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..
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.
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.”
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 - 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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. .
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.
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.
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.
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).
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.
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.
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.
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.
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 - 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.
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.
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.
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.
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.
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.
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
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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..
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.
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.
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.
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.
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.
