Authors - Manasa S, Rupam Bhaduri, Pramod Kumar Naik, Gangadhar T G, Bharath Kumar S Abstract - This paper introduces an adaptive control strategy for a three-phase Dual Active Bridge (DAB) converter, designed to facilitate efficient bidirectional power flow in electric vehicle (EV) fast-charging stations. The proposed control method effectively manages real-time fluctuations in grid conditions and the state-of-charge (SOC) of batteries, ensuring stable operation in both Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) modes. Utilizing a dq-reference frame-based decoupled controller with SOC feedback, the solution is rigorously validated through MATLAB/Simulink simulations. The design encompasses LCL filter modeling, DAB phase shift modulation, and battery interfacing under diverse loading scenarios. Simulation results reveal significant improvements in performance, highlighting the system's ability to maintain high efficiency during both charging and discharging phases. By enhancing the responsive-ness and stability of power exchange between EVs and the grid, this research aims to contribute to the development of advanced fast-charging infrastructure capable of supporting increasing EV adoption while optimizing overall electric grid performance. The findings underscore the potential of adaptive control strategies in ensuring reliable and efficient energy management within smart grid environments.
Authors - Saraswati Patil, Kalyani Rathod, Adarsh Jayfale, Wasim Pathan, Adesh Bhore Abstract - This paper looks at how object detection technology can help blind and visually impaired people. Visually challenged individuals struggle to comprehend their surroundings, especially in outdoor settings where objects constantly shift and move. Object detection solutions can help visually impaired individuals overcome difficulties in daily life. The object detecting system aims to provide a simple, user-friendly, convenient, and cost-effective solution for visually impaired individuals. This yolov11 model has the frame process rate 45 FPS on CPU and 100 – 150 FPS on GPU .The system was tested with different objects and in various environments to see how well it works. Key factors like how accurate it was, how quickly it responded, and how satisfied users were measured. The results showed that the system was good at detecting objects and giving clear instructions to the user in real time.
Authors - Aranya G, Durgalashmi C V, Nidheesh Melethadathil Abstract - This study examines the confidence levels of healthcare and IT professionals regarding the execution of artificial intelligence (AI) in the healthcare sector. Focus on understanding the perceived impacts of AI on patient safety, quality of care, and the ethical and legal implications involved, the research employed an analysis through a detailed questionnaire, gathering responses from 50 healthcare professionals and 50 IT professionals in Kerala using judgmental sampling. Survey model and percentage analysis were used in this study. The findings indicate a mixed sentiment: a substantial proportion of respondents acknowledge the potential of AI to enhance healthcare delivery and patient outcomes, yet there remains significant apprehension concerning data privacy, potential biases, and the need for human oversight. While IT professionals generally display greater confidence and familiarity in AI technologies, healthcare professionals are more cautious, emphasizing the importance of ethical considerations and human involvement in clinical decision-making. The study suggests that bridging the gap between these professional groups through targeted education, hands-on experience, and robust governance frameworks can enhance confidence and facilitate the effective integration of AI in healthcare. Recommendations include ongoing training and clear communication about AI's capabilities and limitations to ensure both ethical application and improved healthcare outcomes.
Wednesday August 26, 2026 3:30pm - 5:30pm IST Virtual Room EGOA, India
Authors - Ritu Ramesh Vernekar, Vijeta D Chitragar, Laxmi Koutanali, Prajwal Sangalad, Hemantaraj M Kelagadi, Suhas B Shirol Abstract - The ESP32 microcontroller and the Blynk IoT application are integrated in a novel system for automatic irrigation and tank water level management. Sensors for water levels, rainfall, temperature, and soil moisture track real-time environmental parameters. Temperature readings ranged from 25°C to 31°C over the 8-day research, but soil moisture was continuously kept within ideal ranges. Water waste was reduced and timely refills were ensured by the water tank level sensor mechanism, which successfully maintained a threshold of 15 cm. Based on sensor data, intelligent algorithms control irrigation, minimize waterlogging, and maximize water usage. Convenience and operational efficiency are increased via remote management via the Blynk app. This intelligent irrigation system provides a sustainable and effective answer to contemporary agriculture by preserving water, improving crop health, and facilitating data- driven farming methods.
Authors - Yash Prajapati, Ketul Patel, Nidhi Acharya, Nidhi Dubey, Nisarg Patel Abstract - The Internet of Things (IoT) is progressively changing and offers IoT ecosystems integrated network security challenges that require sophisticated security solutions. In this paper, we discuss the hybrid model that combines Federated Learning (FL) with Random Forest (RF) algorithms along with the validation of Blockchain to provide adaptive network security within IoT frameworks. The proposed architecture merges Blockchain’s protection against unauthorized access with the automatic updates and data processing of FL, decentralizing the security measures within the IoT ecosystems while increasing detection accuracy and safeguarding sensitive infor-mation. This framework overcomes the constraints imposed by centralized machine learning intrusion detection techniques, providing solutions to real world IoT security issues.
Authors - Harsh Raj, Kanishk Tewatia, Sumeet Gupta Abstract - Channel encoding plays a vital role in modern communication systems by maintaining data integrity and reducing the impact of noise. In this paper, we propose a hybrid model that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to classify various channel encoders. This approach aims to improve feature extraction and classification performance compared to traditional CNN architectures. In typical scenarios, receivers are aware of the encoder’s type and configuration. However, in non-cooperative environments such as military communications, surveillance, and cognitive radio systems, this information is often limited or unavailable. To address this, we explore a deep learning-based method to identify four types of encoders: block, convolutional, Bose–Chaudhuri–Hocquenghem (BCH), and polar encoders. By integrating CNN and LSTM layers, our proposed model achieves up to 98% classification accuracy and demonstrates strong generalization. Comparative analysis reveals that the hybrid model outperforms conventional CNN-based methods in terms of accuracy and robustness.
Authors - Arunkumar V N, Agna.S. Nath, Aswathi.K. B Abstract - This study examines the disconnect between India’s cybersecurity policies and their real-world implementation, revealing systemic barriers to digital empowerment. Through qualitative analysis, the research identifies four critical challenges: inadequate awareness programs, urban-rural security divides, gender-based vulnerabilities, and educational gaps in cyber-literacy. Findings show urban users exhibit risky digital behaviours despite high connectivity, while rural populations avoid online services due to security fears. Women face compounded risks, with many dependent on male relatives for digital access. The education system largely fails to equip students with basic cybersecurity knowledge. However, community-led initiatives demonstrate promising alternatives. Localized, vernacular training programs have successfully enhanced digital safety awareness and reduced fraud incidents. These models highlight the importance of contextual, participatory approaches to cybersecurity education. The study argues for rethinking cybersecurity as an essential dimension of human development rather than just technical infrastructure. It proposes shifting from compliance-focused governance to capability-building frameworks that prioritize protective freedoms for all citizens. Key recommendations include integrating cybersecurity into school curricula, developing gender-responsive digital safety programs, and creating community-based "digital mitra" networks. By bridging policy intentions with ground realities, this research offers pathways to make India's digital growth truly inclusive and secure.
Wednesday August 26, 2026 3:30pm - 5:30pm IST Virtual Room EGOA, India
Authors - Pratyush Jaishankar, Ayman Aftab, Divyanshu Vyas, Dhanashree G Bhate Abstract - The research proposes a distinctive method to identify unauthorized people who enter restricted areas through a combination of KLD7 millimeter wave radar systems and deep learning algorithms. Gait patterns obtained from Doppler and micro-Doppler signals are analyzed by the system which offers both privacy preservation and non intrusiveness as opposed to conventional methods like CCTV surveillance. The Random Forest Classifier shows excellence by accurately identifying authorized or unauthorized individuals at a rate of 82% while maintaining its capabilities during various challenging environmental situations. The solution provides high practicality when used for real-time monitoring deployments. Future development efforts will direct their attention to growing the dataset while making the solution work efficiently on edge computing devices.
Wednesday August 26, 2026 3:30pm - 5:30pm IST Virtual Room EGOA, India
Authors - Shreya Kapadia, Payal D Joshi Abstract - In the era of IR, event detection has moved beyond simple keyword searches to utilize advanced techniques to extract relevant events from massive news article datasets. The rapid growth of news highlights the need for efficient information retrieval techniques to capture the most relevant events. Traditional lexical-based retrieval methods, such as Whoosh and BM25, are effective in keyword matching; however, they have some limitations in understanding the semantic events from the indexed text. To enhance this limitation, this study introduces a Transformer-based deep learning model for Natural Language Processing (NLP), such as BERT, capable of capturing contextual relationships and improving the relevance of data. This research also explores an optimized approach that seamlessly integrates Whoosh for efficient indexing, BM25 for probabilistic ranking, and BERT for neural re-ranking, designed to improve event detection performance. Additionally, Named Entity Recognition (NER) significantly enhances event extraction by accurately identifying real-world entities like individuals, locations, organizations.The results of this research indicate that the integration of lexical models(Whoosh and BM25) with neural ranking models(BERT) significantly enhances precision, recall, and relevance, thereby exceeding the performance of traditional retrieval techniques. In our experiments BERT achieved a relevance score of 62% ,outperforming BM25 , which scored 55%. This demonstrates superior ability to capture contextual and semantic relationship in text. In conclusion, this study articulates prospective directions for future research within the realm of event detection, improving the efficacy of information retrieval in rapidly evolving news environments.
Authors - Sarika Kuhikar, Kashish Mishra, Tejas Dabholkar, Tejal Narvekar, Siddharth Suyal Abstract - This paper presents the design of a control circuit for a single-phase inverter capable of generating a pure sine wave output that is accurately aligned with the desired voltage amplitude and frequency. With the global shift toward renewable energy sources, the need for efficient and reliable power conversion systems has become more critical than ever. The proposed design utilizes advanced microcontroller technology along with modulation techniques such as Sinusoidal Pulse Width Modulation (SPWM) and Selective Harmonic Elimination (SHE). These techniques help achieve higher efficiency, significantly reduce harmonic distortion, and enhance the overall reliability of the inverter. This innovative approach contributes to improved energy efficiency and supports the development of smarter, more environmentally friendly power systems. The inverter is highly suitable for integration into solar energy systems, offering a stable and clean AC power supply for both residential and commercial applications. Its modular architecture also allows easy scalability to meet varying load demands and future upgrades.
Authors - Aditya Waradkar, Anagha Galagali, Niha Solkar, Shloka Suvarna, Aparna Bannore Abstract - Urbanization has resulted in a high rise in the use of vehicles, thus increasing parking problems like extended search times, fuel consumption, traffic congestion, and user frustration. To counter these problems, this paper introduces ParkSense, an IoT-based smart parking system that combines hardware and software elements for real-time parking space monitoring and management. It uses NodeMCU microcontrollers and IR sensors for car presence detection and an LCD display for real-time on-site updates. It has connectivity with ThingSpeak cloud to provide remote data access and visualization. The frontend is built with the MERN stack (MongoDB, Express, React, Node.js), and the Tailwind CSS provides a user-friendly and responsive interface on devices. ParkSense functionalities include real-time slot monitoring, access to historical data, administrative dashboards, and secure online payments. The system has proven to be highly efficient, reliable, scalable, and easy to use during testing and implementation. It saves considerable parking search time and fuel consumption, thus helping to create a more sustainable city environment. Future developments involve AI-based predictive analytics, dynamic pricing, personalized recommendations, and integration with EV charging stations.
Wednesday August 26, 2026 3:30pm - 5:30pm IST Virtual Room EGOA, India
