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.
