Associate Professor and Head, Department of Computer Science & Engineering (Artificial Intelligence), Vishwakarma Institute of Information Technology, Pune, India
Tuesday August 25, 2026 12:28pm - 12:30pm IST Virtual Room BGOA, India
Authors - V. V. mandhare, Poonam Bhokare, P.S. Vikhe, Chandrakant Kadu Abstract - These days, billions of people use the internet worldwide. Technology for intrusion detection may be novel. a security technology generation that keeps an eye on the system to stave against malicious activity. The IDPS tracks malicious user behavior over time using a neighborhood procedure grid. Because of the rhetorical alternatives, the system suggests a security system during this project called the Intrusion Detection and Protection System at call level, which builds user pro- files to monitor usage activities. The proposed work is evaluated using intrusion detection systems and forensic techniques. The bottom paper includes a review of the literature on the Intrusion Detection System (IDS) and Internal Intrusion Detection System (IIDS). Internal Intrusion Detection System (IIDS), which employs predetermined algorithms or approaches to distinguish unauthorized user activity or attacks over a network, was designed during this research.
Authors - Dev Gandhi, Adarsh Srivastava, Rachit Soni, Daksh Parekh, Lokesh Heda Abstract - Recognizing people in images and videos is the main objective of computer vision-based person identification. The past ten years have seen a great deal of research in human detection. As single-stage algorithms, YOLO is a desirable choice for object detection because it offers faster results than two-stage algorithms. The advantage of this approach is that it provides both a manual for choosing the most effective human detection methods for real-world applications and a thorough analysis of current methods. This research paper's objective is specifically to evaluate and compare the performance of YOLOv3, YOLOv4, and YOLOv5 models on various images to detect human in visual scenes. Additionally, this paper discusses various parameters according to which the model’s efficiency is determined. Our experimental results demonstrate that YOLOv5 achieves higher accuracy, precision, and recall as compared to other YOLO models; the highest accuracy attained by YOLOv5 is 0.94 with F1 score of 0.96.
Authors - Shubhamm Kumaar, Akshat Sharma, Sukrati Chaturvedi Abstract - By connecting Agentic AI with Model Context Protocol Servers (MCPS) it is made possible to work towards autonomy of decision making. work-flow automation. Agent AI which is powered by large language models can provide a live context of information using MCPS. This work mostly focuses on the making of smarter workflows which are regular and pleasing. traditional automation. This integration improves using a decentralized multi agent framework. Makes decisions better and works smoother. Healthcare, manufacturing, smart, and other fields. cities. Tesla's efficient production and Singapore's Smart city are real-life examples. Nonetheless, various obstacles regarding scalability, ethics, computation, and more can hinder its application. However, challenges like scalability, ethics, and computational demands remain. This review synthesizes current research, applications, and future directions, underscoring the promise of this technology for innovative automation solutions.
Authors - V. V. mandhare, Hemlata Mali, P.S. Vikhe, M. R. Bendre, M. R. Parkhe Abstract - This paper introduces a smart and efficient Hall Ticket Generation System that integrates QR code technology to improve the process of generating and managing hall tickets for academic examinations and events. Built using Java and MySQL, the system is designed to simplify administrative work, enhance security, and promote eco-friendly practices by eliminating the need for paper-based hall tickets. The core functionality of the system is its ability to generate dynamic QR codes for each hall ticket, which can be quickly scanned using an Android- based scanner application. This allows invigilators to instantly verify the identity and credentials of candidates, reducing the chances of fraudulent entries or unauthorized access to the examination hall. By transitioning tothisdigitalmodel,institutionsbenefitfromreducedpaperusage,lower administrative costs, and improved operational efficiency. The system is not only secure but also user-friendly, making it easier for both staff and students to manage examination logistics. In Automatic Question Paper Generator Module, which addresses the common challenges associated with manually preparing exam questions. To overcome these limitations, the system uses a key word-based randomization algorithm to create question papers swiftly and securely. This method ensures that each set of questions is unique, well-distributed across topics, and free from duplication. The system is capable of storing and managing multiple question paper sets, making it easier to conduct exams across different classes while ensuring comprehensive curriculum coverage. Overall, this project offers a complete solution that not only simplifies exam management but also enhances the quality and integrity of academic assessments through automation and intelligent design.
Authors - Madhu Shukla, Vipul Ladva, Simrin Fathima Syed, Neel H. Dholakia Abstract - Cardiovascular disease continues to be one of the leading causes of death globally, demonstrating the critical role of efficient and reliable prediction models. Here in this study a dataset that is integrated from five heart disease datasets originated from publicly available sources such as UCI for Heart Attack risk prediction and analysis with 1,888 instances are used. Fourteen primary factors that include age,abnormality of cholesterol level, type of chest pain, as well as exercise related parameters from demographic and clinical dimensions were investigated in order to find the association with heart disease. Significant trends were found using data visualization to show high heart risks with certain chest pain types and high maximum heart rate. A correlation matrix illustrates important inter-feature relationships and sheds new light on the predictabilitheckability of the features. This study highlights the possibility to exploit demographic and clinical information for early detection of high risk individuals and in the future, to allow medical interventions and improve health state.
Authors - Bhukya Rakesh, Dasari Yaswanth, Kumari Nidhi Lal Abstract - Mobile Ad Hoc Networks (MANETs) are dynamic, decentralized networks that require efficient routing mechanisms to ensure reliable communication. Traditional routing protocols struggle with issues such as high node mobility, energy constraints, and unpredictable topology changes. This research explores the integration of artificial intelligence, specifically neural networks, to enhance routing efficiency in MANETs. Our approach leverages deep learning models to predict optimal routes by analyzing network parameters such as node density, mobility patterns, and link stability. The proposed AI-driven routing mechanism dynamically adapts to network variations, improving packet delivery ratio, reducing latency, and optimizing energy consumption. Comparative evaluations against conventional routing protocols, such as AODV and DSR, demonstrate significant improvements in network performance. The results highlight the potential of neural networks in revolutionizing adaptive routing for MANETs, paving the way for more intelligent and resilient communication systems.
Authors - Ayush Tiwari, Ayushi Tomar, Prabhjot Kaur Abstract - Online banking fraud constitutes illegal access to accounts for the payment transfer purposes. There are several reasons for difficulty in the detection of such crimes-from the imbalance of data to the fraudsters' techniques that are ever-changing. The set of tools used for this purpose are machine learning, economic optimization, and risk assessment. By combining these techniques, a maximum reduction in the losses due to fraud and false positives will be achieved. The machine learning models, when validated against real datasets, were able to reduce losses by 52%, allowing for just 0.4% false positives. The improvement in behavior analysis for fraud detection is conducted through transaction clustering, sliding window aggregation, and adaptive classifiers. The algorithms such as KNN, SVM, Logistic Regression, Local Outlier Factor, and Isolation Forest are used to predict fraud in credit card transactions so that it is accurately detected, false alarms being at a minimum, and customers are availed against unauthorized charging.
Tuesday August 25, 2026 12:30pm - 2:30pm IST Virtual Room BGOA, India
Authors - Nisarg Chaudhari, Urva Dave, Zeel Patel, Manasvi Vachhani, Dweepna Garg, Bhavika Patel, Kashyap Patel, Parth Goel Abstract - Fertilizer recommendation plays a crucial role in optimizing crop yield while minimizing resource wastage. The integration of machine learning techniques enables precise fertilizer prediction based on soil and environmental conditions, leading to improved agricultural productivity. However, traditional methods often result in overuse or underuse of fertilizers, negatively impacting soil health and crop growth.This study employs various machine learning algorithms, including RandomForest, XGBoost, LightGBM, HistGradientBoosting, CatBoost, and Neural Networks, to classify and recommend fertilizers based on soil parameters. The models were trained on a synthetic fertilizer dataset containing diverse soil compositions and fertilizer requirements.Experimental results indicate that Neural Networks outperform tree-based models, achieving the highest testing accuracy of 84.10%, demonstrating strong generalization capabilities. Accuracy and loss trends over epochs confirm stable learning, while a confusion matrix reveals minimal misclassifications. This study highlights the effectiveness of deep learning in optimizing fertilizer recommendations, contributing to more sustainable and efficient agricultural practices.
Authors - Nirav Narayan, Martin Parmar, Parth Shah, Mrugendra Rahevar Abstract - Water quality is a critical concern for human health, ecosystems, and sustainable resource management. Traditional water quality monitoring methods are expensive, time-consuming, and often lack real-time data availability. This research proposes a Secure Water Quality Monitoring framework integrating IoT, LoRa WAN, and secure data transmission to enable real-time, cost-effective monitoring in remote and urban areas. The system employs low-power LoRa technology for long-range communication, ensuring reliable data transmission even in connectivity-challenged regions. ESP32 microcontrollers process sensor data, measuring key parameters such as pH, turbidity, TDS, EC and DO. Security is ensured using AES-128-bit encryption and SHA-256 hashing, safeguarding environmental data against tampering. The proposed framework addresses challenges in conventional monitoring, such as high costs and limited scalability, by offering a low-cost, energy-efficient, and scalable approach. This study demonstrates the potential of IoT-driven smart monitoring framework to enhance water resource management and environmental sustainability, paving the way for future advancements in sensor technology, energy efficiency, and data security.
Authors - Amrithesh TV, Sajin John Shaji, Sangeeth Gopinath Abstract - Family businesses play significantly in the world economy; yet, the majority can somehow manage to survive through leadership change from impacts of family relationships. Unlike some business corporations with formalized transitions, family companies dominantly depend on family relationships and informal decision-making, hence bringing about business instability as well as conflicts. This research examines just how internal family systems indeed influence successor choice, and shape leadership transition on business resilience. Through in-depth qualitative research with strict thematic analysis of family firm owner interviews, the research discovers a number of important succession determinants, such as traditional heirarchy, mentering, and resistance to modernization of the modern kind. The research does uncover that in fact successor selection is usually highly influenced by family influence, in contrast to planned planning, and therefore jeopardizing some degree of business stability. This paper suggests the adoption of comprehensive succession planning and participative decision-making. Systematic leadership development programs, coupled with open communication, will ensure successful leadership succession and long-term business success.
Associate Professor and Head, Department of Computer Science & Engineering (Artificial Intelligence), Vishwakarma Institute of Information Technology, Pune, India
Tuesday August 25, 2026 2:30pm - 2:32pm IST Virtual Room BGOA, India
