Authors - K Shailaja, Shirina Samreen Abstract - Diabetes mellitus is becoming an increasingly critical health issue across globe that demands early diagnosis and continuous monitoring. Although traditional machine learning models have been employed to predict diabetes, they frequently lack clarity and have difficulty in identifying uncommon or rare patient cases. This research proposes a novel explainable hybrid framework that combines deep learning-based temporal modeling with classical machine learning and unsupervised anomaly detection. The framework utilizes multimodal data sources, including static clinical features, time-series Electronic Health Records (HER) and wearable sensor data, for robust diabetic risk assessment. Explainability is achieved using SHAP (SHapley Additive exPlanations) and counterfactual reasoning to provide both global and local interpretability. In addition, an autoencoder-based novelty detection module identifies patients whose health patterns deviate significantly from the normal. Experimental results on benchmark datasets demonstrate improved prediction accuracy, better anomaly identification and enhanced interpretability making the model suitable for real-world clinical decision support systems.
Authors - Vishal Ambhore, Ketki Kshirsagar, Parikshit Mahalle Abstract - This survey paper thoroughly examines the landscape of access control within the context of the Internet of Things (IoT). With the rapid expansion of IoT technologies, ensuring secure access to resources and data has become a critical concern. We thus present a very wide-ranging review of existing literature, doing an all-round analysis of current access control mechanisms in order to discuss their strengths, weaknesses, and applicability to IoT environments. The result of our investigation shows few major gaps and challenges, such as issues of scalability, interoperability concerns, and a call for access policies that account for contexts. Taking this into account, we introduce new approaches and improvements to positively address these shortcomings and boost the security and efficiency of access control in IoT systems. By integrating findings from multi study research endeavors, we provide new points of view and approaches toward IoT security improvement. Furthermore, we present potential future research directions and challenges to guide development for more resilient and adaptive access control solutions in IoT ecosystems. The paper is a ready reference for researchers, practitioners, and policymakers who want to bolster the security posture of IoT deployments and mitigate newly emerging cyber threats effectively.
Authors - Garima Ahuja, Heena Hooda, Vimmi Malhotra Abstract - The combination of agriculture and Information and Communication Technologies (ICT), known as e- Agriculture, is changing farming practices, especially in regions facing limited resources and climate challenges. e-Agriculture includes digital tools such as mobile-based advisory platforms, Geographic Information Systems (GIS), Internet of Things (IoT), Artificial Intelligence (AI), and data analytics. These technologies provide timely information, improve the use of inputs, and increase access to markets and financial services [1]. Applications of ICT in farming across different regions show improvements in crop planning, yield monitoring, and risk reduction. Evidence from India, Kenya, and the Netherlands highlights positive results where digital tools are adapted to local needs. Improvements include better harvest management, lower post-harvest losses, and increased climate resilience [3]. Despite these benefits, challenges such as poor rural connectivity, low digital skills, and limited policy support continue to affect the full use of such technologies. Scaling e-Agriculture requires affordable access, local training, and supportive ecosystems. Broader adoption can support sustainable farming systems and contribute to goals such as food security, poverty reduction, and environmental protection.
Authors - Khloe Bhel, Krutthika Hirebasur Krishnappa Abstract - Healthcare data growth at an exponential rate together with rising cyber threats require sophisticated cryptographic systems to protect Electronic Health Records (EHRs) from unauthorized access and data tampering. The research develops a cryptographic system which uses simulated quantum key distribution through the E91 protocol to generate symmetric encryption keys that is encrypted using Advanced Encryption Standard (AES) in Cipher Block Chaining (CBC) mode to protect healthcare data. The system operates by using IBM Qiskit’s AerSimulator backend to create entangled qubit pairs for deriving a quantum-safe key between two communicating parties. The entropy measurement of the generated key approaches the maximum value of randomness which provides effective protection against brute-force attacks. The AES encryption process using achieves rate of approximately 6.58 MB/sec during encryption operations and 9.34 MB/sec during decryption operations. The proposed method demonstrates efficient computation and deployment potential for healthcare applications with limited resources including edge-based IoT medical devices and federated learning systems. Security analyses show that this approach gives a good protection against both classical attackers and near-term quantum attackers. The research shows how post-quantum cryptographic methods can be practically used to protect future healthcare systems.
Authors - Suprit V. Hatti, P. G. Sunitha Hiremath, Manohar Madgi, Neha Tarannum Pendari Abstract - This study presents a comprehensive analysis of the factors influencing cognitive decline in Alzheimer’s patients in the USA. The objective is to examine cognitive decline and dementia prevalence across various U.S. regions, focusing on gender, age, and race/ethnicity disparities. Using data from the Behavioral Risk Factor Surveillance System (BRFSS), collected from 2015 to 2021, 59 locations were categorized into Northeast, Midwest, Southeast, Southwest, and West regions. The original dataset contained 31 attributes. The analysis included year-wise trend examination, gender-wise, age-wise, and race-wise distribution assessments, and identification of key risk factors. The Midwest (21.98%) and West (21.83%) showed higher cognitive decline rates, with significant disparities across demographics. The ‘Overall’ age group showed the highest prevalence at 8.54%. Females were most affected, with diabetes, asthma, arthritis, depression, and cardiovascular diseases being the most correlated comorbidities.
Authors - Reshma Y Totare, Anushka Kurandale, Sakshi Kuyte, Kiran Mane, Snehal Nale Abstract - With the increasing reliance on online reviews across various digital platforms, user feedback has become a vital factor in influencing public perception and decision-making. Since users cannot physically verify products or services online, they often depend on reviews to assess quality and credibility. This dependency has led to a rise in deceptive practices, where fake reviews are used to mislead audiences—either by promoting certain offerings or undermining competitors. Detecting such fraudulent content presents a significant challenge in the field of natural language processing (NLP), due to the subtle and human-like nature of these reviews. In this project, we present an approach for fake review detection using a deep learning model that combines Long Short-Term Memory (LSTM) networks with Bidirectional Encoder Representations from Transformers (BERT). Our model utilizes LSTM’s ability to capture long-range dependencies along with BERT’s contextual language understanding to enhance detection accuracy. To improve practicality and trustworthiness, we incorporate several additional features plugin support for easy integration into various review-based platforms, multilingual capability to handle reviews in different languages, and LIME (Local Interpretable Model-agnostic Explanations) to provide word-level interpretability of predictions. We evaluate our model on publicly available datasets containing both real and fake reviews, and the results demonstrate that our LSTM-BERT approach significantly outperforms traditional machine learning techniques. This work contributes to the growing efforts in combating misinformation and enhancing the credibility of online content across diverse platforms.
Wednesday August 26, 2026 9:30am - 11:30am IST Virtual Room EGOA, India
Authors - Ajay Talele, Siddhi Shingate, Om Gaikwad, Mahek Sayyad, Sai More, Pooja Nanaware, Ashwini Borole, Pratiksha Bile, Bharat Bangar Abstract - Crime prevention is being transformed by the Internet of Things (IoT) through proactive, smart solutions. Abstract This paper presents some techniques at the time of crime prevention by the use of IoT. Through the use of IoT driven surveillance systems, integrated smart sensors, real-time data aggregation and analytics, and automated alerts to emergency services and law enforcement, crime prevention, identification, and response are improved significantly. AI in Smart City: The study also calls out real-time examples such as smart cities establishing AI powered CCTV, IoT-enabled access control, predictive policing[1] The contemporary world faces the overwhelming challenge of crime. Criminal activity exists in almost every country and the statistics for certain nations are quite alarming. The advancement of technology has been one of the most significant factors in the development of crime control and prevention measures, such as drone surveillance, GPS tracking and tagging, closed circuit television cameras, etc. Technological advances such as the IoT (Internet of Things), Machine Learning, and Edge Computing call for the attention of the scientific world in regards to the question: how can they utilize these innovations for the purpose of minimizing criminal activities globally? In conclusion, the research results reflect on the employability of AI and IoT technologies in reinforcing security and obstructing offences against state and equally, challenges and ethical issues that accompany their employments [2]
Authors - Sambhram Pattanayak, Archana Paswan Abstract - The evolving landscape of dialogue replacement in film and television production is examined, focusing on advancements and challenges in on-location Automated Dialogue Replacement (ADR) recording and the emergence of real-time dialogue replacement technologies. Advancements in portable recording equipment, microphone technology optimized for field use, and specialized software solutions have significantly enhanced the feasibility and quality of ADR conducted outside traditional studio settings. However, on-location ADR presents unique challenges related to acoustic control, environmental noise, actor availability, logistical complexities, and technical limitations. The paper also examines the current state and potential impact of real-time dialogue replacement technologies, which offer immediate solutions and increased flexibility on set and in post-production. Case studies illustrate the practical applications of on-location ADR, while the discussion of future trends highlights the anticipated integration of artificial intelligence, advancements in hardware and software, the influence of remote collaboration tools, and the potential for incorporating virtual and augmented reality into dialogue replacement workflows. This analysis underscores the ongoing evolution of audio post-production techniques aimed at enhancing efficiency and creative possibilities in modern filmmaking. The research findings presented in this study underscore the importance of ADR in film production and introduce novel approaches and technologies. These advancements provide filmmakers with state-of-the-art tools and techniques, revolutionizing their creative processes and enhancing the quality of their productions.
Authors - Mario Castro Romero, Carlos Ernesto Carrillo Arellano, Leonardo Daniel Sánchez Martínez Abstract - Software-defined wireless sensor networks are emerging as a transformative technology for industrial, research, and IoT applications leveraging their control-data plane separation. This paper analyzes routing protocols, emphasizing their advantages, limitations, and QoS-impacting factors (e.g., latency, bandwidth, among others). Through a systematic review, we identify innovative solutions to enhance QoS in SDWSNs, addressing challenges like energy efficiency and dynamic adaptability. Previous results demonstrate that centralized routing and machine learning-based algorithms significantly improve reliability in critical applications.
Authors - Manpreet Kour, Naman Jain, Neeraj Gupta, Geetanjali Bhola Abstract - Waste recycling is important both in the global economy and in the global climate as a whole. As a result, classification of recyclable waste has become a critical goal for humanity, and deep learning models have important potential to fulfill this task. In this study, six advanced folding network models of neural networks - EfficientNetV2L, EfficientNetB1, EfficientNetB0, MobileNetV2, ResNet50, and VGG16, were compared for the effects of the garbage classification task. The results show that EfficienctNetB0 gave better performance than the other models. Furthermore, data augmentation techniques were used to improve classification accuracy, as data records contained a limited number of samples. Notably, MobileNetV2 not only achieved competitive accuracy, but also became a green choice for its low carbon emissions.
