh P. Shengolkar, Dhanbir S. Sethi
Abstract - This paper presents an innovative microcontroller-based system designed to convert text files into Braille script, making Braille content more accessible for visually impaired users. The system leverages an ARM-based microcontroller and servo motors to enable real-time, mechanical translation of text into tactile Braille characters. To facilitate ease of use and to allow offline operation, an SD card is used as the primary storage medium for text files, enabling users to load and convert documents without requiring an internet connection or additional devices. This design emphasizes affordability, scalability, and usability, with the primary aim of making Braille conversion technology more accessible to educational institutions, libraries, and individuals, particularly in resource-limited settings. By reducing dependency on costly, proprietary Braille technology, this system can improve access to information and literacy among visually impaired communities, especially in developing countries where Braille materials are often scarce or prohibitively expensive. The paper thoroughly explores the system’s hardware and software components, detailing the architecture and function of each element within the overall design. A focus on energy efficiency is highlighted to extend the device’s operational time, and efforts to minimize manufacturing costs ensure this solution remains within a low-cost budget. These design choices make this Braille converter a sustainable option for broad deployment and adoption. Further development aims to expand the device's functionality by integrating wireless connectivity for text input, allowing users to access a greater range of content through online sources. Additionally, future iterations could support a larger tactile display, accommodating more Braille cells simultaneously, which would improve the reading experience for users and enhance the system’s application in educational environments.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room A Pune, India

3:00pm IST

The Role of Technology in Subsistence Farming: Data-Driven Insights and Challenges
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Olukayode Oki, Abayomi Agbeyangi, Jose Lukose
Abstract - Subsistence farming is an essential means of livelihood in numerous areas of Sub-Saharan Africa, with a significant segment of the population depending on it for food security. However, animal welfare in these agricultural systems encounters persistent challenges due to resource constraints and insufficient infrastructure. In recent years, technological integration has been seen as a viable answer to these difficulties by enhancing livestock monitoring, healthcare, and overall farm management. This study investigates the effects of technological integration on enhancing animal well-being, with an emphasis on a case study from Nxarhuni Village in the Eastern Cape province of South Africa. The study employs a random sampling method of 63 subsistence farmers to investigate the intricacies of technology adoption in rural areas, highlighting the necessity for informed strategies and sustainable agricultural practices. Both descriptive and regression analyses were employed to highlight the trends, relationships, and significant predictors of technology adoption. The descriptive analysis reveals that 56.6% of respondents had a positive perception of technology, even though challenges like animal health concerns, environmental conditions, and financial constraints persist. Regression analysis results indicate that socioeconomic status (coef = 1.4468, p = 0.059) and gender (coef = -1.1786, p = 0.062) are key predictors of technology adoption. The study recommends the need for specialised educational programs, improvement in infrastructure, and community engagement to support sustainable technology use and enhance animal care practices.
Paper Presenter
avatar for Abayomi Agbeyangi

Abayomi Agbeyangi

South Africa
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room A Pune, India

3:00pm IST

Voice Cloning and Avatar System
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Dinesh Rajput, Prajwal Nimbone, Siddhesh Kasat, Mousami Munot, Rupesh Jaiswal
Abstract - We introduce a system based on neural networks that combines real-time avatar functionality with TTS synthesis. The which system can produce speech in the voices of various talkers, including ones that were not seen during training. To generate a speaker embedding from a brief reference voice sample, the system makes use of a unique encoder that was trained using a large volume of voice data. Using this speaker voice, the algorithm converts text into a mel-spectrogram graph, and a vocoder turns it into an audio waveform. Concurrently, the produced speech is synced with a three-dimensional avatar that produces equivalent lip motions in real time. By using this method, the encoder's learned speaker variability is transferred to the TTS job, enabling it to mimic genuine conversation in the voices of unseen speakers. On a web interface, precise lip syncing of speech with facial movements is ensured via the integration of the avatar system. We also demonstrate that The system's ability to adapt to novel voices is markedly improved by training the encoder on a diverse speaker dataset. In addition, The capacity of the model to generate unique voices that are distinct from those heard during training and retain smooth synchronization with the avatar's visual output is demonstrated by the use of random speaker embeddings, which further showcases the model's capacity to produce high-caliber, interactive voice cloning experiences.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room A Pune, India

3:00pm IST

Adaptive Base Representation Theorem: An Alternative to Binary Number System
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Ravin Kumar
Abstract - This paper introduces the Adaptive Base Representation (ABR) Theorem and proposes a novel number system that offers a structured alternative to the binary number system for digital computers. The ABR number system enables each decimal number to be represented uniquely and using the same number of bits, n, as the binary encoding. Theoretical foundations and mathematical formulations demonstrate that ABR can encode the same integer range as binary, validating its potential as a viable alternative. Additionally, the ABR number system is compatible with existing data compression algorithms like Huffman coding and arithmetic coding, as well as error detection and correction mechanisms such as Hamming codes. We further explore practical applications, including digital steganography, to illustrate the utility of ABR in information theory and digital encoding, suggesting that the ABR number system could inspire new approaches in digital data representation and computational design.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room B Pune, India

3:00pm IST

AI Applied to Stock Market Prediction
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Aarya Pendharkar, Tanmay Pampatwar, Mrunal Zombade, Ashwini Bankar
Abstract - This study offers an effective approach for forecasting changes in stock prices using a binary classification model that makes use of sentiment analysis, technical indicators, and historical stock data. The model forecasts whether a stock will gain or lose the following day, rather than predicting actual stock prices. Technical indicators including moving averages, the Relative Strength Index (RSI), and Bollinger Bands are among the input elements, along with historical price data (open, close, high, low, and volume). Market news and social media data are subjected to sentiment analysis, which produces sentiment ratings (positive, neutral, or negative) in order to identify general patterns in market sentiment. When combined with technical indicators, these mood scores provide additional context for stock movements. The model uses machine learning techniques like XGBoost, SVC, Logistic Regression, and Random Forest, and it outputs a confidence score and a binary forecast. Performance indicators like accuracy, precision, recall, and F1 score are used to assess the model's efficacy. Back testing is also done to evaluate the robustness and performance of the past. The suggested model offers a comprehensive perspective of stock movements by integrating technical and sentimental aspects, producing better prediction skills than conventional models that only use past price data.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room B Pune, India

3:00pm IST

An Efficient Smart Agriculture Monitor System using IoT
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Divyashree H.B., Shirshendu Roy, Supraja Eduru, Dev Sharma, Prathamesh M.Naik
Abstract - In today's tech scenario maximum farmers are practicing unconventional farming which needs hard work, in detail if say it is physical practicing. Especially the day-to-day work if talk about that watering the crop manually without measuring the temperature or having the knowledge of soil moisture in the field. As this is practiced from generation to generation, instead of any gain they are losing manpower, water loss which leads to low production and lower the income of farmer. The development of smart agriculture which is built, gives the surety about the soil's water level and fertility outcome by using several sensors. The sensors which are included is temperature sensors, soil moisture sensors and humidity sensors. The coordinated work with these sensors integrated with IoT and raspberry pi will make it convenient and limits the excessive work of the farmers. The integrated sensor will be placed on the water tank and interconnected with pump source, will give alert notification to the farmer phone about the need of water supply. Most the problems are related to electricity is there this issue can be resolved by connecting the sensors with power source and integrating it with cloud so that every controls of the farm will be in the fingertips of farmers. Similarly for soil moisture sensors in case of water requirement by the soil will be directly reach to users phone. So they can perform irrigation. Cattles responsibility is there, farmers owns livestock in the time of grazing, it may lost or distracted from the pathway. Collar tracker with map support will be beneficial at that time. Livestock abnormal behaviors can be detected, there feeding and water tank refilling can be done by just one click. Cows milk thickness health issue and certain things can be managed. Not only limited to cow but for other livestocks. Climate and weather conditions will be directly updated on the applications. Data analytics support for managing expenses. Graph guidance for the soil moisture, temperature and irrigation support. Water tank percentage filled, air composition whether drop irrigation or sprinkler irrigation needed, temperature, humidity cattles live location on custom based maps will be displayed on the dashboard. Application usage guidance and query support will be there for smooth use of application.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room B Pune, India

3:00pm IST

Cloud-Enabled Learning Management Systems: A Study on Scalability and Personalization
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Ankit Patne, Hritika Phapale, Kaushik Aduri, Hemantkumar B Mali
Abstract - Cloud-based Learning Management Systems (LMS) are secure online platforms that enable L&D professionals to upload their resources and build a comprehensive suite of learning materials. This paper presents an overview of the cloud LMS technologies landscape and examines architecture, scalability solutions, and security perspectives on deploying these tools. We take a look at how these platforms are also incorporating machine learning into their personalization of learning experiences. If you investigate some of the case studies on platforms like Coursera, you will get a sense of practical ways to implement and maintain performance improvements. The present paper, by reviewing current literature reviews major benefits of cloud technologies in improving educational outcomes, which include reducing cost, better scalability, and enhanced security. Such a study contributing to the evolving knowledge base of cloud-based education is shedding new light into the possibility of how cloud LMS can revolutionize IT security education delivery.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room B Pune, India

3:00pm IST

Content Preserve for 3D Video Stabilization using Warping Techniques
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - R.Mehala, K.Mahesh
Abstract - The Content Preserve for 3D Video Stabilization using Warping Techniques for making a hand-held video camera captured using a guided camera motion. This technique enables the simulation of 3D camera movements by modifying the video look it was captured from adjacent views. Its algorithms successfully reproduce dynamic scenes from a single source video by focusing solely on perceptual plausibility rather than perfect reconstruction. The method that modifies a hand-held video camera's output to make it look as though it captured using a directed camera motion. This technique enables the simulation of 3D camera motions by modifying the video to look as though it was captured from adjacent views. It is possible to automatically select a particular wanted camera path. The warp calculated the content maintains the video frame while adhering to sparse deletions suggested by the restored 3D structure. This method works well as seen by the experiments stabilizing difficult movies with dynamic sceneries.
Paper Presenter
avatar for R.Mehala
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room B Pune, India

3:00pm IST

Digital Forecasting as a Tool: Assessing the Performance of Public Sector Banks in India
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - K.Sarvani, Dinesh, Bijith Narayanan, Aayush Rai
Abstract - Digital finance has become a buzzword in every financial service to identify any country's solvency position and competitive environment. This study emphasizes the performance of the banking sector with respective to macro-economic variables to assess the solvency and profitability position of commercial banks in India. Two macroeconomic variables namely gross domestic product, and inflation were considered to identify the performance of nonperforming assets of the public sector banks. There are twelve public sector banks in India as of 2013-24 as per the RBI database. All the public sector banks were considered for the study for ten years. The data was collected from PROWSSIQ for the financial data of public sector banks. Macroeconomic variables were taken from Economic Times data from the published data from web sources. The findings of the study are that non-performing is negatively correlated to inflation and GDP growth rates. The adjusted R Squared value is 61 percent implying that the regressors are perfectly explained that the dependent and independent had a relation. Forecasting the performance of non-performing was done using the SARIMA model. It is found that for all the select banks, non-performing assets are continuously increasing which implies that the recovery of bad debts may be done by the adoption of new fintech apps and it is a positive sign for the performance of the banks in coming years.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room B Pune, India

3:00pm IST

Optimizing HR Utilization in the BPO Industry: The Power of Predictive Analytics
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Anudeep Arora, Neha Arora, Neha Tomer, Ranjeeta Kaur, Vibha Soni, Lida Mariam George, Anil Kumar Gupta, Prashant Vats
Abstract - Effective human resource management is a major issue for the Business Process Outsourcing (BPO) business, which is marked by a high staff turnover rate and a dynamic operating environment. These issues are frequently not adequately addressed by traditional HR management techniques, which results in inefficiencies and higher expenses. BPO companies may improve employee engagement, optimize staffing levels, and anticipate workforce demands with the use of predictive analytics, which makes it a potent option. The use of predictive analytics for efficient HR utilization in the BPO sector is examined in this article. It explores important technologies, tools, and processes; talks about the advantages and difficulties of implementation; and provides case studies of effective deployments. BPO firms may increase labor productivity, lower attrition, and boost overall company success by utilizing predictive analytics.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room B Pune, India

3:00pm IST

RAG Chatbots: Implementing Large Language Models in Retrieval-Augmented Generations
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Kavita Patil, Rohit Patil, Vedanti Koyande, Amaya Singh Thakur, Kshitij Kadam, Kavita Moholkar
Abstract - This paper evaluates a chatbot system designed for personalized business interactions using advanced Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG). The system combines proprietary business data with external databases to improve contextual relevance. A comparative analysis of leading LLMs—Gemini Pro, GPT-4, Claude 2, GPT-3.5, and LLaMA 2—was conducted across benchmarks like MMLU, GSM8K, BigBench Hard, HumanEval, and DROP. Gemini Pro outperformed the others, with scores of 88.9% on MMLU, 86.3% on GSM8K, 78.1% on BigBench Hard, 73.5% on HumanEval, and 79.2% on DROP, showcasing its strength in complex reasoning and long-context retrieval. Fine-tuned with business-specific data, Gemini Pro sets a new standard for high-accuracy, scalable chatbot solutions, ideal for enterprise applications.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room B Pune, India

3:00pm IST

The Role of AI-Powered Chatbots in Mental Health Care for Anxiety and Depression
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Darshana Naik, Aishwarya Bhagat, Amman Baheti, Atharva Kulkarni, Hitesh Kumar
Abstract - This paper examines the potential of AI-powered chatbots to address the growing global need for accessible and effective mental health support. It traces the evolution of chatbots, from rudimentary systems to sophisticated AI-driven platforms, emphasizing advancements in artificial intelligence and natural language processing that enable personalized responses. Driven by the need to overcome barriers of cost, availability, and stigma in mental health care, the paper explores chatbot integration strategies. These include using chatbots for screening and triage, extending therapist reach, bridging care gaps, reaching underserved populations, and leveraging data for personalized interventions. While chatbots show promise in delivering therapeutic support and improving symptoms, they are envisioned as a complement to, rather than a replacement for, traditional therapy. The paper advocates for leveraging AI to enhance the scalability, reach, and personalization of mental health care, ultimately aiming to improve global mental health outcomes. By exploring both the potential and the challenges of AI-powered chatbots, this paper contributes to the ongoing dialogue about the future of mental health care in an increasingly digital world.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room B Pune, India

3:00pm IST

Transforming Sign Language into Emotion-Enhanced Speech with Machine Learning
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Ashwini Bhosale, Laxmi Patil, Gitanjali Netake, Sakshi Surwase, Rutuja Gade, Prema Sahane
Abstract - This paper discusses a project that aims to create a system for translating sign language into spoken words while also recognizing the emotions of the signer. The goal is to make communication easier for Deaf and hard-of-hearing individuals by converting hand gestures into speech and reflecting the signer’s emotional tone in the voice output. This would make conversations feel more natural and expressive, enhancing interactions in both social and work environments. The project uses computer vision and Convolutional Neural Networks (CNNs) to accurately recognize various sign language gestures. To identify emotions, it uses deep learning models like VGG-16 and ResNet, which focus on facial expressions. It also uses Long Short-Term Memory (LSTM) networks to analyze audio input and detect emotional tones in speech. For turning sign language into spoken words, the system employs Text-to-Speech (TTS) technologies like Tacotron 2 and WaveGlow. These tools create natural-sounding speech, and the detected emotions are added to the voice by adjusting tone, pitch, and speed to match the signer’s feelings. With real-time processing and an easy-to-use interface, this system aims to provide quick translation and emotion detection. The expected result is a fully functional system that not only translates sign language into speech but also effectively conveys emotions, making communication more inclusive for Deaf and hard-ofhearing individuals.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room B Pune, India

3:00pm IST

Automatic Generation of Executive Summaries for Online Meetings using NLP: A Review
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Sujith Kumar Banda, Ramzan Shareef, Swathi Sowmya Bavirthi, Mohammed Arbaz Ahmed
Abstract - While meetings help make company decision-making more effective, documenting and distilling the material turns out to be a lot of time-consuming work and may also contain mistakes. The project provides an automated way of transcribing audio recording of meetings into text and applying NLP for perfect creation of useful summaries. As opposed to the existing techniques that resort to either means of human beings or platform-specific ones, our solution is a versatile way that can handle transcripts from a variety of online resources. This is a system that offers both abstractive and extractive summary techniques in the form of developed transformer models, such as BERT, to form logical summaries and TF-IDF and TextRank to focus the most important points in the summary. A wider applicability of Named Entity Recognition (NER) and Part-of-Speech (POS) tagging will allow summarization over key elements, including decisions taken and responsibilities assigned. The approach aims to make the capture of output from the meeting more efficient and reliable by automatically summarizing proceedings in meetings. User input and ROUGE scores will assess how well the system performs and guarantees quality useful summaries to stakeholders.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room C Pune, India

3:00pm IST

Development of an Energy-Efficient Deep Learning Framework for Intrusion Detection in IoT Environments
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Rajeev Sharma, Santanu Sikdar, Govind Murari Upadhyay
Abstract - To protect network infrastructure from new vulnerabilities and security dangers caused by the rapid growth of Internet of Things (IoT) devices, robust and adaptable Intrusion Detection Systems (IDS) are necessary. Due to their limited scalability and reactivity to different attack patterns, conventional intrusion detection systems (IDS) struggle to meet the unique demands of Internet of Things (IoT) networks. The novel Intrusion Detection System introduced in this paper is based on deep learning and is tailor-made for Internet of Things (IoT) environments. It employs complex neural network topologies to enhance the accuracy and efficiency of detection. Regarding the massive amount and variety of data generated by IoT devices, our suggested method improves performance without compromising detection accuracy by combining feature selection and dimensionality reduction strategy. Standard IoT network datasets were used for training and validation, with several assaults implemented to ensure comprehensive threat coverage and practical applicability. The results of the experiments show that the proposed system outperforms the state-of-the-art machine learning-based intrusion detection systems in detection accuracy, false positive rates, and scalability in contexts with limited resources for the Internet of Things.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room C Pune, India

3:00pm IST

Effectiveness of Artificial Intelligence in Stock Market Prediction
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Rutuj Barudwale, Vijeyandra Shahu
Abstract - This article focuses on attempting artificial intelligence in stock price forecasting. Common stock market predictions and their prices can be assessed using dual primary analytical models known as technical and fundamental analysis. I employed a technical analysis of price trends predicting price movements using regression machine learning (ML). For instance, predicting how the price of a particular stock will close at the end of today based on historical price trends. In contrast to this approach of technical analysis, fundamental analysis can be applied to supervised machine learning algorithms to assist with identifying how news and social network users appear to be for or against certain entities. In the technical analysis, the historical price trends are retrieved from Yahoo, and in the fundamental analysis, the stock market tweets are analyzed to assess how the public feels about the stock prices. The findings portray an average performance; therefore, given the present environment of - technology, it is rather optimistic to presume that technology will ever beat the stock market consistently.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room C Pune, India

3:00pm IST

Integrated Approaches for Secure and Predictive Management of Electronic Health Records: A Review
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Satvik Taviti, Srreyasri Kurlagunda, Nandikanti Sri Gayatri, R M Krishna Sureddi, Raman Dugyala
Abstract - Electronic Health Records (EHR) are considered to be amongst the most crucial elements for exchanging data in healthcare services. Thus, security for these records is the keystone of patient privacy and easy cooperation between the service providers. This review looks at four primary approaches to EHR security and predictive management: Blockchain, Attribute-Based Encryption (ABE), Deep Learning, and Access Control Models. Blockchain ensures data integrity, transparency, and traceability but scalability issues, high transaction costs, and interoperability challenges prevent its widespread adoption. ABE is appropriate for fine-grained access control in data sharing under the patient-centred approach but cumbersome and resource-intensive for managing encryption across the large healthcare network. Deep learning helps predictive analytics, personalized medicine, but with high computational demands that affect its real-time application in the clinical environment. While in terms of data confidentiality protection, models such as Role-Based or Attribute-Based Access Control may ensure proper restriction of authorized access, they might not suffice for dynamic, multi-provider health environments. Comparing the techniques will outline their relative merits, weaknesses, and security considerations, thus helping to understand how safe yet scalable systems for EHR storage could be built.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room C Pune, India

3:00pm IST

Integrating AI with IoT: A Review of Applications, Challenges, and Future Directions
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Edidiong Akpabio, Supriya Narad
Abstract - This review aims to understand the integration of two emerging technologies: artificial intelligence and the Internet of Things. IoT is defined as the capability of implementing connections between regular items and industrial apparatuses that can liaise in real-time, exchanging and analyzing data. AI is an ideal companion to IoT in the sense that it brings decision-making into the equation and boosts the effectiveness and functions of IoT systems. This paper aims to review the use of AI and IoT in various fields, namely, smart cities, health, farming, and transport. In smart cities, IoT and AI applications are also applied to enhance traffic, energy consumption systems, and urban design. It has changed the way that the healthcare industry operates through better methods of patient monitoring, performance analysis, and telehealth. In agriculture, IoT sensors help monitor the effectiveness of crop management and the use of AI-based automation. It also covers the implementation of AI and IoT in autonomous vehicles, particularly the use of sensors for data processing, decision-making, and real-time data communication. However, the use of AI and IoT has some limitations, such as data limitations, security and privacy, and environmental impact. Indeed, the paper dwells upon these issues and provides the outlook for further research regarding edge AI, IoT sustainability, and the further evolution of the connections. With technological progress still in the process of evolving AI and IoT, future advancements hold more potential in terms of creating better connected, efficient, and sustainable solutions, not to mention the fact that AI is capable of solving existing challenges.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room C Pune, India

3:00pm IST

Integrating Edge-Cloud Computing and IoT for Real-time Food Quality Assessment
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Rydhm Beri, Parul Sachdeva
Abstract - The advent of IoT technology has significantly transformed the industrial sector, paving the way for the emergence of Food Industry 4.0. This research explores the integration of edge–cloud computing and IoT to create a smart framework tailored for the food industry. Central to this framework is the appli-cation of a Bayesian belief network (BBN) on an edge–cloud platform, enabling data-driven insights into food quality. The framework assesses data to calculate the Probability of Food Quality (PFQ) and utilizes the Food Quality Analysis Measure (FQAM) to evaluate food outlets. A bi-level decision-tree model further enhances the evaluation process by providing an in-depth analysis of food quality metrics. To address concerns around data security, blockchain technology is implemented, ensuring the protection of food-related information. The model is rigorously tested on a comprehensive dataset encompassing 43,520 instances from four restaurants. Simulation results highlight its high performance, achieving a temporal delay of 96.43 seconds, and the system demonstrates an accuracy of 98.93%, showcasing its robustness in real-world applications.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room C Pune, India

3:00pm IST

Optimizing Plant Disease Detection with a Novel Deep Ensemble Framework
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Rydhm Beri, Parul Sachdeva
Abstract - Plant diseases present a serious threat to all forms of life. Early detection is vital which allows farmers to take prompt action, improving both their response and productivity. Our research centers on five common rice leaf diseases—bacterial leaf blight, leaf blast, brown spot, leaf scald, and narrow brown spot—along with a category for healthy leaves. Additionally, we examine two types of betel leaves: healthy and unhealthy. This study propose an innovative deep ensemble model that combines the EfficientNetV2L, InceptionResNetV2, and Xception architectures. This model addresses issues of underfitting and performance by utilizing advanced techniques including data augmentation, Global Average Pooling, preprocessing, Dropout, L2 regularization, PReLU activation, Batch Normalization, and multiple Dense layers. This robust approach surpasses existing models by managing both underfitting and overfitting, while delivering superior performance.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room C Pune, India

3:00pm IST

Sign Language Recognition and Caption Generation: A Review
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - T. Sridevi, Chidhrapu Harini, Kurella Sai Veena
Abstract - Sign Language is the primary means of communication among 1.8 million deaf people across India, and although Indian Sign Language (ISL) translation to technology-based effective solutions is still very limited, tremendous effort has so far been made in global research in sign language recognition. Nevertheless, the challenge persists in transcoding of text from ISL. This project will fill the vacuum by developing a deep-learning-based model capable of generating subtitles for ISL videos. With a pre-trained Convolutional Neural Network (CNN) for spatial feature extraction and a Recurrent Neural Network (RNN) for encoding the temporal pattern, the model learns on the Indian Sign Language Videos dataset. Designed in a manner to achieve high-accuracy captioning of ISL for reliable communication with the Indian deaf community. This will provide access to means of communication for millions of ISL users, but at the same time offers a critical communication tool meant to facilitate improvement in circles of education, social life, and professional circles in India.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room C Pune, India

3:00pm IST

Time Series Analysis for Stock Market Prediction: Techniques, Challenges, and Future Directions
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Anudeep Arora, Vibha Soni, Lida Mariam George, Anil Kumar Gupta, Ranjeeta Kaur, Neha Arora, Neha Tomer, Prashant Vats
Abstract - In the field of financial analytics, stock market prediction continues to be one of the most difficult and sought-after objectives. A key component of stock price modeling and forecasting is time series analysis, a statistical technique that examines sequences of data points gathered at successive times. A thorough review of time series analytic techniques for stock market prediction is given in this article. These techniques include machine learning and deep learning, as well as more sophisticated approaches like GARCH and ARIMA. It addresses the drawbacks and advantages of these methods, looks at the difficulties in putting them into practice, and identifies new developments in time series forecasting. Investors and analysts may improve their ability to anticipate the future and make better judgments in the ever-changing stock market environment by being aware of these techniques and how they are used.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room C Pune, India

3:00pm IST

“Psychoacoustic Wellness”: Unveiling the Efficacy of Vedic Chants and Music on Alleviating Depression
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - M R Shreyaank, Dhanush Karthikeya A J, Dhanush Rajan S, Ashwini Bhat
Abstract - This research attempts to investigate the potential healing effects of Vedic chants and music on the human brain through an in-depth analysis of EEG signals. The Vedic chants are known for their inherent calming and meditative attributes and are believed to impart positive influences on the human mind and body. The study employs a simulative model to analyse EEG signals during exposure to Vedic chants. Recorded EEG signals from MDD (major depressive disorder) subjects are subjected to preprocessing and feature extraction processes involving frequency-domain analysis and power spectral density. The study compares the extracted features between conditions of Vedic chant exposure and controlled settings and shows that there is significant increase in alpha and beta powers after listening to the specified chants. Rejuvinating and Calming chants showed the best positive impact.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room C Pune, India

3:00pm IST

A Comparative Study of Machine Learning and Deep Learning Techniques for Cybercrime Detection on Facebook and Twitter
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Suresh V Reddy, Sanjay Bhargava
Abstract - Cybercrime on social media platforms such as Facebook and Twitter has emerged as a significant challenge due to the open, interactive nature of these platforms. Various machine learning (ML) and deep learning (DL) techniques have been deployed to detect different forms of cybercrime, including phishing, spamming, hate speech, and identity theft. This paper provides a comparative analysis of these approaches, focusing on their application to cybercrime detection on Facebook and Twitter. Through a detailed literature review, we evaluate the strengths and weaknesses of these techniques, considering their performance and scalability. Moreover, the ethical challenges and the need for privacy-preserving mechanisms are discussed, along with future directions for research.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room D Pune, India

3:00pm IST

A Review of Artificial Intelligence Techniques for Brain Tumour Segmentation and Classification
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Rakesh Babu B, Rajesh V, Syed Inthiyaz, Srinivasa Rao K, Sri Sravan V
Abstract - Brain tumours are life-threatening disorders with significant fatality rates. Patients have a higher chance of survival when brain tumours are diagnosed early and treated more effectively. Therefore, for the purpose of better and boost the early identification of brain tumours, computerized segmentation as well as classification techniques are needed. It is possible to safely and promptly detect tumours using brain scans such as computed tomography (CT), magnetic resonance imaging (MRI) and other techniques. Revolutionary changes have occurred in many different disciplines as a result of recent developments in artificial intelligence (AI). AI models are becoming essential tools for interpreting images in bio medical field. Deep learning is one of these that signifies extraordinary capacity to deal with enormous data collection, revolutionizing numerous fields in the biomedical profession. This article evaluates a state-of-the-art AI based segmentation and classification system and discovers major classes for brain tumours. The potent learning capability and effectiveness of AI approaches have been assessed. Convolutional Neural Network (CNN) is one of the AI subfields that has demonstrated remarkable performance in analysing medical imagery. Consequently, the processing of medical imagery, particularly brain MRI images, was the main emphasis of this review paper, which also examined different deep learning model architectures in addition to CNN.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room D Pune, India

3:00pm IST

AI-DRIVEN OPTIMIZATION IN HEALTHCARE SUPPLY CHAINS
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Shubham Kadam, Chhitij Raj, Pankajkumar Anawade, Deepak sharma, Utkarsha Wanjari, Janhvi Shirbhate, Sharvari Pipare
Abstract - Artificial Intelligence (AI) is increasingly being hailed as the key to the future of healthcare supply chain management in countries such as India, where healthcare is a particularly complex setting for an integrated supply chain. This review presents the various Data-driven Artificial Intelligence (AI) technologies such as Machine Learning (ML), Natural Language Processing (NLP), Computer Vision, and Robotic Process Automation (RPA) that help in the automation of essential processes like demand forecasting, inventory management, and cold chain logistics in an efficient and timely manner. AI helps deliver vital supplies on time and minimizes any disruptions of services by utilizing predictive analytics and real-time monitoring. However, high implementation costs, data privacy concerns, the need for integration with legacy systems, and a need for more skilled professionals are barriers to the adoption of AI computing. To extract the maximal potential AI can offer healthcare logistics, the issues above need to be addressed. Upcoming research directions include further development in quantum computing, IoT integration, and collaborative AI platforms to fulfil resilience and sustainability objectives for supply chains. The results underscore the potential of AI to transform health supply chains and provide an opportunity to realize more scalable, responsive, and efficient health services.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room D Pune, India

3:00pm IST

Enhancing Portfolio Analysis and Stock Prediction Through LSTM and XGBoost Integration
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Rajeshree Khande, Sachin Naik, Akshay Tayade, Amar Kale, Kunal Phalke
Abstract - The authors propose for the LSTM-XGBoost model for portfolio optimization as well as stock price prediction. The model has incorporated the benefits derived from XGBoost, a gradient-boosting algorithm that enhances the ability of a model to predict structured and improved data, and Long Short-Term Memory (LSTM) networks, which excel at characterizing time-series data based on temporal relationships. The XGBoost model takes advantage of the LSTM model by utilizing the anticipated outputs it makes for improving the precision and overall efficiency of the model while the LSTM model is designed to work with ordered data peculiar to stock markets specifically on patterns and trends over time. In the study authors employ this type of hybrid to determine variables such as volatility and the moving average of historical stock price index of NIFTY50. The authors have obtained total model accuracy of 98.33%. Authors also use the Sharpe ratio to maintain an optimal portfolio because it shows investors the optimal ratio of expected stock returns. This research contributes to enhancing financial forecasting by integrating deep learning and machine learning techniques, ultimately offering the formulation of a new risk avert portfolio as well as stock price prediction.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room D Pune, India

3:00pm IST

From Prompts to Programs: A RAG-Based Framework for Code Synthesis
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Jaiditya Nair, Sunil Kumar
Abstract - The increasing demand for AI-driven solutions in development has encouraged people to conduct various research into generating code from natural language prompts. My paper presents a Retrieval-Augmented Generation (RAG) pipeline for code generation, making use of embedding models, contextual retrieval, and advanced language models such as Mistral and CodeLLama. This approach incorporates document indexing and metadata extraction to create context-aware code snippets and at the end of the process, we get a python file with the generated code present in it.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room D Pune, India

3:00pm IST

Impact of the Internet on Human Life a data-driven Analysis using Machine Learning and Statistical Correlations
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Nishita Shekhar Bala, Sree Vani Bandi, Stephen R, Ravi Dandu, Balakrishnan C
Abstract - These days internet is became an essential part of human life and affects various domains which includes education, business, social interactions, mental health. It pushes the society ahead through increasing innovations, amplifying learning techniques, connecting people across the globe and access to vast resources which makes it a valuable tool in this modern society. But it comes with problems such as internet addiction, sleeping disorders, health complications. This abstract discusses about dual impact of internet uses, focusing on its significant benefits and possible dangers. Hence, there is need to mange use of internet so one can make use of its benefits at the same time reducing the affects which are caused by internet on human life.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room D Pune, India

3:00pm IST

Sign Language Recognition Using CNN Model
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Chandan Raj B R, A. Yasaswi, Deepika K, Uday Bhaskar Reddy, Delina Yadav K, Joshna K
Abstract - It is quite difficult to communicate with deaf individuals. This article extends the complexity of Indian Sign Language (ISL) character classification. Sign language is insufficient for the hearing and speaking disabled. Hand gestures of disabled individuals may appear confused to those who have not learnt the language. Communication should be two-way. In this essay, we will discuss how to learn a language through sign language. Images are processed using computer vision processes, including grayscale conversion, dilation, and masking. We employ Convolutional Neural Networks (CNN) to train and recognize images. Our example has an accuracy of approximately 95%. Gestures serve as a nonverbal communication tool in language. People with hearing or speech difficulties frequently utilize them to communicate with others or among themselves. Many loudspeakers are created by various manufacturers around the world. This study demonstrates that many experiments are undertaken each year, with several articles published in journals and conferences, and that research on vision-based gesture recognition is ongoing. Cognitive navigation focuses on three areas: information retrieval, environmental information, and gesture representation. In terms of identity verification, we also evaluated the authentication system's effectiveness. The physical movement of the human hand generates gestures, and gesture recognition contributes to improvements in autonomous vehicle operation. This paper use the convolutional neural network (CNN) classification technique to detect and recognize human motions. This workflow consists of region-of-interest coordination via masking, finger segmentation, normalization of segmented finger pictures, and finger recognition using a CNN classifier. Use the mask to separate the hand portion of the image from the rest of the image. The histogram equalization approach is used to improve the contrast of each pixel in an image. This work uses a variety of scanning techniques to classify fingerprints from hand photographs. The segmented fingers from the hand image are put into the CNN classification algorithm, which separates the image into different groups. This research proposes gesture recognition and recognition methods based on CNN classification, and the technology achieves good performance using cutting-edge methodologies.
Paper Presenter
avatar for Deepika K
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room D Pune, India

3:00pm IST

Strategic Analysis for Internal Audit and Data Analytics: Enhancing Audit Effectiveness through Data-Driven Insights
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Anudeep Arora, Ranjeeta Kaur, Neha Tomer, Vibha Soni, Neha Arora, Anil Kumar Gupta, Lida Mariam George, Prashant Vats
Abstract - The incorporation of data analytics into internal audit operations is a noteworthy progression in augmenting the efficacy and productivity of audits. In this paradigm, strategic analysis refers to using data-driven insights to evaluate risks, expedite audit procedures, and enhance organizational controls. This article examines the use of strategic analysis in data analytics and internal audits, including important techniques, advantages, and difficulties. It talks about how sophisticated data analytics methods, such as machine learning, statistical analysis, and visualization software, can change the way that auditing is done today. In addition, the paper looks at case studies and potential future developments in the subject, giving readers a thorough understanding of the various ways internal auditors might use data analytics to provide audit results that are more precise and useful.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room D Pune, India

3:00pm IST

Uncertainty-Based Decision-Making in Pandemics
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Artika Singh, Manisha Jailia
Abstract - Effective management of infectious disease outbreaks rely heavily on informed decision-making processes. There are many approaches given for decision-making some of them are expert decision-making, creative problem solving, public engagement, and decision-making under deep uncertainty (DMDU) in outbreak management (OM). The integration of these aspects is critical to enhancing the responsiveness and efficiency of public health interventions. This paper discusses the current state of expert decision-making processes, the role of creativity in managing complex situations, the impact and challenges of incorporating public and patient engagement (PPE) in OM. The paper concludes with recommendations for future research and practice to improve outbreak management strategies.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room D Pune, India

3:00pm IST

Voting System Using Blockchain
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Prateeksha P Malagi, Priyanka R Patil, Shamshuddin K G, Suneeta V Budihal
Abstract - The advent of blockchain technology presents a transformative opportunity for enhancing the integrity and efficiency of voting systems. This paper explores the design and implementation of a blockchain-based voting system aimed at addressing common challenges faced in traditional electoral processes, such as voter fraud, lack of transparency, and low participation rates. By leveraging the decentralized and immutable nature of blockchain, our proposed system ensures secure voter authentication, real-time vote tracking, and tamper-proof record keeping. The study outlines the technical architecture, including smart contracts and cryptographic techniques, while evaluating the system's performance through simulated voting scenarios. Furthermore, we discuss the implications of this technology for promoting democratic engagement and restoring public trust in electoral outcomes. Our findings suggest that a blockchain-based voting system not only enhances security and transparency but also offers a scalable solution to modern electoral challenges.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room D Pune, India

3:00pm IST

Anuvaad: Integrating Technology with Indigenous Languages
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Manjusha Pandey, Rajeev Kumar, Satyam Tiwary, Yuvraj Singh, Oindrella Chatterjee, Siddharth Swarup Rautaray
Abstract - This paper delves into the complexities of providing equitable access to multimedia content across India's diverse linguistic landscape. It proposes innovative strategies for translating English video content into Indian regional languages, leveraging cutting-edge technologies such as machine translation, speech recognition, and text-to-speech synthesis. The suggested approach involves a systematic four-phase process, encompassing audio separation, text conversion, machine translation, and speech synthesis. [1] By utilizing open-source tools like IBM's Watson supercomputer and the Flite engine from Carnegie Mellon University, the system achieves a commendable 79% accuracy in terms of naturalness and fluency, as evaluated by native speakers. However, challenges persist in handling multi-speaker conversations and accommodating a broader range of Indian languages. Despite these limitations, the research lays a solid foundation for future advancements in the field. By fostering cross-cultural communication and knowledge dissemination, the proposed solution holds the potential to bridge linguistic barriers, empower marginalized communities, and foster an inclusive digital ecosystem in India.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room E Pune, India

3:00pm IST

ARTIFICIAL INTELLIGENCE IN ORGANIZATIONAL CULTURE ASSESSMENT: TRANSFORMING INSIGHTS AND STRATEGIES
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Shubham Kadam, Chhitij Raj, Pankajkumar Anawade, Deepak sharma, Utkarsha Wanjari
Abstract - This paper explores the phenomenon of Artificial Intelligence (AI) transformation in organizational culture evaluation, discussing capabilities, advantages, obstacles and future direction. While traditional means of mining forms like surveys and interviews are often lengthy and flawed due to human biases, AI tools rely on real-time data, natural language processing, and predictive analysis to deliver objective insights instantly. Such applications, including sentiment analysis, behavioural analytics, and cultural diagnostics, allow organizations to mitigate cultural misalignments in advance at the organizational level or within specific teams, idem for the employee's engagement and inclusivity. Nonetheless, ethical issues related to data privacy, security and algorithmic wage discrimination continue to pose significant challenges. The implications of this study highlight the increasing importance of artificial intelligence in enabling organizations to build dynamic, resilient, and agile organizational cultures.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room E Pune, India

3:00pm IST

Development of Flutter Mobile Application for Real-Time Plant Disease Detection Using Convolutional Neural Networks and TensorFlow Lite
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Sakshi Sharma, Tanisha Verma, Shailesh D. Kamble
Abstract - Accurate, timely detection of plant disease is critical to protect crop from being damaged and increase agricultural productivity. Many disease identification methods are labor intensive and only practical with an expert set of trained eyes. A mobile application for real time plant disease detection using CNNs presented in this paper allows farmers to have a simple yet powerful access to a diagnostic tool. CNN was trained on a big collection of plant leaf images to discriminate between diseases using Keras and TensorFlow. The application was built using Flutter for cross platform mobile development, trained model deployed on mobile devices using TensorFlow Lite, which allows offline inference. Users can capture images of affected plant leaves and get immediate diagnostic feedback as to the potential disease involved. Following data preprocessing and model optimization, the application uses a lightweight architecture that achieves high accuracy while meeting requirements for mobile deployment. This research shows integration of AI with mobile technology can provide a scalable, efficient and accessible solution to crop disease detection. The system as proposed is capable of improving crop health management, reducing losses, and working towards global food security.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room E Pune, India

3:00pm IST

GAMIFICATION IN HUMAN RESOURCE MANAGEMENT WITH ARTIFICIAL INTELLIGENCE: ENHANCING ENGAGEMENT AND PRODUCTIVITY
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Utkarsha Wanjari, Shubham Kadam
Abstract - Gamification in HRM through AI is thus a total revolution that can maximize the engagement and productivity of employees. Game-like qualities such as rewards, badges, leaderboards, and challenges incorporated in the HR processes create a captivating environment that motivates and pushes an employee into an achievement culture. AI amplifies the effect of gamification: it enables data-driven insights, personalized experience, and real-time feedback loops. The paper also looks into the psychological underpinnings of gamification intrinsic and extrinsic motivation and their alignment with the organizational goals. It analyzes some of the challenges in incorporating gamification, including ethical considerations, potential overuse, and the balance between entertainment and productivity. It also reflects on some success stories and presents a pathway to implementing gamified AI solutions into the existing HR framework. This is because gamification, combined with AI, will alter the way human resource practice prevails, uplift employee productivity, boost employee satisfaction, and contribute to the long-term success of an organization. The present research study aspires to provide business organizations with the actionability of a very innovative method to remain ahead of their game in the changed wilderness of the workplace.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room E Pune, India

3:00pm IST

GREEN ICT LEADERSHIP IN E-GOVERNANCE: STRATEGIES FOR REDUCING THE CARBON FOOTPRINT OF DIGITAL GOVERNMENT OPERATIONS
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Shubham Kadam, Chhitij Raj, Pankajkumar Anawade, Deepak sharma, Utkarsha Wanjari
Abstract - The paper investigates Green ICT leadership in e-governance towards carbon footprint mitigation from the digital government. E-governance uses information and communication technology (ICT) to deliver administrative services through enhanced technology in this service chain, thus increasing the efficiency of their services, which is guided by an aim for complete transparency that requires accurate information. However, digitalization is responsible for environmental problems such as carbon emissions produced by data centres, digital infrastructure, and devices. The paper emphasizes the importance of vision-oriented leadership in promoting sustainability through processes of strategic thinking, collaboration and innovation. The Guide presents a series of critical strategies, including energy-efficient data centres, virtualization and cloud computing, sustainable procurement, and citizen engagement to build green practices. Innovative technologies such as AI, IoT, and blockchain are labelled enablers for optimizing energy consumption and increasing transparency.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room E Pune, India

3:00pm IST

Machine learning based Advance K-Means Architecture for Grass Quality Recognition
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Alpa R. Barad, Ankit R. Bhavsar
Abstract - Analysis of grass quality is essential to improve cattle health. To improve animals' health and productivity, it is necessary to survey quality food. Grass is a primary and major source of food for every cattle. As a part of vegetation quality of grass is decreasing day by day, and it’s also not possible to survey fresh grass on a daily basis. Proposed research is used to analyze the quality of grass based on its color space. The quality of grass differs over the grass species and weather, and it's become more difficult with a single model to recognize its quality. To solve this problem proposed research uses machine learning based hybrid approach. The proposed research uses Median filter with kmeans clustering. Based on the clustering, the Simulation uses color deflection code to identify threshold values for a given species of grass. Proposed research finds the remarkable performance of three different qualities of grass. Simulation of study uses a Wiener filter and data augmentation to identify the impact of the proposed k-means based hybrid approach for grass quality recognition.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room E Pune, India

3:00pm IST

Reducing 5G Modem Costs through Virtualization: Leveraging SDN, NFV, and Open RAN for Efficient, Cost-Effective Design
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Aryan Jain, Shrirang Joshi, Vatsal Jain, Dinesh Kumar Saini
Abstract - 5G network roll-out is expanding globally, which further shows that low-cost and good modem design remains to be absolutely integral. Scaling here is tough, not to mention the complexity and cost of production involved in traditional hardware-based 5G modems. This analysis explores how advances such as Open Radio Access Networks (Open RAN), Software-Defined Networking (SDN) and Network Function Virtualization (NFV) could reduce the hardware requirements, leading to lower costs for 5G modems. We marvel over the functionalities which we take for granted in a modem, such as digital signal processing and base-band processing, are being virtualized so that it is done on general-purpose hardware rather than on parts custom designed to do these particular tasks. Adopting cloud-native and software-based solutions for these traditional hardware-driven processes can bring huge savings without compromising on performance. In addition, we discuss Dynamic Resource and Change in Network efficiency which are improved by Modem Allocation, Edge Computing, Network Slicing — SDN NFV open day light. This collection of methods is described in a comprehensive article on the application of virtual network technologies to improve 5G modem design, reduce deployment costs, and enable more flexible, scalable, and energy-efficient 5G solutions.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room E Pune, India

3:00pm IST

Regression Techniques for Calorie Prediction: A Comparative Analysis
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Mallu Praneeth Reddy, T. A. S. Vardhan, Kura Bhargava Gupta, Nagireddy Deekshitha, Pudari Shrainya Goud, Khalvida Pamarty, Sushama Rani Dutta
Abstract - This paper aims to predict the calories burnt by a person using machine learning models built on several regression algorithms like Linear, Random Forest, XGBoost,and CatBoost based on gender, age, height, weight, duration of exercise, body temperature, and heartbeat of the person. In addition, the analysis compares the algorithms based on performance metrics like MAE (Mean Absolute Error), MSE (Mean Square Error), and R2 score and determines the most effective algorithm for calorie prediction.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room E Pune, India

3:00pm IST

Smart Contract-Based Validation of Intrusion Detection Systems in Blockchain Networks
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Shailender Vats, Prasadu Peddi, Prashant Vats
Abstract - Blockchain technology's explosive growth has created previously unheard-of potential in several industries, but it has also revealed fresh security flaws. To improve threat detection and response mechanisms, this paper provides a complete intrusion detection system (IDS) designed especially for distributed blockchain ledger security. It makes use of sophisticated smart contracts. We demonstrate the efficacy of the suggested IDS in detecting possible intrusions while preserving the integrity of the blockchain environment by validating it using simulation-based scenarios. According to the research, combining IDS with blockchain technology and smart contracts greatly improves security and is a viable way to address current cybersecurity issues.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room E Pune, India

3:00pm IST

The Influence of Adverse Weather on the Reliability and Performance of Autonomous Vehicles
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - M Nanda Kumar, Harsh Sharma, Rajan Kakkar, Tushar Naha, Atul, Rishabh Yadav, Naveen
Abstract - The demand for autonomous vehicles (AVs) has grown rapidly due to their potential to revolutionize transportation by enhancing safety, efficiency, and convenience while reducing human error, a leading cause of road accidents. AVs leverage advanced technologies like machine learning, LIDAR, GPS, cameras, RADAR, and ultrasonic sensors for precise navigation, obstacle detection, and real-time decision-making. However, their reliability and safety in di-verse environmental conditions remain a significant challenge. Extreme weather events such as heavy rain, snow, fog, ice, hail, and dust storms can impair sensor performance, reducing visibility, traction, and the ability to detect road markings, obstacles, and other vehicles. These conditions degrade the accuracy of critical systems like LIDAR, RADAR, and cameras, raising concerns about AVs’ reliability, particularly in emergencies or unpredictable scenarios. This review paper explores the effects of adverse weather on AVs’ performance, analyzing the limitations of key sensors and assessing various mitigation strategies to enhance their resilience. By identifying technological gaps and emphasizing the need for weather-resilient solutions, the paper aims to guide future research and innovation to improve AVs’ safety and reliability in challenging real-world conditions, ensuring their readiness for broader deployment.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room E Pune, India

3:00pm IST

A Comprehensive Literature Review of the function of Electronic Word of mouth in Online Social Networks
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Sneha Singh, Deepak Kaushal, Bhupinder Preet bedi, Sanjay Taneja, Pawan Kumar
Abstract - An ever-increasing number of individuals from all over the world are devoting a significant portion of their time to activities that take place in the digital realm, such as communicating with one another and looking for information. There is no denying the fact that social media platforms, which include Facebook, Twitter, sites like Instagram, and video sharing platforms like YouTube, play a vital role in the day-to-day lives of individuals, thereby altering the way in which people go about their routines. Over the past few years, electronic word-of-mouth communication, often known as eWOM, has seen a significant surge in popularity. Accordingly, the purpose of the study is to gain an understanding of the current situation regarding eWOM and social networks by means of a comprehensive review of the relevant literature. A comprehensive selection of 100 research studies was obtained from Scopus. The findings will offer a new direction to academicians in the future.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room F Pune, India

3:00pm IST

Blockchain Technology for Strengthening Content Protection in E-Voting
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Akash K, Joseph Jilvis J, Felicia Lilian J, Subhashni R
Abstract - This paper focuses on a voting system which is on a blockchain technology platform. To address issues that are known to be present in voting, it employs decentralized applications of ethereum known as dApps. Some of the contemporary matters raised include fraud as well as complexity. The proposed dApp is based on the use of smart contracts, as well as two-factor authentication through Metamask. A number of features might be noted. For example, one of the services provided by the system is event coverage such as the elections results. There is also a Voter Analysis Report Feature. This particular feature provides information on demography and the voting behaviour and it best viewed in pie chart. This dApp employs technologies including HTML, CSS, JavaScript & solidity in the process of its development. All in all, it seeks to enhance integrity, accessibility and transparency of the voting system. By doing this, it intends to increase trust and openness in elections more effectively.
Paper Presenter
avatar for Akash K

Akash K

India
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room F Pune, India

3:00pm IST

Cloud Network Security for Wireless Networks – A Review
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Eshwari Khurd, Tushar Nasery, Rupesh C. Jaiswal
Abstract - Data storage and applications have observed a large shift from being stored and used in local drives just a decade ago to being almost entirely cloud dependent today. This change in usage has brought about new challenges to be dealt with. Traditional security solutions were developed keeping in mind the use case for local storage. Techniques like cryptography have evolved to be more adaptive and secure. Yet, time after time, it has been proven that they can be broken. However, this is no longer adequate as the working and use of cloud networks is vastly different than local storage devices. Thus, new solutions need to be developed in order to secure this already established pattern of data consumption.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room F Pune, India

3:00pm IST

Color Image Data Fusion in view of Image Thresholding and Segmentation
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors: Shailesh T. Khandare, Nileshsingh V. Thakur
Abstract: Image segmentation is the key and important process in the image analysis. In general, thresholding technique is used for the grey level image segmentation and when it comes to apply for the color images, the RGB color image is separated in three grey level planes and then it is applied on these grey level planes or else the color image is directly converted to grey level image and then it is applied on this converted grey level image. This paper addresses the issue of computation time requirement to carry out these three grey level plane image segmentations through the generation of grey level image without using any inbuilt function of tool or platform. The data fusion approach is proposed which is based on the trichromatic coefficients. A single grey level image is formed from the available IR, IG, and IB grey level planes using the trichromatic coefficients. Obtained results are compared on the basis of bilevel and multi-level thresholds. Otsu bilevel threshold of obtained grey level image differs with the Otsu bilevel threshold of converted grey level image by 11 %. Obtained grey level image by proposed approach is visually near about similar to converted grey level image. Error between the thresholded images of proposed approach and converted image is less. Obtained multi-level threshold values are close with the multi-level threshold of converted image.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room F Pune, India

3:00pm IST

Cyber Security and its Vulnerabilities- A Review
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Rajni, Parminder Kaur, Harmandar Kaur
Abstract - The current world is run by technology and network connections, which are indispensable parts of day-to-day life. Corporate organizations, the military, and the government have adopted automation, and computers connected to the network are being used for the storage and sharing of vital, highly confidential, and valuable information. Hence, is essential to prevent the attackers from exploiting the vulnerabilities for illegally accessing the crucial data. With increased dependency on the internet owing to the proliferation of technologies such as cloud computing, the Internet of Things (IoT), wireless communication, and social media networks, high security is required in cyberspace. Cybersecurity provides the methods used to protect sensitive information in cyberspace. Disturbed denial of services (DDoS), phishing, man-in-the-middle (MiTM), passwords, SQL injection, Cross-site scripting, malware, and drive-by download are a few types of cyberattacks. Traditional methods such as firewalls, intrusion detection systems, antivirus software, access control lists, etc., are no longer productive in detecting new generation attacks. Therefore, there is an urgent need to design new methods to prevent these sophisticated cyberattacks. This paper explains the main reasons for cyberattacks and reviews the various types of cyberattacks, their vulnerabilities, detection and prevention techniques. To prevent current and future cyberattacks such technologies as machine learning, cloud platforms, big data, and blockchain can play an important role. The solutions provided by these technologies may assist in detecting malware, intrusion detection, spam identification, DNS attack classification, fraud detection, recognizing hidden channels, and distinguishing advanced persistent threats, enhancing the overall defense against sophisticated cyberattacks.
Paper Presenter
avatar for Rajni

Rajni

India
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room F Pune, India

3:00pm IST

Design and Implementation of Approximate Adders for Power Constraint Intelligent Edge Device
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Shubham Garg, Kanika Monga, Nitin Chaturvedi, S. Gurunarayanan
Abstract - Approximate computing has emerged as a promising paradigm for error- tolerant AI/ML applications deployed on energy-constrained edge devices. It has gained significance for edge devices due to its potential to reduce power consumption. In conventional computing systems, implementing computationally intensive machine learning algorithms results in large power consumption. Addressing this challenge, the complexity of hardware computing units can be reduced by optimizing the circuit logic while slightly trading off the computational accuracy. This technique is termed as Approximate computing where the circuit provides close-to-accurate results rather than precise results with significant reduction in power consumption. Therefore, in this work, we propose two approximate adder configurations that utilize novel logic optimization techniques to lower the power consumption and the hardware complexity of the circuit. The proposed approximate adders are designed using 55 nm technology and evaluated based on power consumption, delay, area, and power delay product (PDP). The simulation results indicate a reduction of 46.9% and 57.21% in power consumption for the approximate adder-1 & adder-2 compared to the conventional full adder. Furthermore, to validate the reliability of the proposed design, we also evaluated and calculated the accuracy metrics in terms of mean error distance (MED) of 0.25, which reflects the error tolerance of the proposed design.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room F Pune, India

3:00pm IST

Enhanced Myocardial Infarction Prediction Using Stacking Ensemble Approach
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Parambrata Sanyal, Mukund Kuthe, Sudhanshu Maurya, Sushmit Partakke, Firdous Sadaf M. Ismail, Rachit Garg
Abstract - The most important public health challenge of myocardial infarction is caused by the obstruction by cholesterol and plaque accumulation in arteries, resulting in morbidity and mortality across the globe, especially in low and middle economies that lack health services, preventive measures, and early detection facilities. This study seeks to support the development of effective strategies by proposing a stacking ensemble model for timely forecasting and treatment of this disease in a serious way to improve healthcare significantly around the globe. The proposed methodology has been implemented on a retrospective dataset acquired from IEEE Dataport. The methodology involves normalization and standardization of the dataset, ensuring uniformity so that the machine learning classifiers work well. Our research compares several widely used machine learning classifiers, including Support Vector Machines (SVM), Gradient Boosting (GB), and Naive Bayes (NB), whose hyperparameter tuning has been done by grid search CV (GCV). The proposed stacking ensemble model stacks Light Gradient Boost and Cat Boost algorithms after being hyper-tuned by the Particle Swarm Optimization technique to enhance the overall predictive capacity. The results demonstrate that the proposed stacking ensemble model surpasses the individual classifiers in metrics, including the F1 score, recall, accuracy, and precision that are considered in this paper. Future directions of the research would be to work on expanded datasets and, most importantly, increase population diversity, add clinical parameters, and instead utilize more sophisticated machine learning techniques.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room F Pune, India

3:00pm IST

Optimizing Quantum Computer Simulator Performance: A GPU-Accelerated Approach
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Mirza Zuber Baig, Vivek Nainwal, Anoop kumar, Bharat Kumar
Abstract - Quantum computer simulators play a crucial role in understanding and analyzing the behavior of quantum systems. However, simulating large-scale quantum systems over classical machines can be computationally expensive and time consuming, limiting the practicality of many quantum algorithms. In this research paper, we explore the methodology employed for accelerating indigenous density-matrix based quantum computer simulator by using state of art libraries for GPUs (Graphics Processing Units) effectively increasing the number of Qubits it can simulate. The paper discusses the methods and techniques employed to identify computationally intensive and time-consuming functions within the simulator. By analyzing the profile results, we identified specific functions that required significant computational resources. To accelerate these functions, we utilized GPU acceleration techniques, leveraging parallel processing power. Our study demonstrates a significant improvement in simulation speed, achieving a significant speedup, showcasing the effectiveness of GPU acceleration in quantum computer simulations.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room F Pune, India

3:00pm IST

Stereoscopic Scalable Quantum Convolutional Neural Networks with Banyan Tree Growth Optimization for Predicting IoT Security Attacks by Mirai Malware
Friday January 31, 2025 3:00pm - 5:00pm IST
Authors - Ravi Kumar Suggala, Khushi Kumari, Mathi Gayathri, Koppisetti Deepika Naga Sree, Nekkalapudi Gayathri, Suma Kadali
Abstract - Malware file production grows rather actively, which is explained by the development of digital structures. The proliferation of cyber trends poses severe security challenges due to the increasing complexity of attacks. These files could be difficult to detect when they share characteristics with normal files or if they are altered. Internet of Things (IoT) networks put a probability of vulnerability akin to Mirai malware to cyberattacks. There is a need to develop complex procedures for top security since it is important for such networks. This paper presents a new framework of preprocessing techniques, feature selection, and classification for predicting Mirai malware IoT security attacks. The preprocessing part uses the Global-Local Depth Normalization (GLDN) of features for dissolving noise and for better normalization of feature depths to enhance the learning factor. Practical feature selection is performed by using a combination of Gooseneck Barnacle Optimization (GBO) and Human Memory Optimization (HMO). This hybrid makes an intelligent dimensionality reduction decision determined by choosing appropriate features from among the set by the right balance between exploration and exploitation using biologically inspired optimization algorithms. For classification, there is proposed a Stereoscopic Scalable Quantum Convolutional Neural Network (sQCNN) that applies quantum computation principles to enhance computational scalability at the quantum level. The Banyan Tree Growth Optimization (BTGO) algorithm can optimize the classifier with high accuracy and attack detection immunity. The concept of Banyan tree growth in a hierarchical structure is similar to the classifier structure. Experiments conducted on the N-BaIoT dataset successfully prove the idea behind the proposed approach. The results propose that the new methods ensure better results over the traditional methods concerning the achieved accuracy of 99.67% and precision of 99.61%, while also incorporating reduced computational over- head. This new framework is a major step forward in defending IoT networks against current emerging threats, stressing the collaboration of preprocessing, feature selection, and quantum learning.
Paper Presenter
Friday January 31, 2025 3:00pm - 5:00pm IST
Virtual Room F Pune, India

4:45pm IST

Session Chair Remarks
Friday January 31, 2025 4:45pm - 4:50pm IST
Invited Guest/Session Chair
avatar for Dr. Ruchi Sharma

Dr. Ruchi Sharma

Professor, Artificial Intelligence & Data Science, Jaipur Engineering College and Research Centre, Jaipur, India.
Friday January 31, 2025 4:45pm - 4:50pm IST
Virtual Room A Pune, India

4:45pm IST

Session Chair Remarks
Friday January 31, 2025 4:45pm - 4:50pm IST
Invited Guest/Session Chair
avatar for Dr. Nidhi Tiwari

Dr. Nidhi Tiwari

Associate Professor, R&D Head, SAGE University, Indore, India
Friday January 31, 2025 4:45pm - 4:50pm IST
Virtual Room B Pune, India

4:45pm IST

Session Chair Remarks
Friday January 31, 2025 4:45pm - 4:50pm IST
Invited Guest/Session Chair
avatar for Dr. Archana Chaudhari

Dr. Archana Chaudhari

Assistant Professor, Vishwakarma Institute of Technology, Pune, India
Friday January 31, 2025 4:45pm - 4:50pm IST
Virtual Room C Pune, India

4:45pm IST

Session Chair Remarks
Friday January 31, 2025 4:45pm - 4:50pm IST
Invited Guest/Session Chair
avatar for Dr Ashish Patel

Dr Ashish Patel

Associate Professor, Parul Institute of Pharmacy, Parul University, Vadodara, India
Friday January 31, 2025 4:45pm - 4:50pm IST
Virtual Room D Pune, India

4:45pm IST

Session Chair Remarks
Friday January 31, 2025 4:45pm - 4:50pm IST
Invited Guest/Session Chair
avatar for Dr. Archana S. Banait

Dr. Archana S. Banait

Assistant Professor, MET's Institute of Engineering Department of Computer Engineering, Nashik, India
Friday January 31, 2025 4:45pm - 4:50pm IST
Virtual Room E Pune, India

4:45pm IST

Session Chair Remarks
Friday January 31, 2025 4:45pm - 4:50pm IST
Invited Guest/Session Chair
avatar for Dr. Satish S. Banait

Dr. Satish S. Banait

Assistant Professor, K.K. Wagh Institute of Engineering Education and Research, Nashik, India
Friday January 31, 2025 4:45pm - 4:50pm IST
Virtual Room F Pune, India

4:50pm IST

Closing Remarks
Friday January 31, 2025 4:50pm - 5:00pm IST
Moderator
Friday January 31, 2025 4:50pm - 5:00pm IST
Virtual Room A Pune, India

4:50pm IST

Closing Remarks
Friday January 31, 2025 4:50pm - 5:00pm IST
Moderator
Friday January 31, 2025 4:50pm - 5:00pm IST
Virtual Room B Pune, India

4:50pm IST

Closing Remarks
Friday January 31, 2025 4:50pm - 5:00pm IST
Moderator
Friday January 31, 2025 4:50pm - 5:00pm IST
Virtual Room C Pune, India

4:50pm IST

Closing Remarks
Friday January 31, 2025 4:50pm - 5:00pm IST
Moderator
Friday January 31, 2025 4:50pm - 5:00pm IST
Virtual Room D Pune, India

4:50pm IST

Closing Remarks
Friday January 31, 2025 4:50pm - 5:00pm IST
Moderator
Friday January 31, 2025 4:50pm - 5:00pm IST
Virtual Room E Pune, India

4:50pm IST

Closing Remarks
Friday January 31, 2025 4:50pm - 5:00pm IST
Moderator
Friday January 31, 2025 4:50pm - 5:00pm IST
Virtual Room F Pune, India
 

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