Authors - Harshit Kadam, V Harshavardhan, Shrigouri S G, Bhavana Gadagin, Nalini Iyer, Prabha Nissimagoudar Abstract - One of the key challenges for blind spot detection systems is the ability to detect and track objects in irregularly shaped regions. This problem becomes much more severe in addition to considering different vehicle velocities, motions due to other objects, and many different environmental conditions. Ordinary systems have fixed parameters for operating conditions, which either are slow or may not adapt to changeable driving scenarios. The proposed solution is an adaptive continuous monitoring approach that provides the defined polygons with the ability to report on any encroachments while giving a proximity risk based on context data. This real-time adaptability allows the system to provide accurate and timely notifications to the driver, thereby increasing the safety of critical events such as lane changes, parking maneuvers, or heavy traffic situations where blind spot threats are most prevalent. Initial results show that this adaptable system can outperform traditional precision and time response methods, improving overall safety while driving.
Tuesday August 25, 2026 3:30pm - 5:30pm IST Virtual Room DGOA, India
Authors - Sharim Iqbal, Purnima Ahirao, Deepti Patole Abstract - QR code payment systems have become increasingly popular thanks to their speed, simplicity, and convenience. However, as their usage grows, so do concerns around security—issues like tampering, spoofing, and man-in-the-middle attacks are becoming more common. To address these vulnerabilities, this paper introduces a novel approach to securing QR-based transactions using visual cryptography. Visual cryptography works by splitting an image into multiple shares, which individually reveal nothing but can reconstruct the original image when overlaid—without the need for complex decryption algorithms. This research proposes a secure QR payment system that leverages visual cryptography to enhance data integrity, prevent fraud, and strengthen the overall security of mobile payments. The study covers current security challenges, outlines the proposed system architecture, implementation details, and evaluates its performance.
Authors - A.Akshaya, G.Veera yasaswini, P.Akshith, K.Mahimanusha, M.TanviSahasra, Sushmarani Abstract - Sentiment analysis is among the primary natural language processing (NLP) tasks and is widely utilized for extracting emotions and sentiments from text corpora. This paper proposes a comprehensive sentiment analysis approach for movie reviews based on Word2Vec, TextBlob, VADER, and Gated Recurrent Units (GRU). Word2Vec is employed for word embeddings to extract semantic word relationships for improved feature representation. TextBlob and VADER are implemented as lexicon-based sentiment analysis tools, for which TextBlob is interested in polarity and subjectivity and VADER is engineered for short texts with clear-cut sentiment indications. Besides, deep learning architecture in the form of GRU is employed for extracting long dependencies and context associations between words of text corpora for enhanced sentiment classification. Methods are experimented and contrasted on the basis of a benchmark IMDB dataset with reference to accuracy, precision, recall, and F1 score. Experimental findings substantiate that sentiment handling by deep learning-based approaches, i.e., GRU via Word2Vec embeddings, is better than traditional lexicon based approaches. The work provides insights to NLP-based opinion mining researchers and practitioners regarding the merit of utilizing hybrid approaches towards sentiment classification.
Tuesday August 25, 2026 3:30pm - 5:30pm IST Virtual Room DGOA, India
Authors - V M Aparanji, Chaithanya C K, Nanditha N, Poornima H S, Shreya A N Abstract - Face recognition is a vital biometric technology with growing applications in security, access control, and automation. Despite challenges such as lighting variation, facial expressions, pose angles, and aging effects, Support Vector Machine (SVM) has proven effective in modeling and classifying facial features due to its ability to construct optimal decision boundaries in high-dimensional spaces. In this study, SVM was applied to a face recognition system for smart lock operations, yielding strong performance metrics of 89.9% accuracy, 89.7% precision, 89.5% recall and an F1 score of 89.7%. These results demonstrate the capability of SVM to effectively manage facial variability while maintaining high accuracy in face recognition tasks.
Authors - Ajay Talele, Amruta Mankawade, Aryan Sutar, Nishit Shelar, Urvesh Somwanshi, Anushka sonde, Shiv Sagar Singh, Sharvari Savardekar, Shivanand Satao, Shridhar Sarda, Raj Bapat Abstract - A Medicine Recommendation System intended to use user-provided symptoms to identify possible diseases and provide personalized suggestions for safety measures, diets, drugs, and exercise regimens. The system predicts symptoms using Natural Language Processing (NLP) and Fuzzy Matching, guaranteeing accurate identification even in the presence of noisy inputs. It makes predictions about likely diseases and obtains overarching information for each by comparing extracted symptoms with a disease- symptom dataset. The system, which was developed with the Flask framework, provides an intuitive online interface for smooth communication. This initiative aims to direct users toward informed medical treatment by showcasing potential in early disease identification. Future research will concentrate on improving accessibility more broadly and integrating healthcare data in real-time.
Tuesday August 25, 2026 3:30pm - 5:30pm IST Virtual Room DGOA, India
Authors - Praniv Warungshe, Siddhi Desale, Sushant Mhatre, Rujata Chaudhari, Saylee Lapalikar Abstract - After graduation, alumni often face difficulties in managing important academic documents such as Leaving Certificates (LCs), marksheets, Letters of Recommendation (LORs), and convocation updates. Traditional processes are manual, time-consuming, and lack real-time tracking, leading to delays and repeated campus visits. To address these challenges, a cross-platform application was developed using React Native to simplify and digitize post-graduation work-flows. The application enables alumni to request, verify, and correct documents through a single digital platform with real-time status updates. LCs and marksheets are available on-screen, making the verification process faster and more convenient. Convocation details can also be managed easily within the app. Faculty benefit from tools to handle LORs and other academic tasks efficiently, while administrators use a centralized dashboard to track applications, generate reports, and respond to urgent requests quickly. The cross-platform nature ensures seamless access across various devices, enhancing user convenience. By uniting all stakeholders on one platform, the system boosts transparency, reduces manual workload, and modernizes the overall process, offering an efficient and user-friendly solution to manage alumni post-graduation needs.
Authors - Abhishek A Joshi, Vasudhaika S, Sinchana Chindi, Kaushik Mallibhat, Satish Chikkamath Abstract - The proposed work aims to present a novel framework to classify the oral diseases through multimodal data consisting of images and symptoms. The outcome of the work helps towards early diagnosis of oral diseases. Oral diseases are neglected by most people in the initial stages due to lack of awareness, accessibility, and availability of dental care. Early diagnosis of oral disease is necessary, and early research focused on either the image of the affected area or just the textual description (symptom) to predict the illness; both are essential. The study employs multimodal approach and utilizes an image dataset comprising seven categories of affected areas, sourced from Kaggle. Additionally, a textual symptoms dataset was developed, consisting of 150 combinations for each illness. Confidence scores of both the model (image-classifier model and symptom-based illness prediction model) with the true label, a new dataset is generated and it is trained with logistic regression to get the final predicted class. Image classifier model achieved 81% of accuracy whereas symptom-based model 97%, resulting in final multimodal accuracy of surpassing both the model accuracies.
Tuesday August 25, 2026 3:30pm - 5:30pm IST Virtual Room DGOA, India
Authors - Laxmi Sanjay Badiger, Keerti, Sumangala Basavaraj Donkanavar, Satish Chikkamath Abstract - Sentiment analysis is a vital task in NLP. That identifies the emotions in the text. Many studies concentrate only on the English language. There is an insufficient resource in Kannada language emotion analysis. This paper examines the sentiment in Kannada text using a manually prepared dataset. The datasets are divided into three classes positive, negative, and neutral. The processing techniques like context cleaning, tokenization, and sequence padding are used. The model uses RNN-LSTM which is efficient in handling the sequential data. The embedding layer is used to represent the words, the LSTM layer is used to get the context of the sentence, the Dropout layer is used to reduce the over fitting of the model and the dense layer is used to classify the sentiments into categories. The effectiveness of the model was measured using evaluation metrics like precision, recall and f1 score. By predicting sentiments for Kannada text, the paper also exhibits practical use of the model. This study demonstrates that LSTM-based models work well for sentiment analysis in Kannada. It also emphasizes the importance of creating and using manual datasets for low resource languages. The findings would be helpful to further research, and the out-comes can be directly applied in many areas, including social media monitoring, customer feedback analysis, and regional language processing.
Authors - Bhadouriya Khushi Mukeshsingh, Rajput Adityasingh Shashikantsingh, Parmar Smit Dharmeshkumar, Tiwari Prashant Dineshkumar, Soumya Kiran Prajapai, Nirav D. Mehta, Anwarul M. Haque Abstract - The accelerating proliferation of electronic devices has led to a surge in electronic waste (e-waste), presenting a significant environmental and resource management challenge. Conventional e-waste disposal practices are inadequate, often resulting in the release of hazardous substances and the loss of valuable materials. This paper explores a sustainable framework for e-waste reduction by leveraging advancements in power electronics and promoting standardization across electronic design and manufacturing. Key areas of focus include the adoption of fixed-type ports for power and data transfer, implementation of mandatory certification and testing standards for electronic components, and the design of modular and fixed PCBs to facilitate component reuse. Emphasis is placed on developing universal and multipoint-compatible components, particularly in the context of electric vehicle (EV) charging infrastructure, where interoperability can significantly reduce hardware redundancy. The integration of circuit protection mechanisms is proposed as a means to extend product lifespan and minimize failure-induced waste. Furthermore, strategies for the reuse and remanufacturing of components are examined as critical elements of a circular economy. By combining technical, regulatory, and design-driven approaches, this study outlines a comprehensive pathway toward reducing the environmental footprint of electronics and fostering sustainable innovation in power electronics.
Authors - Ayush Kayasth, Vrund Raval, Keren Khambhata, Nirali Nanavati Abstract - India leads the world in textile production and exports, but it also faces an increasing environmental problem: the country produces about 7,800 kilotons of textile waste a year, or 8.5% of the world's total. Even with dispersed efforts at recycling and reuse, progress is still hampered by the lack of a centralized, digitalized infrastructure for managing textile waste. In order to reduce waste in the Indian textile industry, our study proposes TextileLoop, a mobile application based on the ideas of the circular economy. The research employs a qualitative methodology, with focus group discussions conducted among key stakeholders in Surat India’s leading textile hub. These conversations uncovered important issues, such as small buyer networks, reliance on offline trade, and traditional players' reluctance to adopt new technologies. The results served as a guide for creating Textile Loop, a B2B platform with an MVC architecture developed with Flutter and Appwrite. In addition to providing educational materials and a collaborative environment for industry stakeholders, the application makes it easier to exchange excess textiles, faulty goods, and used machinery. Its integrated approach, which combines digital infrastructure with circular economy strategies, is what makes it innovative and the first circular economy based platform in India. Our platform aims to scale to a B2C and C2C model, allowing for greater engagement. With implications for both industry and policy, this work offers a workable plan for converting India's textile sector into a circular and sustainable ecosystem.
Tuesday August 25, 2026 3:30pm - 5:30pm IST Virtual Room DGOA, India
