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.
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.
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).
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.
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.
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.
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
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
