Authors - Manasi Golesar, Priti Jagtap, Kamlesh Khatod, Kshitij Malode, Vaishali Pawar Abstract - This research presents a novel approach to bot detection in web applications using behavioral biometrics and machine learning. Our system leverages a Flask based web application with a registration form as a testbed to distinguish between human and automated users. The implementation collects multidimensional behavioral data including mouse movements, typing patterns, form fill speed, and browser fingerprinting to build a comprehensive user profile.Two machine learning models, Random Forest and XGBoost, are dynamically compared for performance, with the superior model being automatically selected for deployment. The system incorporates a honeypot field as a simple yet effective first pass filter and implements progressive model learning through a database backed training pipeline that continually improves detection accuracy.Key innovations include the real time behavioral analysis during form completion, automated weekly model retraining, and an administrative interface that allows for manual labeling of edge cases to enhance the training dataset. Our approach achieves high detection accuracy while maintaining a low false positive rate, effectively balancing security with user experience.This research demonstrates that integrating behavioral biometrics with adaptive machine learning provides a robust defense against increasingly sophisticated bot attacks without requiring traditional CAPTCHA challenges that often degrade user experience.
Authors - Devarsh Damodaran, Krishna Bharathi V, Dhanya M Abstract - This study explores user sentiments towards AI-powered fitness applications by analyzing user reviews from platforms like Google Play Store. With the increasing adoption of digital health solutions, understanding user satisfaction, trust, and key concerns is crucial. Using Natural Language Processing (NLP) techniques, sentiment analysis was conducted to classify user feedback into positive and negative sentiments. Machine learning algorithms like Logistic Regression and Support Vector Machine (SVM) were utilized for classification. Findings are prominent drivers of satisfaction, where usability, effectiveness, and personalization are essential drivers, while cost, technology glitches, and unrealized expectations drive dissatisfaction. These findings give interesting insights for fitness-tech business companies and app developers to drive engagement and better experience for their users.
Authors - Silpa Raj R, Durgalashmi C V Abstract - The widespread adoption of social media enables female entrepreneurs to leverage innovative tools and strategies, fostering the development of sustainable business practices. The present study analyses how the female entrepreneurs in Kerala utilize social media (SM) in promoting sustainable innovations in their business activities. Research investigates how social media affects sustainable business practices among women entrepreneurs in Kerala, with the focus of four key variables: idea generation, customer connectivity, collaboration, and sustainable outcomes. This study aims to fills the gap by exploring how women use social media for entrepreneurial practices and adoption of sustainable outcomes. This study used a structured questionnaire to collects data from women entrepreneurs in Kerala. The variables including frequency of idea generation through social media, customer or stakeholders’ collaborations and the adoption of sustainable practices influenced by digital platforms are observed. To ensure the participation of entrepreneurs actively using social media for innovation, purposive sampling techniques were employed. The hypotheses were tested using the statistical tools like chi-square, correlation and regression analysis, providing empirical evidence on impact of SM usage among Kerala’s women entrepreneurs. The study emphasizes the significance of social networking platforms in encouraging innovative methods that contribute to sustainability via three major variables. The findings suggest practical implications for policyholders, entrepreneurs and researchers. The research adds existing corpuses of research on digital entrepreneurship and sustainability focusing on how social media improve sustainable practices.
Authors - Vineet Wagh, Srushti Chopade, Sneha Patil, Vighnesh Padwal, Sarika Kuhikar Abstract - In Institutions and schools, attendance management is a crucial task for faculty to monitor class strength. Traditional methods such as manual entry, biometrics, and RFID-based systems are commonly used, but they are time-consuming and, in the case of biometrics, potentially unhygienic. This paper presents an automated face recognition-based attendance system that utilizes preinstalled CCTV cameras to monitor student presence in real-time. The system employs RetinaFace for face detection and the face_recognition library for face encoding and matching. Known face images are preprocessed to generate face encodings, which are then compared with detected faces in each frame to determine attendance. The proposed system offers accuracy, efficiency, automation, and contactless operation while seamlessly integrating with existing infrastructure. A web interface allows users to start and stop attendance tracking, remove duplicate records, and download attendance logs in CSV format. The system demonstrates its applicability in educational environments by providing a scalable, non-intrusive, and secure solution for automated attendance management.
Authors - Madhumati Shinde, Premanand Ghadekar Abstract - Cloud computing is a dynamic part of today's high-tech framework, given that frequent welfares such as cost-effectiveness, scalability, convenience, novelty, and safety. Its impact is multifaceted, transforming competition and corporate operations in the digital age. To improve speed, optimize resource usage, and support sophisticated applications, cloud computing makes use of a variety of learning strategies. A learning technique's effectiveness in the field of cloud security depends on its ability to recognize, stop, and handle security threats. In order to identify and reduce security threats, machine learning particularly anomaly detection using supervised and unsupervised learning is crucial with advancement of federated learning. Deep learning models like RNNs and CNNs process extensive datasets to uncover intricate attack patterns, while federated learning improves privacy by training models on decentralized data sources. Reinforcement learning facilitates adaptive security strategies, continually enhancing threat responses. Security is paramount in cloud computing as it safeguards sensitive data, applications, and services hosted on cloud platforms from unauthorized access, breaches, and cyber threats.This paper highlights the security concerns in cloud environment with framework to improve the performance matrix to recognize federated cloud trust.
Authors - Ajuram. P, E. Grace Mary Kanaga Abstract - Large Language Models have demonstrated great effectiveness in generating text and images. However they can become even more efficient by perfecting the prompt given to them. This paper proposes a multi LLM framework that dynamically orchestrizes several specialized LLM models in accordance with complex user prompts. First, a primary LLM analyzes the user prompt and breaks it down into multiple sub tasks. Then, for each identified sub task with respect to its type (text to text, text to image, or image to text), a suitable LLM is assigned. The context, instructions, and the output format is also provided by the primary LLM for each chosen model. The sub tasks are executed either in parallel or in sequential order. This approach automates the workflow, optimizes model utilization, and improves response relevance, making it suitable for applications requiring multi modal collaboration and processing.
Wednesday August 26, 2026 12:30pm - 2:30pm IST Virtual Room EGOA, India
Authors - Devika Vijapur, Nidhi Desai, Aishwarya Naik, Smita Ganur, Supriya Katwe Abstract - Road accidents are one of the global safety concerns leading to loss of millions of lives every year. One of the factor leading to this is over-steering. Oversteering is phenomenon that occurs when the rear wheels of the vehicle lose grip which causes the vehicle to turn more than expected. The detection of oversteering in real-time is crucial for the improvement of vehicle safety to prevent accidents as well as for advanced driving assistance systems(ADAS). This paper presents a holistic approach to over-steering detection using a decision tree algorithm. The proposed system analyzes various vehicle dynamics parameters such as lateral acceleration, yaw rate and steering angle to identify the patterns that cause over-steering. The system incorporates collection of real-time data from Inertial Measurement Unit (IMU) sensors that enhances reliability of oversteering detection under various conditions. The model is trained from the data obtained, using decision tree algorithm and obtained accuracy of 96.08%. The hardware implementation is done by placing ESP-32 integrated with MPU 6050 and Arduino Nano 33 BLE sense accordingly in the vehicle. Based on thresholds of the parameters mentioned in the paper, oversteering is detected.
Wednesday August 26, 2026 12:30pm - 2:30pm IST Virtual Room EGOA, India
Authors - Sneha S. Temgire, Y.S. Angal, Ashwini V. Waghmare, Chetana Sharma, Ashwini Gajre Abstract - Agriculture is essential for food security and economic growth, but traditional farming faces challenges such as plant diseases, inefficient irrigation, and labour-intensive monitoring. This project focuses on automated and manual irrigation in addition with plant disease detection and growth monitoring using image processing on a Raspberry Pi 3B+. By leveraging TensorFlow Lite and OpenCV, the system can analyze plant health and trigger appropriate irrigation actions. The aim is to design accurate agriculture system by reducing water wastage and improving crop monitoring. A key feature of this system is web-based monitoring, where the Raspberry Pi transmits real-time plant health data and sensor readings to an HTML-based webpage. Users can remotely access this data via a web interface, enabling continuous monitoring of plant conditions, disease status, and irrigation control from any location. By combining machine learning, image processing, IoT automation, and real-time web-based monitoring, this system reduces manual labour, optimizes water usage, and ensures early disease detection. The web interface enhances accessibility, allowing farmers and researchers to track plant health remotely and make informed decisions.
Authors - Sauvik Bose, Rina Bhattacharya, Rajeshwari Roy Abstract - Avian monitoring is a crucial component of biodiversity conservation, providing insights into population trends, habitat changes, and environmental stressors. The fast growth of mobile telephony has raised issues regarding its potential upon the avian population, their behaviors and breeding, predominantly due to electromagnetic radiation exposure. This study investigates the feasibility of using drones for avian monitoring near mobile towers in Arambagh Municipality (22.8838° N, 87.7819° E), Hooghly, West Bengal, India, which is a semi-urban landscape with rich avian diversity and has undergone a significant growth in mobile tower installation over the last few decades. Drones offer a non-invasive, scalable, and high-resolution method for ecological monitoring, surpassing traditional survey techniques in terms of not only efficiency and data accuracy but also consuming less time and effort. A drone (model: DJI MAVIC MINI) equipped with a high-resolution camera is deployed at selected base station sites within the study area. The study pattern included regulated flight patterns, periodic monitoring. Findings disclosed noticeable behavioral variations in birds near mobile base stations. The repulsion of smaller birds to the high EMR zone has been distinctly observed along with anomalies in roosting and breeding habits. A correlation was observed between radiation levels and avian health oddities, underscoring the need for further research. In the future, research ought to be performed on in-depth monitoring efforts in urban and semi-urban areas along the different geographical landscapes. Improving drone technology for ecological studies and exploring alternative communication infrastructures with reduced environmental impact is much needed.
Authors - Dhanyashree S, Keshav S, Deepak Gupta, Shobhana Palat Madhavan Abstract - This study investigates a chain reaction triggered by cliffhangers in media consumption, focusing on their role in driving binge-watching, self-regulatory depletion, binge-eating, and reduced mental well-being. Grounded in Self-Regulatory Depletion Theory, a sequential mediation model is proposed and analyzed through serial mediation regression. Data from 170 Indian respondents revealed that cliffhangers significantly predicted binge-watching, which in turn increased self-regulatory depletion. Depletion heightened binge-eating tendencies, and binge-eating negatively impacted mental well-being. Bootstrapped mediation confirmed an indirect pathway from cliffhangers to reduced mental well-being via binge-watching and self-regulatory depletion. These findings underscore the ethical responsibility of streaming platforms to mitigate compulsive viewing and highlight interventions for mindful consumption.
