Authors - Sreenath M. V., Abhigna Suresh Babu, Addanki Naga Sai Greeshmitha, C. R. Ananya, Lakshmi M., Mohan S. G. Abstract - Conventional recipe formats interrupt cooking workflows by requiring repeated attention shifts to external devices. This paper presents Beyond the Cookbook, a Mixed Reality (MR) cooking assistant developed for Meta Quest headsets. The system delivers spatially anchored, context-aware instructions using persistent holographic overlays, synchronized narration, and multimodal interaction including voice commands, controller input, and hand-tracking gestures. By integrating passthrough MR and spatial mapping, the assistant enables hands-free and hygienic guidance directly within the user’s kitchen environment. A usability study with twenty-one participants demonstrates high interaction reliability, instructional clarity, and user confidence. The results validate the feasibility of MR-based procedural learning support in domestic settings.
Authors - Dinesh O. Shirsath, Swati V.Sankpal Abstract - This paper presents a hybrid denoising pipeline for multi-channel electrocardiogram (ECG) recordings. First, blind source separation (BSS) isolates putative sources (cardiac, motion, muscle, baseline drift). Second, each separated component is represented sparsely in a suitable transform or learned dictionary; small / noise-dominated coefficients are attenuated and the component reconstructed. Finally, recombination yields a denoised ECG that preserves waveform morphology while suppressing compound, nonstationary noise. The paper describes the mathematical model, algorithmic steps, implementation tips, evaluation metrics, and practical considerations for deployment.
Authors - Aarya Sagar Sonawane, Rutuja Rajendra Thorwat, Shravani Rajeev Deshpande, A. R. Bankar Abstract - A significant security issue facing organizations is insider threats since one has access to privileged information and the behavior of users keeps evolving. Current solutions can be un-explainable, unable to manage new behavior patterns, generate high false positives, and un privacy friendly because of centralized data analysis. To solve these problems, this paper presents EXPLAIN-ITD, an explainable, adaptive and privacy-aware artificial intelligence system to detect insider threats. The framework is an integration of multi-modal data fusion, dual memory continuous learning, explainable risk scoring, human feedback in the loop and federated learning and differential privacy. As the exper imental findings have demonstrated, EXPLAIN-ITD has a better level of accuracy in detection, a lower level of false alarms and better interpreta bility than the current approaches.
Authors - Kamalakar S, Anjan Babu G, Ravi Kumar G Abstract - Artificial intelligence has become an important tool for addressing environmental challenges because it can analyze large datasets, detect patterns, and support accurate predictions. As climate change increases pressure on natural and built environments, organizations adopt AI to improve monitoring, optimize resource use, and inform sustainability decisions, though research remains fragmented. This review examines studies from 2020 to 2025 and assesses how AI is applied in renewable energy, water management, agriculture, waste management and the circular economy, and environmental health and public safety. A major objective of this synthesis is to highlight commonly employed functions by researchers and practitioners such as forecasting, anomaly detection, and operational optimization, alongside emerging model frameworks that strengthen environmental management. While AI offers meaningful benefits, it also presents challenges related to governance, transparency, and the energy demands of large scale models. This review consolidates developments and identifies priorities for future research.
Authors - Anil Kumar Bandani, Anupama Bollampally, Ramesh Deshpande B Saritha, P Rajesh Abstract - Transformer-based models in modern applications struggle with continual learning due to catastrophic forgetting. This paper presents Lapis Whale, a framework that incorporates a Selective Replay Utilization Mechanism (SERUM) to help a model retain previously learned knowledge while adapting to new tasks. The approach leverages a memory buffer to replay representative samples from earlier tasks during training. Experiments on the CIFAR-100 dataset show improved accuracy retention and reduced forgetting compared to standard fine-tuning methods. The framework is computationally efficient and well-suited for real-world adaptive AI systems.
Authors - Suman Kumar Mandal, Wendrila Biswas, Jaydev Mishra Abstract - Glaucoma is an optic neuropathy that is progressive and one of the most common causes of permanent blindness in the world. The retinal fundus images used to diagnose the condition are still time-consuming and highly reliant on the clinical expertise to detect the condition early, before the loss of vision becomes severe. In this experiment, we suggest a deep learning model that will use the ResNet50 architecture to identify retinal fundus images as belonging to one of two categories: Referable Glaucoma (RG) and Non-Referable Glaucoma (NRG). ResNet50 has been selected because it has good feature ex-traction (residual learning and deep convolutional learning). The standard performance measures were used to assess the trained model, such as accuracy, precision, recall, F1-score, and area under the ROC curve. The experimental findings indicate that the suggested approach yields consistent and accurate classification of RG and NRG cases, and it can be used to assist the ophthalmologist in clinical decision-making. The paper demonstrates how deep learning models could assist in further development of early glaucoma detection and mass screening, which, in their turn, can contribute to better patient outcomes and prevention of blindness before its onset.
Authors - S. Jayaraj, G. Anjan Babu, Krishnamurthy Kavitha Abstract - As neurodegenerative diseases like Huntington’s become a global health priority, the difficulty of early and accurate radiological diagnosis remains a significant hurdle. While Deep Learning, predominantly CNNs (Convolutional Neural Networks), offers a clarification for medical image classification, performance is often hindered by the inadequacy of high-grade datasets. This research addresses these limitations by proposing an ensemble deep learning model that integrates ResNet, MobileNet, and VGG16 architectures. By combining these networks, the study achieves enhanced robustness and superior classification accuracy compared to standalone models. This automated framework serves as a vital clinical support tool, enabling faster interventions, improved treatment planning, and a reduction in the global burden of neurodegenerative disorders [10,12].
Authors - Abhijit Dnyaneshwar Jadhav, Prashant G. Ahire, Madhuri Hiwale Abstract - A significant security issue facing organizations is insider threats since one has access to privileged information and the behavior of users keeps evolving. Current solutions can be un-explainable, unable to manage new behavior patterns, generate high false positives, and un privacy friendly because of centralized data analysis. To solve these problems, this paper presents EXPLAIN-ITD, an explainable, adaptive and privacy-aware artificial intelligence system to detect insider threats. The framework is an integration of multi-modal data fusion, dual memory continuous learning, explainable risk scoring, human feedback in the loop and federated learning and differential privacy. As the exper imental findings have demonstrated, EXPLAIN-ITD has a better level of accuracy in detection, a lower level of false alarms and better interpreta bility than the current approaches.
Authors - Tirupathi Rao Dockara, Pradeep Rajagopal Kirthivasan Abstract - Healthcare data scarcity poses significant challenges for machine learning applications in clinical settings, particularly for conditions with limited patient populations. This paper presents a novel quantumenhanced data augmentation framework that addresses this challenge through a three-pillar architecture: Quantum Random Number Generation (QRNG) for true randomness, Statistical AI for intelligent parameter optimization, and Generative AI for clinical interpretability. Our implementation utilizes Bell state quantum circuits to generate genuinely random perturbations, ensuring higher entropy than classical pseudorandom methods. The framework incorporates medical domain knowledge through constraint-aware augmentation, maintaining clinical validity while generating synthetic patient records. Experimental evaluation on the Pima Indians Diabetes dataset (768 samples, 8 features) demonstrates that our quantum-enhanced approach achieves 100% medical constraint compliance while generating high-quality synthetic data. The system provides both command-line and web interfaces, with automatic fallback to classical methods when quantum resources are unavailable. Our contributions include: the first practical application of quantum computing to healthcare data augmentation, an AI-driven optimization system that automatically determines augmentation parameters, integration with large language models for non-technical summarization of validation reports, and a production-ready implementation with comprehensive validation mechanisms. The framework represents a significant advancement in synthetic medical data generation, offering a scalable solution for addressing data scarcity in healthcare AI applications.
Authors - Jyotiprakash Mishra, Sanjay K. Sahay, Swati Mishra, Aman Pathak Abstract - Memory encryption is a key security requirement for modern computing systems, addressing vulnerabilities between CPUs and main memory. Traditional storage encryption is insufficient for protecting volatile data in RAM, which remains exposed to bus sniffing, cold boot attacks, and side-channel exploits. This paper therefore systematically reviews memory encryption techniques focused on hardware-based solutions like Intel Total Memory Encryption (TME), Multi-Key TME, and AMD Secure Memory Encryption, which provide robust protection while minimising performance overhead. The paper also explores integrity protection via Merkle trees and side-channel countermeasures against Differential Power Analysis and Simple Power Analysis attacks. Additionally, granular memory encryption methods for multi-tenant environments are discussed, highlighting their role in isolating sensitive data across security domains. By examining security guarantees and performance trade-offs, we emphasise the necessity of efficient memory encryption to safeguard against evolving threats targeting the CPU-memory interface, providing hardware engineers a foundation for ensuring data confidentiality and integrity.
