Authors - Rachit Chetankumar Mehwala, Angshuman Kishore Mahato, Patil Sujit Maruti, Surendra Solanki, Gaurav Kumawat, Ravindra Kumar Soni Abstract - Disaster whether it is natural or man-made most of time led to significant challenges to society, often resulting in loss of life, economic instability, infrastructure damage. Effective "Disaster Management" requires seamless communication and good connectivity under extreme conditions. 6G technology with its capabilities such as ultra-low latency, large bandwidth, and terahertz frequencies offers an evolutionary approach to real-time disaster management. This paper explores how 6G technology can be harnessed to build reliable system capable of alleviating the impacts of disasters. By using 6G’s unparalleled communication capabilities we propose framework that ensure strong connectivity and efficient resource allocation in real-time and monitoring during disasters.
Thursday August 27, 2026 9:30am - 11:30am IST Virtual Room EGOA, India
Authors - Kumkum Saxena, Akshay Rathod, Shagun Gupta, Archie Shah, Deep Prajapati Abstract - Professional scarcity, the absence of individualized treatments, and access to mental health assistance only in limited regions creates problems in mental health care. These issues can fundamentally be solved with the introduction of AI. AI has powerful applications in the realm of mental health care, including systematic classification and analysis of data, as well as facilitating tracking and predictive treatment that leads to personalized medicine. Chatbots, predictive analysis and cognitive computing try to give precise diagnosis by facing the unsolved challenges in the empiric domain of cognitive sciences. This review attempts to highlight the need for multidisciplinary collaboration and more research so that mental health care AI systems are more inclusive.
Authors - Amruta Amuna, Hardik Rokde, Anuj Gosavi, Gaurang Gulhane, Ishaan Chepurwar, Arnav Jadhav, Vinayak Musale Abstract - In today’s digital age, most of the work has become sedentary. Whatever you want to do is now available at the tip of your fingers, reducing your physical activity and causing individuals to suffer from chronic health issues, such as back pain and postural disorders. Yoga has been publicised throughout the world, and now all of us are aware, and many of us even perform yoga regularly. But doing yoga is not enough. We must do it efficiently & with the correct posture to get the benefits of yoga. It has been observed that more than 40% of people doing yoga do it incorrectly. This motivated us to develop a smart, Iot-enabled solution that addresses this problem during yoga practice. The system integrates Force Sensing Resistors (FSRs) to measure the force applied on the sensor, LED indicators to guide the correct position for palms and feet for every yoga pose, and a buzzer to give auditory feedback for misalignment. By combining all of these along with the Arduino Uno microcontroller board, this solution bridges the gap between self-guided yoga practice and expert supervision, making each yoga pose more efficient, easier, and more effective for the user’s body.
Authors - Sahana S, Umabharati H, Rakshita.G, Vaishanvi.K, Nikita Patil Abstract - In a populous nation like India, one fundamental need is travel so travel encompasses road, rail and water. Road transport is most utilized and also the prime reason people get to lead simple lives. Even after studying the present situation, we found a major problem on the roads: potholes. These potholes have become a source of harm to the condition of the roads and an additional threat of accidents on the roads. The detection of potholes is vital for safety on roads. The best method for pothole detection is using the real-time accurate efficient YOLOv10 model. A Raspberry Pi Camera Module can record real-time video and images of the road. Further, the Raspberry Pi can be integrated with a GPS module to find the precise coordinates of any potholes. The data generated by the GPS module is helpful in making repairs and guiding drivers in choosing routes. The system relies on a Convolution Neural Network (CNN) model, which assists in pothole detection using YOLO models.
Authors - Jai Ramani, Tanay Kelkar, Darshil Shah, Divanshu Maheshwari, Archana Nanade Abstract - Anonymized employee reviews on platforms like Ambition-Box offer insights into workplace experiences such as salary, work culture, working hours, and management quality. However, manually analyzing large volumes of reviews is challenging and time-consuming. To overcome this limitation, an automated system is proposed to collect, process, and present employee sentiment in a structured and meaningful way. Using the technique of Aspect-Based Sentiment Analysis (ABSA), the system classifies reviews as positive or negative while identifying sentiment across key concerns such as salary, work-life balance, career growth, management quality, etc. To identify the keywords and their corresponding sentiment, this study utilizes the T5 model that is fine-tuned using the InstructABSA framework. Data is gathered through web scraping, ensuring coverage of employee opinions from multiple platforms. The resulting analysis highlights areas where companies excel or need improvement, providing actionable insights to enhance the workplace.
Authors - Dhaval Shah, Shivani D. Anjaria, Bhupendra Fataniya Abstract - Hardware Security is the key aspect of the integrated circuit’s life cycle; Any malicious modification in the system design at the foundry is a significant concern for hardware threats, known as a Hardware Trojan attack. These Trojans are very difficult to detect in the real world, even during manufacturing and testing. In this article, the impact of Hardware Trojan on the performance of the cache memory is presented. Insertion of Trojan demonstrated in the cache replacement policy, which replaces the original cache replacement (least recently used, first in first out, and least frequently used) policies with another replacement policy (most recently used). The performance was analyzed in the gem5 simulator after inserting a Trojan. It was clearly evident that Trojan insertion degrades cache performance and affects overall processor performance. It is observed that the impact on the Trojan was a savior on LFU compared to other cache replacement policies, since LFU persists in its counter-based memory.
Authors - Ajay Talele, Revati More, Satej Patil, Shreya Bedre, Gokarn Nemade, Harshwardhan Vanmore, Dipak Parvate, Sakshi Dhumale, Varun Deshmane, Vedika Dange Abstract - An Advancement in Effective Parking Solutions: The Smart Car Parking system. In cities, parking congestion results in wasted time, fuel, and irritated drivers. By offering real-time parking availability updates, expediting the procedure, and lowering traffic in parking lots, the Smart Car Parking System provides an answer. The system, which was constructed with an Arduino Uno microprocessor, uses infrared (IR) sensors to identify whether a car is in each slot. To assist drivers in making educated judgments, a linked LCD shows real-time data on available and occupied spaces. Entry and exit barriers are controlled by servo motors, which grant only permitted access. The technology automatically updates the slot status when cars enter or exit. This improves traffic flow, lowers pollutants, saves fuel, and lessens the need for manual supervision. The system is perfect for public lots, workplaces, malls, and residential areas. It can be improved with AI for space optimization and IoT-based apps for slot reservations, increasing the sustainability and efficiency of urban parking. [5]
Authors - P Sanjana, Smrthi Harits, Divyadarshan C.S Abstract - This project aims to solve the issues faced by Bharatanatyam dancers in accessing the translations and interpretations of compositions used for Bharatanatyam performances. Understanding these compositions is crucial in depicting an accurate vision of the composition. Through structured user interviews, it was discovered that due to the non-preservation of these translations and limited access to scholars, accurate translations, and music resources, dancers face significant issues. This hinders their creative process and restricts their creative freedom. Compared to experienced dancers with access to vast resources, the upcoming artists have very few such connections and guidance, making them more vulnerable to this problem. This necessitates a solution to reduce the accessibility issue faced by the upcoming dancers. Various design methodologies were employed to tackle the issues faced by Bharatanatyam dancers. To bridge this gap, Kalaahithaa, a digital service platform, was designed to provide a repository of verified translations, connect dancers to scholars, musicians and teachers to help them understand the compositions, create new compositions and choreographies, and foster collaborative opportunities. This solution was further developed using a service design blueprint, user flows, site maps, and low-fidelity and high-fidelity prototypes. The platform supports personalised interactions, enabling dancers to seek expert guidance, upload and access translations, and engage in meaningful exchanges that enhance their understanding of compositions while promoting monetary ethical practices. By centralising resources and fostering connections with a usercentred approach, Kalaahithaa serves as a vital tool for dancers to refine their art while preserving the integrity of Bharatanatyam.
Authors - Sonali Antad, Kalyani Ghuge, Prakash Sharma, Vaishnavi Chirawande, Aditi Gade, Shweta Ahire, Sharvari Jadhav Abstract - This study describes an AI healthcare chatbot that automatically assesses patients’ medical needs, conducts inter- views and performs thorough health analyses. Using several state-of-the-art natural language processing (NLP) models, including sentence transformers and Llama-based large language models, the system analyzes user symptoms and classifies them into specific medical domains like diabetes, blood pressure, skin and stomach disorders. It uses Gemini API’s generative AI. This dynamically creates several questions and suggests some medical diagnoses. The platform’s improved fishbone diagram considerably aids root cause analysis by visually showing how potential causes relate to user-reported symptoms. Our project creates efficient, interactive healthcare assistants that improve access to preliminary medical advice.
Authors - Rhucha Deodhar, Tanya Gadwal, Ananya Bhat, Aditi Hinge, Shilpa Pant Abstract - This paper presents a real-time, vision-based system for Indian Sign Language (ISL) recognition and translation, aimed at enhancing communication between the deaf community and non-signers. The system combines a CNN-LSTM architecture for static gesture recognition, achieving an accuracy of 98.47% and introduces GestureNet, a bidirectional LSTM model trained on a custom dynamic gesture dataset, which attains 96.83% recognition accuracy. Ad-ditionally, a Generative AI framework is integrated to convert recognized ges-tures into semantically coherent and contextually appropriate sentences. By em-phasizing real-world applicability and high recognition performance, the pro-posed system advances sustainable and accessible communication technologies, with potential impact in education, public services, and digital inclusion, partic-ularly in developing regions.
