Authors - Palak Patel, Chintan Shah, Premal Patel Abstract - The use of Internet of Things (IoT) in healthcare systems the world has also measured substantial improvement in patient care by offering real time monitoring and diagnosis. But growing connectivity means that sensitive medical data is also vulnerable to numerous security threats like man-in-the-middle (MITM) attacks, key theft, and device cloning, among many others. These traditional methods for authentication, based on either passwords or static keys, are especially susceptible to such cyber-attacks; thus, they are ineffective in protecting healthcare IoT environments characterized by dynamic threat landscapes. This research article presents a new dynamic key-based authentication scheme for healthcare Internet of Things (IoT) networks. In the suggested framework, mutual authentication between each of the healthcare server and the physical IoT client device is used to protect the sensitive data of the patients in transit, along with providing real-time secure transmission. By focusing on how cryptographic keys develop, the framework addresses current security threats, including key theft and MITM attacks. Not only does this method keep computational and communication costs low; it is also lightweight enough for resource-constrained medical IoT devices using a lightweight authentication protocol. This system is efficient with respect to data size between registration and authentication phases while performing better than all other systems. It also minimizes latency and bandwidth usage by improving the caching layer. The scheme is also computationally cheap, making it feasible in resource-poor healthcare settings. The security analysis tools confirm that the suggested architecture is resistant to ordinary attacks such as MITM and replay attacks while incurring low overhead.
Authors - Abhijeet Kumar, Yash Shekhawat, Rahul Kumar, Naresh K Abstract - Real-time sign language recognition depends on an enhanced CNN-LSTM architecture which uses ASL training data. A new preprocessing approach boosts image resolution to 46×46 pixels thus enhancing the recognition precision. Numerous frames enter the system which enables a deep learning model to analyze spatial and temporal features to identify different hand signals in real time. The system integrates with the Flask-React-based frontend which allows real-time predictions through webcam interfaces in order to support practical field use. The system translates acknowledged signs into speech audio through Text-to-Speech APIs which drives inclusion between hearing-impaired users and people who do not use sign language. The system’s effectiveness was validated through experimental testing which reached 95% accuracy. The system's development will advance by implementing support for local sign languages with mobile platform integration.
Authors - Vitika Soni, Sakshi Chauhan, Harsho Mohan Chattoraj, Varun Dutt Abstract - There have been few direct comparisons in the literature between the emotional and autonomic impact of virtual reality (VR) and traditional and visual-narrative media, although VR has the potential to create vivid engagement. This study bridges this gap by using the culturally relevant narrative of King Harishchandra from the Indian Knowledge Systems (IKS) corpus to examine the impact of various storytelling media—text reading, comics, and virtual reality (VR)—on emotional and physiological engagement. We measured physiological arousal through Heart Rate Variability (HRV) indices, including RMSSD (parasympathetic modulation) and LF/HF ratio (sympathovagal balance), and emotional reactions through the Positive and Negative Affect Schedule (PANAS) in 30 participants (N = 10 per group). Comics performed better than text and virtual reality in terms of ratings of positive affect. While the VR and text groups reported significantly higher RMSSD values, reflecting more parasympathetic activity, comics registered the lowest LF/HF ratios, reflecting smoother cognitive-emotional processing and greater autonomic balance. The findings indicate that comics, despite being frequently neglected, can stimulate more stable emotional engagement than virtual reality immersion, which has implications for future application in therapeutic design, education, and culturally-based digital media. By emphasizing the importance of narrative coherence and cognitive manageability in shaping user experience, this research contributes to digital storytelling, affective computing, and media psychology.
Authors - Zaibunnisa L.H. Malik, Amena Niyaz Ahmed Malik, Pooja Raundale Abstract - Autistic Spectrum Disorder (ASD) refers to a group of developmental disorders that affect the nervous system, leading to challenges in social interaction, communication, and behavior. The severity of ASD symptoms can vary widely, ranging from mild to severe. Diagnosing and predicting ASD with high accuracy requires the use of advanced machine learning models. However, one of the major challenges in building such models is the availability of sufficient data. Open-source datasets often have a limited number of instances, which may not be enough to train robust models that can generalize well to new, unseen data. To overcome this limitation, it is essential to augment the dataset with additional, synthetically generated instances. In this context, techniques like corGAN (Conditional Generative Adversarial Networks) are employed. This comprehensive dataset is then used to train a machine learning model, which can more effectively predict ASD. The synthetic data ensures that the model has access to a richer, more varied set of information, ultimately leading to better performance and more accurate predictions for diagnosing and understanding ASD. We will also apply SMOTHE and Adaptive_Synthetic on GAN data, and prove that SMOTHE on GAN data gave a better distribution than Adaptive_Synthetic on GAN data.
Authors - Amitabha Bhattacharyya, Ayush Misra, Sourkarjya Kundu Abstract - The Relational Database was widely used in Industry and academics for software development in the early 90's and it is continuing now even starting from Oracle 7.1.3 version. But with increasing complexity of data, unstructured data or semi structured data capturing, research has shifted the paradigm from SQL to NoSql. Variety of NoSql databases has emerged and could not sustain in the market because of their own limitations. The Relational Database System has complex join operation and is costly compared to NoSql. 80% market share is grabbed by Oracle for any new software development in 2005 and later on researcher started inventing NoSql with low cost, open source and it’s quite challenging. In our IEEE 2020 we did survey different types of Graph databases and also showd the path on how to reengineer SQL to NoSql in the Springer publication. This paper aims to provide insights about Nosql MongoDB, relational database Oracle or MySQL, re-engineering methodologies study from sql to NoSql and also how much faster is SQL vs NoSql.
Tuesday August 25, 2026 9:30am - 11:30am IST Virtual Room DGOA, India
Authors - Devashree Kute, Anjali Naik Abstract - This study introduces a resource-efficient method for generating artwork in the traditional Madhubani style using Stable Diffusion XL (SDXL). Using the low rank adaptation (LoRA) technique, the model is fine-tuned with culturally relevant prompts and stylistic guidance to emulate the distinct characteristics of this Indian folk art form. To improve inclusivity and accessibility, prompt multilingual support is incorporated, covering Hindi, Bengali, Telugu, and English, through an automated translation mechanism, which retains the keyword to ensure consistency between languages. The training process is optimized for standard consumer-grade GPUs utilizing FP16 precision, CPU memory offloading, and a fixed Variational Autoencoder (VAE), enabling stable 1024×1024 image generation. For evaluation, a Contrastive Language Image Pretraining (CLIP) based scoring method is employed to assess the semantic alignment between prompts and generated images. The findings indicate that the style remains remarkably consistent across languages, demonstrating that even minimal training can enable AI to effectively capture and preserve traditional art forms within digital media.
Authors - Sonali Patil, Adwait Jadhav, Sahil Bhavsar, Pradnya Kamble, Arya Tandale Abstract - This research introduces the innovative AI-driven dashboard, leveraging Transfer Learning on Efficient Net and Retrieval Augmented Generation (RAG) for recommendation generation. The research is focused on minimizing the losses incurred due to diseases thus increasing the crop yield to meet the increasing demand. Our system incorporates the pre-trained Efficient Net model to classify the leaf images into various disease categories while LLAMA 3.1 8B LLM model is used to generate the remedy insights. Our methodology not only tackles the issue of hallucinations as well as correctness of the information which are very common in LLM response generation, but provides a personalized remedy plan for the farmer incorporating climatic conditions such as Temperature and Humidity as well. The suggested methodology not only aims at detecting diseases at early stages, but also at securing the necessary food supply, reducing the amount of pesticides used and promoting eco-friendly way of cultivation. Looking at the future, the project visions of increasing the use of LLMs in the agriculture industry by continuously upgrading RAG as well as the supplied documents to maintain up to date responses.
Tuesday August 25, 2026 9:30am - 11:30am IST Virtual Room DGOA, India
Authors - Athira T, Abhishek H, Deepak Gupta, Shobhana Palat Madhavan Abstract - With growing environmental awareness, consumers are drawn to sustainable offerings. Greenwashing — false environmental claims—has made consumers skeptical and impacted purchasing behaviour. Understanding greenwashing perception is vital to studying sustainable consumer behaviour, especially in India where research is limited. This paper explores greenwashing and sustainable consumer choices through a quantitative research design, using data from 223 Indian consumers analyzed via SEM and Mediated SEM in Stata. Employing the Value-Belief-Norm (VBN) Theory and the Theory of Planned Behaviour (TPB), the study examines the role played by environmental knowledge, green skepticism, social influence, and perceived moral obligation in sustainable consumption and green purchase intention, with greenwashing perception acting as a mediator. The findings suggest that perceived moral obligations are the strongest driver for greenwashing perceptions, as well as for green purchase intentions and sustainable consumption behaviour. Environmental knowledge enhances green-washing perception and sustainable behaviour. Green skepticism affects sustainable choices indirectly via greenwashing perception. Social influence affects greenwashing perception in a negative manner but sustainable consumption and purchase intention in a positive manner. These insights can help shape strategies that build consumer trust and promote genuine sustainable practices.
Tuesday August 25, 2026 9:30am - 11:30am IST Virtual Room DGOA, India
Authors - Keyur Kurani, Vaibhavi Machchhar Abstract - Ransomware is a rapidly evolving cyber threat with the potential to cause significant financial and operational disruption across industries. This paper analyzes the 2017 WannaCry attack, which exploited a Microsoft Windows vulnerability to infect over 200,000 systems globally, severely affecting sectors such as healthcare, including the UK’s NHS.The study explores ransomware’s progression from simple attacks to advanced, AI-powered variants. These modern forms use real-time analysis, intelligent target selection, and adaptive evasion techniques to bypass traditional defenses and enhance social engineering tactics.To counter these threats, organizations must adopt proactive strategies, including AI-driven cybersecurity solutions, behavior-based anomaly detection, and targeted employee training. Understanding the AI–ransomware link is essential for building resilient defenses against future cyberattacks.
Authors - Purvee Agrawal, Sanika Bhosale, Viraj Kakade, Vishal Jaiswal, Sarthak Baraliya Abstract - This paper presents the design and implementation of a Web Application Firewall (WAF) using machine learning models to effectively detect and mitigate three prominent web security threats: Distributed Denial-of-Service (DDoS), SQL injection, and Cross-Site Scripting (XSS). The proposed system leverages separate machine learning models for each attack type, optimizing detection by focusing on specific features unique to each threat. By analyzing traffic behavior, request payloads, and input structures, the WAF ensures high accuracy in identifying and blocking malicious activities. This multi-model approach significantly reduces false positives and enhances real-time protection. The solution is scalable and can adapt to evolving attack patterns, providing robust security for modern web applications and critical infrastructure.
Authors - Amulya Naik, Pallavi Dhaded, Shireesh Hakki, Satish Chikkamath, Suneeta V Budihal, Sujata Kotabagi Abstract - A text processing framework that applies Encoder-Decoder architecture with attention mechanism functions as the main focus of this research for resolving Natural Language Processing predictive problems. The research first outlines base technologies along with methodologies and frameworks required to build the system design. Detailed analysis of the dataset happens at this phase through observing dataset structure and calculating statistical summaries to detect missing or duplicate values. Better understanding of the dataset by using descriptive analytics to identify potential problems which leads them to improve the dataset. Data cleaning serves multiple functions during the process by eliminating unneeded columns together with missing value management and text normalization methods. The normalization procedure entails converting text into either upper or lowercase format and executes tag stripping alongside URL replacement and shorthand elimination and emoji and contraction removal. The processing begins after tokenization divides the text into segments. After cleaning the data the input and output components get separated while padding is used to maintain consistent dimensional structure.The model base incorporates an Encoder-Decoder framework combined with attention functionality while implementing a BiLSTM network. Through this specific model configuration both past and future inputs can be read contextually which boosts the prediction accuracy. The designed model produces 90.23 percent achievement in accuracy which highlights its strong capability in processing intricate NLP operations.
Authors - Abirami K, Megha Nayanar, Shobhana Palat Madhavan, Deepak Gupta Abstract - This study explores the impact of AI anxiety on career anxiety and career uncertainty, using Self-Determination Theory as its theoretical framework. As Artificial Intelligence (AI) continues to reshape industries, it presents both opportunities and challenges in career related factors. AI anxiety, driven by concerns over job security and skill obsolescence, affects individuals' confidence in their career choices and decision-making processes. As a result, many individuals may experience uncertainty about their professional future. By analyzing data from 237 respondents across India, this study identifies AI anxiety as a significant factor influencing career anxiety and career uncertainty. The findings reveal that individuals with higher AI anxiety are more likely to experience higher anxiety and indecisiveness in their career. However, the overall life satisfaction helps in lowering career anxiety by providing individuals with a higher sense of well-being that counteracts the anxiety. In contrast, factors such as relatedness, competence, and AI resilience do not show significant influence on either career anxiety or career uncertainty. This study enhances the understanding of how AI anxiety shapes career-related concerns, offering insights into how individuals navigate career decisions in an AI-driven world.
Tuesday August 25, 2026 12:30pm - 2:30pm IST Virtual Room DGOA, India
Authors - Shreya Ratagal, Parvati Navalur, Amruni Joshi, Satish Chikkamath, Sujata Kotabagi Abstract - With the instant advancement of generative artificial intelligence, this study investigates the domain of text-to-image generation, concentrating on the utilization of the Stable Diffusion model. The research examines the creation of visual content from written descriptions by leveraging sophisticated neural network architectures and underscores the importance of Natural Language Processing (NLP) in producing high-quality results. An extensive analysis was performed, integrating both qualitative and quantitative assessments to evaluate the model’s performance, scalability, and ability to adapt to various inputs. The study emphasizes potential uses in creative content production, virtual environments, and educational resources while tackling ethical issues to promote responsible AI practices. The results highlight the revolutionary effects of text-to-image generation in transforming the process of visual content creation.
Authors - Nishant Sharma, Mohit Mahlawat, Mohit Sharma, Gagandeep Singh, Ayush Kumar Singh, Kamlesh Sharma Abstract - An Early Warning System (EWS) utilizing Internet of Things (IoT) technology represents a transformative approach to disaster prevention and management. By leveraging interconnected devices, sensors, and real-time data transmission, IoT-based EWS enhances the ability to detect potential hazards—such as natural disasters, industrial failures, or environmental threats—at their earliest stages. These systems enable timely alerts and response strategies, minimizing risks to human life, infrastructure, and ecosystems. IoT technology plays a crucial role in gathering precise, real-time data from various sources, including seismic sensors, weather stations, water levels, and air quality monitors. This data is then transmitted to centralized platforms for analysis, allowing authorities and stakeholders to predict, assess, and act swiftly before a disaster strikes. With cloud computing and AI integration, IoT-enabled EWS can also deliver highly accurate forecasts and automated decision-making, further enhancing disaster resilience. As the world faces increasing threats from climate change, environmental degradation, and urbanization, IoT-based Early Warning Systems are becoming essential tools for safeguarding communities, enhancing preparedness, and ensuring a more resilient future
Authors - Jigme Nidup, Adithya Gattadi, Naresh K Abstract - Many serious health conditions, such as atrial fibrillation (AF), neuropathy, muscle disorders, and sleep-related neurological diseases, often go undiagnosed until complications arise. To address these challenges, this paper presents an advanced health monitoring system that integrates a multifunctional 4-in-1 electrogram sensor capable of measuring muscle activity using Electromyography (EMG), eye movement using Electrooculography (EOG), brain activity using Electroencephalography (EEG), and heart rhythm using Electrocardiography (ECG), along with a body temperature sensor, into a compact and wearable device at low cost. The device leverages the ESP32 Wi-Fi module to process and enable seamless data transmission to a Message Queuing Telemetry Transport (MQTT) cloud platform, ensuring secure, efficient, and scalable storage and analysis of collected health data. The system uses multiple pre-trained CNN models, each specialized in detecting specific diseases. Tests show an average accuracy of 90.3% making it a cost effective and efficient solution.
Authors - Harsha M R, Jyotiradhitya Kallimani, Nitishgouda Patil, Tohid Bijalikhan, Satish Chikkamath, Suneeta V. Budihal, Sujata S. Kotabagi Abstract - In India what does it take to go PRO in football? Players from their childhood shred sweat,blood and their precious time. And in sport we know that the next-gen superstar is guaranteed to start off his career from local and youth leagues. And honestly speaking these leagues do not offer the resources to invest. Resources in the sense that include technical skills, opponent player data, and event data(player stats). Currently, event data is mostly collected manually by human individuals, who gather data in several steps and through numerous persons involved. And this manually collecting data requires a lot of human resources and requires multiple checks and for that reason collection data is not practical in local or youth leagues. And this process takes a lot of time. So Automatic event detection could provide event data faster. which players can take use to analyse players and their own performance. And through which scouts would be able to ensure no player is missed or overlooked.
Authors - Arushi Madaan, Sunita Garhwal, Anu Bajaj Abstract - Women today are most likely to be experiencing Polycystic Ovary Syndrome (PCOS), a hormonal imbalance disorder. This disorder mostly affects women’s ovaries, where a large number of tiny fluid-filled sacs called cysts—also referred to as follicles—form around the ovary’s periphery. The exact root cause of PCOS is still unknown despite advances in science. Using ultrasound (US) scans to identify numerous follicles is an efficient way to diagnose PCOS early and schedule treatment. The primary purpose of this article is to determine whether or not a woman has PCOS or not without supervision from a physician. In this work, we provide a deep learning (DL) method based on transfer learning for PCOS classification using US ovarian images, with the goal of improving diagnostic efficiency and precision. InceptionV3 and ResNet50 models, which had accuracy rates of 99.68% and 97.5%, respectively, were used for this research. The study’s findings show that, as compared to conventional machine learning (ML) techniques, transfer learning-based classification performs better in PCOS variant classification. This study aims to accurately diagnose PCOS in patients and use our proposed model to treat PCOS. Gynaecologists and other medical professionals can benefit from our model’s ability to provide a prompt, reliable, and correct response.
Tuesday August 25, 2026 12:30pm - 2:30pm IST Virtual Room DGOA, India
Authors - Sushma Vispute Priya Surana, Shubhangi Vairagar, Sujit Shaha, Omkar Shinde, Sameer Sambhare, Krushna Salbande Abstract - Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition affecting social interaction, communication, and behavior. Autism is the third most common developmental disorder in the world. In India, the prevalence of autism is increasing and is estimated to be around 1 in 68 children. With its rising prevalence, early detection is crucial for timely intervention. This paper serves as both a review and a research study. The review explores existing ASD detection methodologies, highlighting machine learning approaches such as multinomial logistic regression (MLR), support vector machines (SVM), and convolutional neural networks (CNN), along with tools like eye-tracking and EEG analysis. The research component applies machine learning models—including Logistic Regression, Decision Tree, Random Forest, SVM, KNeighbors, Naive Bayes, and Neural Networks—on AQ10 survey data (1054 samples, 19 features) to evaluate their effectiveness. SVM achieved the highest accuracy. Further analysis examined the necessity of all 10 AQ10 questions, revealing that AQ4 and AQ10 contribute the least to predictive accuracy. Heatmap analysis confirmed weak correlations with the total ASD score. These findings suggest that refining ASD screening tools by removing less informative questions can improve efficiency while maintaining diagnostic reliability.
Authors - Vijayalakshmi S Katti, Usha J Abstract - Pests, pose a pervasive threat to agriculture on a national scale. Their voracious feeding on plant roots results in diminished crop yields, leading to economic losses for farmers and potential food security challenges. The integration of advanced sensor technologies and deep learning offers a promising avenue to address the impact of Pests, enabling timely and accurate detection. This, in turn, allows for targeted and efficient pest management strategies, mitigating the widespread repercussions of infestations and fostering sustainable agricultural practices on a national level. This paper explores the transformative synergy between sensor technologies and deep learning techniques for the identification and density detection of pests in agriculture. Traditional methods face limitations, prompting a shift towards advanced technologies. We survey the landscape of sensor technologies, including image sensors, acoustic sensors, and soil sensors, highlighting their real-time, high-dimensional data contribution. Integration with deep learning models, such as Convolutional Neural Networks and Recurrent Neural Networks, offers a precise and adaptive approach to pest management. The potential impact of this integration is substantial, promising increased crop yields, reduced reliance on broad-spectrum pesticides, and improved environmental sustainability. The review underscores the importance of continuous adaptation and scalability, setting the stage for a future where technology plays a pivotal role in ensuring the health and productivity of agricultural landscapes.
Authors - Ashwini Jarali, Sanskruti Lad, Snehal Kavathekar, Prajwal Lalpotu, Shreya Jadhav Abstract - Potholes on roads significantly impact safety and road infrastructure, leading to accidents and increased vehicle damage. Timely detection and repair are crucial to address these issues effectively. This paper presents an AI-based system for automated pothole detection and reporting, aimed at improving pothole management for road maintenance authorities. The system uses the YOLO (You Only Look Once) object detection model to accurately identify potholes in real-time road imagery, combined with GPS for precise localization. Detected potholes are automatically reported to the relevant authorities via email, ensuring swift corrective action. The YOLO model is trained on a diverse dataset of pothole images, achieving high detection accuracy across various pothole sizes and shapes. Additionally, the system tracks the status of reported potholes to ensure repairs are completed. This solution enhances road safety and reduces manual effort, providing a comprehensive approach to modern road maintenance.
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
