Authors - Chinmayee Padhy, Himansu Mohan Padhy, Pranati Mishra, Nabin Kumar Nag Abstract - Establishing an institution's excellence requires measuring their innovation and research accomplishments. Tracking, verifying, and evaluating innovation and research output in an efficient manner is currently constrained by a lack of efficient reporting systems and disorganized methods of obtaining the necessary data. The creation of InnovateHub, a web-based, secure, scalable, and cloud-based platform that provides a centralized system for analysing, managing, and visualizing research and innovation throughout the world's education sector. The InnovateHub provides a central location where a single point of access can be used to collect and process all types of innovation and research information via an effective system; an interactive dashboard and analytical visualisation allows users easy access to relevant information. InnovateHub provides a role and permissions-based access control mechanism to preserve the data privacy and accountability of Administrators, Faculty, and Students. InnovateHub also supports Multi Factor Authentication (MFA) using JSON Web Tokens (JWT) for multiple layers of security and verification of user identity as well as One Time Passcode (OTP) confirmed through email, and uses cryptographic hashing to provide a form of security for storing documents and provides a biometric face-based verification system (i.e., facial recognition) to authenticate a user during critical submission phases. Automated certificate generation and contribution recognition mechanisms at InnovateHub provide additional visibility into, and motivation for, users' contributions to the platform. Utilizing the MERN Stack and AWS for Hosting of MERN Stack: Utilizing the MERN Stack (MongoDB, Express, React, Node.js) & AWS to Host a MERN Stack Application Innovative Hosting Solutions by AWS Include Amazon EC2 Instances to Host Both the Application Back End as Well as Application Front End Services and Amazon S3 for Secure and Scalable Storage of Research Document & Certificate Generation. Experimental Deployment Indicates Reliable Operation, High Availability and Secure Handling of Data During Real Time Utilization within the Loss Prevention Environment. Innovate Hub Provides Real Time Analytics, Secure Verification & Cloud Scaleability for Institutional Research Governance and the Development of a Data Driven Platform of Continuous Innovation and Growth through the Development of a Data Driven Innovation Platform.
Authors - Pranav Rao, Pranav S Acharya, Rishika Nayana Naarayan, Shreya M Hegde, Pavan A C Abstract - The rapid expansion of cloud computing, Internet of Things (IoT), 5G networks, and distributed enterprise infrastructures has significantly in creased the complexity and attack surface of modern networks. Traditional net work security mechanisms—primarily based on static rules and signature-based detection—are increasingly ineffective against advanced persistent threats (APTs), zero-day exploits, polymorphic malware, and encrypted attack chan nels. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative technologies capable of enabling adaptive, predictive, and au tonomous cybersecurity systems. This paper presents a comprehensive technical framework for AI-driven network security. We propose a hybrid architecture in tegrating supervised classification, unsupervised anomaly detection, and deep learning-based behavioral modeling. Mathematical formulations for intrusion detection, anomaly detection, and adversarial robustness are provided. The framework is evaluated using benchmark intrusion detection datasets, and per formance is analyzed using standard metrics including accuracy, precision, re call, F1-score, and ROC-AUC. Results demonstrate that AI-driven models sig nificantly outperform traditional signature-based approaches in detecting zero day and evasive attacks. The paper concludes by discussing adversarial machine learning risks and future directions toward autonomous and self-healing net work security ecosystems.
Authors - Rosa Cristina Pesantez, Estevan Gomez-Torres, Cesar Adrian Guayasamin Abstract - The vast implementation of cloud computing has uplifted the modern IT practices by improving scalability, flexibility, and budget efficiency. In contrast, there has been an increase in energy consumption, which results in carbon emissions. This happens because of overusage, overconsumption, overprovisioning, unused capacity, and inefficient data center management. These days, data centers act as the sole contributor to global greenhouse gas (GHG) emissions; therefore, sustainable cloud operations are essential in addressing this challenge. GreenOps, or green operations, defines the cloud deployment and operational practices that take place but also considers the environmental impact; it depicts energy-efficient infrastructure design, optimized resource usage, virtualization, and the integration of renewable energy resources. This survey presents a summary of green cloud computing, including the current trends, challenges, energy-aware scheduling algorithms, and optimization techniques for obtaining energy-efficient cloud deployment.
Authors - Govind Sambare, Sarika Deokate, Saurabh Dhakite, Sahil Ambokar, Gargi Barve Abstract - Static perimeter-based security architectures are now inef fective in the current threat scenario. The ability of attackers to obtain legitimate credentials and the presence of zero-day exploits often cause real-time breaches of the network perimeter. An area of concern is the real-time monitoring of these systems. In the current scenario, security monitoring is performed in a segregated manner, where network analysts analyze time-stamped network logs and identity analysts analyze time stamped login attempts, without cross-referencing in real time between these two domains. The proposed solution is a fusion platform capable of ingestion of raw network transport data and real-time human element monitoring data. This is achieved through the integration of two dif ferent threat detection mechanisms using a FastAPI backend. The first threat detection system will be the Network Threat Detector (NTD), im plemented in Python and using the Scapy library to parse deep packet data in real time for flow analysis. The second threat detection system will be a JavaScript tracker designed for monitoring digital behavioral indicators and calculating real-time metrics such as mouse velocities, ac celerations, kinematic jerk, and typing speeds. Real-time monitoring will be achieved through a machine learning framework with three different modules for inferring user intent using the Random Forest algorithm, detecting anomalous statistical patterns using the Isolation Forest algo rithm, and detecting malicious plaintext syntax using Logistic Regres sion. The system has been tested in a lab scenario and has been able to classify user session states into four different states: Engaged, Con fused, Frustrated and Suspicious with accuracy exceeding 95%. These digital behavioral indicators will be fed into the Network Transport Data (NTD), allowing the computation of a real-time risk score.
Authors - Duc Thinh Nguyen, Diem Huyen Nguyen Ngoc, Khoa Tran Thi-Minh Abstract - In the present-day context, presentations and computer-based interac tion play a crucial role in various domains, particularly in education and business. Traditionally, users have to rely on physical devices such as mouses, keyboards, or laser. Although these devices meet the basic requirements, they still reveal many limitations regarding mobility, continuity, and dependence on battery life. To address these limitations, hand gesture-based presentation control systems have emerged as a promising solution due to their intuitive, natural, and engaging interaction style. This paper proposes a touchless system that enables users to control common desktop operations as well as presentations in a natural manner using hand gestures captured via a standard webcam. The proposed system lev erages OpenCV for real-time video acquisition and preprocessing, while Medi aPipe framework is employed for hand tracking and landmark extraction. From the experiments, our system can process in real-time with the accuracy of approx imately 92%. As a result, users can seamlessly control slides, use virtual mouse operations, annotate presentation content, and engage with the audience in a more interactive and natural way without physical contact.
Authors - Deepali Lokare, Pankaj Chandre, Prashant Dhotre Abstract - The rapid expansion of digital services has significantly increased the collection and processing of personal data through online platforms such as e-commerce systems, social media applications, and digital payment services. To regulate the use of personal information, governments worldwide have introduced data protection regulations such as the General Data Protection Regulation (GDPR), the Digital Personal Data Protection Act (DPDPA), and the California Consumer Privacy Act (CCPA). Organizations publish privacy policies to inform users about their data practices; however, these policies are often lengthy, complex, and difficult for users to understand. Consequently, users frequently accept privacy policies without fully reviewing how their personal data is collected, processed, and shared. Recent research has explored automated approaches for privacy policy analysis using artificial intelligence techniques, including machine learning, natural language processing, and large language models. Retrieval-Augmented Generation (RAG) has further enhanced compliance evaluation by linking policy statements with relevant regulatory clauses. Despite these advancements, challenges remain, such as the lack of standardised datasets, limited explainability of AI decisions, dependence on prompt design, and insufficient validation with regulatory experts. This paper discusses future research directions in AI-driven privacy policy compliance analysis and highlights emerging opportunities for improving regulatory compliance assessment, user privacy protection, and transparent privacy governance in digital ecosystems.
Authors - Samiksha M, Sharanya G S, Shrina Anahosur, Surabhi K C, Surabhi Narayan Abstract - Multi-angle image synthesis is highly important when it comes to the generation of 3D scenes. But the current methods are either ex pensive in terms of computational costs or lack photorealism in their outputs. We propose a novel sketch and text based multiview image generation approach that solves the above-mentioned problems by mak ing use of multimodal diffusion models efficiently. Our pipeline utilises DreamShaper v8 for converting the input sketch and text into a pho torealistic 2D image and then passes this 2D image into a fine-tuned Zero123plus model for the final generation of consistent multiview im ages, showing a 43.69% improvement in the overall perceptual quality compared to baseline sketch-to-multiview models. Moreover, our pipeline shows flexibility in scalability by generating anywhere from 6 to 64 consis tent multiview images according to the requirements of the downstream tasks. We demonstrate the success of our pipeline through extensive ex periments conducted using voxel-based grid approaches and Neural Ra diance Fields (NeRF). Our pipeline greatly reduces computational costs, all while maintaining photorealism in the outputs, confirming the poten tial of sketch and text based multimodal conditioning as an intuitive and efficient paradigm for controlled 3D content generation.
Authors - Balasubramanian M, Arasu Prabhu V S, Nalini Subramanian Abstract - Privilege Escalation is a major issue for securing Linux sys tems. When a user gains unauthorized root access he has the ability to access all system resources and manipulate them at will. In the past, Linux has used Static Access Control Policies and User Space Monitoring Tools to secure system access. However, these methods provide little in sight into how the kernel is modifying users credentials when permissions are changed. In this paper we propose a Kernel-Level solution to detect and prevent unauthorized privilege escalations. This detection/ preven tion occurs in real time via a Credential Transition Monitoring Mecha nism within the kernel layer, which prevents the elevation of privileges by illegal means. To create the functionality necessary for the above, a Linux Kernel Module (LKM) was created which utilizes kprobes to in tercept calls to the commit creds() function, which is used to update a processes credentials in the kernel. To evaluate if the privilege escalation being requested is legitimate or malicious, the LKM contains a Policy Based Evaluation Mechanism which evaluates each request to modify a process’s credentials. We tested our proposed solution using a con trolled test environment composed of a Virtual Machine (VM) running the Ubuntu Operating System. We ran two types of tests, first were Le gitimate Administrative Operations utilizing the ”sudo” utility, second were Simulated Privilege Escalation Attacks based upon SetUID Vul nerabilities. Our results show that the system effectively detected and blocked malicious privilege escalations, while providing minimal over head to normal system operation.
Authors - Noel Milliones, Vicente Pitogo, Mark Phil Pacot Abstract - The sensitive information in the healthcare industry along with the increasing phe nomenon of the use of intelligent health-related devices makes it a very difficult task to ensure the privacy of patients as well as carry out precise analysis. The centralized methodology in cur-rent machine learning models requires the exchange of raw information of patients from different healthcare institutions and health related devices to the centralized computer system through the network. However, due to the privacy issues and network traffic issues in this methodology, the proposal proposes the development of a privacy-preserving health analytics platform. Here in this proposed methodology, every healthcare center as well as health-related device has its own local machine learning model without transferring even a single piece of information outside. However, the models also employ disease-specific models including CNN heart diseases models of 95 percent accuracy, Gradient Boosting Classifier Diabetes models of 93 percent accuracy models, along with SVM models of liver diseases along with 96 percent GridSearch models. Each edge device carries out the data preprocessing for the local environment, as well as the processes of model training and the transmission of secure updates, in such a way that the sensitive patient data has never left the environment. The platform presented proves the idea that edge computing and collaborative learning can lead to scalable and secure healthcare analytics with high predictive performance.
Authors - Etambuyu Akufuna, Mayumbo Nyirenda, Ruth Wahila, Marjorie kabinga Makukula Abstract - As the primary cause of death worldwide, cardiovascular disease (CVD) necessitates accurate early detection methods. We provide a machine learning approach for predicting heart illness using clinical health data that is enabled by the Internet of Things. An SVM classifier that was trained using 14 Cleveland Heart The disease dataset separates patients at high risk from those in good health. Preprocessing, feature standardisation, and GridSearch Cross-Validation hyperparameter optimisation are all included in the workflow. The model outperforms a number of benchmark techniques in the literature with an accuracy of 93.33% and an AUC of 0.97. A scalable and comprehensible basis for IoT-based clinical decision assistance is confirmed by comparative outcomes.
Authors - Hemamalini Siranjeevi, Swaminathan Venkatraman, Dharshini V, Gayathri A, Sushma Sri R Abstract - Urban environments generate massive video data from surveillance and mobile sensors, necessitating efficient and intelligent summarization for smart city and transportation systems. This paper proposes a multimodal video summarization framework that moves beyond object-centric analysis toward high-level urban scene understanding. Unlike traditional methods that rely on low-level visual features or isolated object detection, the proposed approach captures contextual relationships and temporal continuity through a multi-stage pipeline. The system integrates multimodal perception, combining deep learning-based object detection, multi-object tracking, and acoustic analysis to preserve entity identities and environmental context. We employ relational inference and motion heuristics to model spatial and semantic interactions, which are then structured into a Dynamic Knowledge Graph (DKG) representing entities, interactions, and temporal events. A semantic synthesis module, powered by a transformer-based language model, generates concise, coherent, and semantically meaningful summaries. This architecture enables scalable, context-aware video summarization adaptable to real-world urban applications.
Authors - Nithin Gattappagari, Lakshmi Sagar S, Reddy Lokesh K, Banu Prakash N, Asritha A, Varalakshmi U, Karthik P, Praveen Kumar Rayani Abstract - Conventional one-time authentication cannot prevent session hijacking after login. This paper proposes a session-level impostor de tection framework based on Siamese learning over mouse dynamics for continuous authentication. The model combines statistical behavioral de scriptors with lightweight temporal modeling (Conv1D+GRU) to learn compact embeddings for open-set verification. It supports one-shot en rollment by comparing a query session against a single verified reference session and stores non-reversible embeddings instead of raw trajectories to improve privacy. We evaluate on Balabit and SAPiMouse under se vere class imbalance using balanced batching, semi-hard negative mining, and focal contrastive loss. The framework achieves AUROC 0.95/0.96, F1 0.80/0.85, and accuracy 0.92/0.93, with 46K trainable parameters and approximately 15ms inference time, indicating practical deployment potential.
Authors - Rishav Kumar Agrawal, Maharshi Bhowmick, Mir Abbas Hussain, Sachin, Vaishali Shinde Abstract - This paper presents a platform for scalable validation, visu alization, and explanation of synthetic tabular data in a rigorous and operationally practical workflow. The system integrates statistical test ing, dimensionality reduction, anomaly detection, and AI-assisted in terpretation into a single analysis pipeline. Through an insurance-data case study, we show that the platform can detect subtle distributional artifacts, support utility–privacy trade-off assessment, and provide in terpretable evidence that is difficult to obtain from isolated univariate checks. We conclude by discussing practical value, current limitations, and directions for future development.
Authors - Rowena Ocier Sibayan, Hazel C. Tagalog, Ronald S. Cordova Abstract - As digital marketing expands in Oman, many organizations struggle to transform large volumes of customer data into actionable insights. This study presents an AI-driven marketing intelligence framework designed for non-technical users, combining automated customer segmentation, sentiment analysis, and personalized recommendations. The framework employs an autoencoder-based feature extraction approach to capture key behavioral patterns, followed by K-Means clustering to define meaningful customer segments (Berahmand et al., 2024). A fine-tuned BERT model analyzes multilingual feedback in Arabic and English to assess customer sentiment (Manias et al., 2023). The framework was evaluated using 12 months of campaign data from 450 customers across multiple Omani businesses. Analysis revealed four distinct customer groups and an overall positive sentiment of +0.55. Controlled A/B experiments demonstrated that AI-guided campaigns outperformed traditional methods, increasing conversion rates by 27%, improving retention by 15%, and generating a threefold return on marketing spend. These results indicate that accessible AI tools can deliver measurable marketing benefits in emerging markets and provide a scalable solution for Gulf-region businesses.
Authors - Maria George Anthraper, Kusuma Sanjaykumar, Sinchana K C, V R, Badri Prasad Abstract - Post-quantum migration is increasingly constrained by time: deployed cryptographic mechanisms may need to be retired, hybridized, or re-keyed before effective security margins fall below asset-specific pol icy thresholds. This timing problem is complicated by uncertainty in clas sical hardware acceleration, algorithmic progress, implementation ero sion, and the arrival of cryptographically relevant quantum comput ers. This paper presents a compact probabilistic pipeline that translates evolving assumptions and evidence into decision-facing migration guid ance. The approach couples three layers: (i) a security-trajectory model that encodes expected margin erosion under scenario parameters, (ii) a latent-regime model that represents partially observed risk states and updates them as evidence changes, and (iii) an option-style timing layer that quantifies the diminishing value of delaying migration as thresholds approach. Outputs are conditional on stated assumptions and are in tended to be reported with sensitivity bands and lead-time constraints. In practice, the pipeline is intended to be re-run as assumptions and evidence evolve, preserving an auditable trail from scenario inputs to in termediate states and final decision artifacts. The primary deliverables are comparative rankings and conservative “start-by” windows under stated assumptions, rather than single predicted break dates.
Authors - Jayalakshmi D, N. Priya Abstract - Online product reviews play a key role in the success or failure of an e-commerce business. Often, online reviews from previous customers provide buyers with detailed advice about the product and help them decide before purchasing a product or service. However, some e-commerce products can be promoted or damaged by fraudsters who post fake reviews. Synthetic Reviews (SRs) have the capacity to deceive consumers, influence purchasing decisions, and lead to losses. Thus, SRs pose a significant risk to e-commerce companies and content creators, undermining consumer loyalty and brand reputation. Specifically, the development of AI-generated fake reviews has made them harder to detect, as they are very similar to human-written texts. This review paper presents a Deep Learning (DL)-based framework that offers comprehensive insight into fraud and synthetic review detection in an evolving e-commerce environment. This review paper discusses the importance of DL for detecting online product fake reviews in sentiment analysis using various approaches based on Graph Convolutional Network (GCN), Hierarchical Graph Attention Network (HGAN) Sentiment Majority Voting Classifiers (SMVC), Convolutional Neural Networks with Bidirectional Long Short-Term Memory Networks (CNN-Bi-LSTMs), and a proposed Optimized Bidirectional Encoder Representation Transformers (OBERT) model. This review paper focused on the importance of DL models, particularly the GCN, for effective identification of fake online reviews. This review paper proposed a DL algorithm for fake review detection in online products and demonstrated its practical application in a real-world scenario.
Authors - Miroslav Cech, Rastislav Roka Abstract - Private 5G networks require a reliable, high-capacity, and secure transport infrastructure, especially in industrial and critical applications. Free Space Optics is a promising solution enabling multi-gigabit transmissions with low latency and increased physical security. The article analyses the possibili ties of integrating FSO technology into Standalone Non-Public Network and Public Network Integrated Non-Public Network architectures and evaluates the role of FSO links as a transport or interconnection layer and their impact on la tency, reliability, and security for 5G services such as eMBB, URLLC, and mMTC. The article then summarizes current research trends, including the use of artificial intelligence and machine learning to optimize FSO-based transmission.
Authors - Tanmoy De, Vimal Kumar, Pratima Verma Abstract - The process of operating modern engineering companies is often compartmentalized due to the straightforward nature of the operations requirements that mani-fest themselves within the realm of the software creation and hardware manufacturing. The absence of integration between Agile practices and Waterfall lifecycles is a waste of administrative resources and delays time-to-market. A hybrid project management SaaS is offered in this project called Converge, which will target the integration of these areas without sacrificing the integrity of the data stored in digital code repositories and physical Bill of Materials (BoM). The adoption of Multi-Modal Documentation, Real-time State Synchronization and IoT-oriented Task Automation have their measures of efficiency of workflow, responsiveness of interface, and cross-domain data consistency. The most recent breakthroughs in Natural Language Processing (NLP) and Computer Vision are used to make the experience more practical; a custom AI pipeline based on the ResNet50 and LSTM networks are able to extract visual storyboards of technical video reports with an impressive F Score of 83.00% (with 79.20% Precision and 86.50% Recall), and Transformer based models (including BART) are able to generate structured textual summaries with the leading ROUGE-L score of 0.42. The system is anchored on a dynamic split-brain architecture to display coherent information in either Kanban boards or Gantt charts as the case arises. Status updates increase exponentially with integrated IoT triggers to computerize the execution of tasks via a direct hardware to software communication. The survey is based on the trade offs between the flexibility of UI, the complexity of the database schema, and the latency of the API to compare the old siloed tools to this new hybrid framework. The future of engineering management relies on new tendencies, such as Hybrid Machine Learning, to predictively allocate resources, cutting the error rates in estimating the effort by three times (MMRE to 0.32) with the help of such dominant historical measures of resources as Lines of Code (feature importance score of 0.73) and automated reporting of resource depend-ency. Finally, it is demonstrated that the suggested architecture with the support of a CNN optimized backend video storage, which will save 61.80% of the time at a small cost of 2.30% BDBR, will save about 60% of time on manual docu-mentation and synchronize assets in real-time with a latency less than 200ms (2 seconds).
Authors - Dennis A. Dizon, Gleen A. Dalaorao Abstract - Access to formal financial services remains limited in many develop ing regions, largely due to economic and infrastructural constraints. This study uses the ISO/IEC 25010 as the evaluation framework to present a software quality assessment of a lending automation system installed in a financial insti tution in Butuan City, Philippines. The evaluation focuses on five essential as pects of software quality: usability, reliability, functional suitability, perfor mance efficiency, and security. Usability surveys using SUS and UMUX-Lite, operational and performance testing, and an evaluation of security and data privacy compliance were used to gather empirical data. According to the results, the system achieved high performance with an average inference latency of 0.208 ms per record, uptime reliability of ≥99.5%, excellent usability with a mean SUS score of 82.5, and full compliance with data privacy regulations. Predictive analytics, specifically the Random Forest model with isotonic calibration, further enhanced the automated loan assessment’s interpretability and reliability. The system proved that it is appropriate for real-world applications and can encourage financial inclusion in resource-constrained environments, as it exceeded the intended benchmarks for each quality model. To guarantee the long-term adoption of lending automation technologies, the study emphasizes the significance of thorough software quality evaluation in addition to predictive accuracy.
Authors - Nita Dimble, Satish Narayanrav Gujar Abstract - The fabrication of components across various industries is accom plished through welding. Although welding has been practiced for more than a hundred years, defects may still occur during the welding process. Thus, indus trial standards require welded joints to be inspected and evaluated to ensure their quality and reliability. Conventional ultrasonic testing (UT) has long been widely used in industry for detecting and evaluating defects in weld specimens. Over the last few decades, advances in sensor technology and signal analysis techniques have significantly advanced ultrasonic testing methods. Advanced methods, such as Time Of Flight Diffraction (TOFD), are more likely to detect linear defects. However, one of the major challenges in applying TOFD to the inspection of austenitic stainless steel (ASS) weldments is noise in the signals. Various signal processing approaches have been developed to suppress such noise, each with its own advantages and limitations. In this work, the focus is placed on the applica tion of multi-level discrete wavelet transform (DWT) decompositions with ‘n’- order wavelet filters for de-noising ultrasonic TOFD A-scan signals. The results show that this approach achieves greater improvement in signal-to-noise ratio (SNR) while requiring less computational time.
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.
