Authors - Nimisha K, Sridharan G, Kathiresh kumar K, Lohit S, Shyam Ganesh K Abstract - The rapid growth of sensitive data requires backup systems that are both storage-efficient and risk-aware. Traditional backup approaches rely on static policies that ignore temporal changes, data sensitivity, and redundancy, leading to inefficient storage use and higher risk exposure. This work proposes a risk-adaptive backup optimization framework integrating temporal modelling, sensitivity-aware deduplication, and online learning. The system reconstructs data evolution using intrinsic timestamps and quantifies data criticality through continuous sensitivity scoring. A unified risk model combines sensitivity, change intensity, and exposure over time to determine backup urgency. An online rein forcement learning agent dynamically optimizes backup decisions based on evolving data patterns. The framework applies secure, sensitivity-based dedupli cation to reduce redundancy while preserving privacy. Operating in a read-only, metadata-driven manner, it ensures compliance with strict data governance re quirements. By decoupling decision logic from storage, the system supports hy brid cloud environments. Experimental results show reduced storage costs and controlled risk, demonstrating its effectiveness for scalable, intelligent data pro tection.
Authors - Anirudh P, Nimisha K, Princy P Abstract - As technology advances, circuit complexity increases, integrated cir cuits become more prone to defects during manufacturing and operation. Conse quently, in order to ensure reliable operation, effective testing and stability eval uation of memory cells are essential. Static random-access memory plays a major role in modern digital systems due to its high-speed data access and efficient per formance. However, its reliable functioning is strongly influenced by device level parameters and supply voltage variations. In critical applications, even single fault occurrence may pose serious reliability issues, highlighting the need for ef ficient test methods. Extensive research has been carried out to investigate the static noise margin of SRAM cells. However, the influence of multiple defects has received relatively limited attention in existing literature. This study empha sizes the analysis of multiple defects because their occurrence becomes more fre quent in nano-meter technology regimes. Moreover, these defects can cause sig nificant fault behavior, potentially reducing the stability and reliability of SRAM cells. Multiple defects (Df3-Df3c) and (Df4-Df4c) are selected for analysis as they produce strong fault effects as they occur in the power supply and ground paths of the SRAM cell, which are critical for proper circuit operation. Any dis turbance along these conduction paths alters the effective operating voltage of the cross-coupled inverters and consequently affect the drive capability of the associated transistors. Moreover, the behavior of these defects is examined under various temperature conditions, supply voltages, and process corners in order to assess their overall effect on SRAM cell stability.
Authors - Sunil Jagannath Panchal, Gajanan Madhavrao Malwatkar Abstract - This research deals with the persistent challenges of document man agement in higher education institutions which focuses on the development of a digital support tool for Mariano Marcos State University (MMSU). Traditional paper-based systems and fragmented repositories often result in inefficiencies, duplication of work, and risks of data loss. The project adopted the Agile Devel opment methodology with emphasis on flexibility, collaboration, and iterative improvement. The d-T.R.A.I.L. system was built using JavaScript, PHP Laravel, HighCharts, and MySQL, integrating features such as tagging, repository man agement, granular access control, and collaborative modules like Teams. These functionalities were designed to streamline document organization, retrieval, and secure sharing across diverse academic and administrative units of the Univer sity. A User Acceptance Test (UAT) was conducted involving 70 participants from different MMSU offices that utilizes a Likert scale to measure satisfaction. Re sults yielded an overall mean score of 4.36 which was interpreted as Very Satis factory. High ratings were recorded for productivity, user-friendliness, and doc ument organization, while scalability received the lowest score which indicates an area for future enhancement of the system. The findings demonstrate that the system effectively improves workflow efficiency, accessibility, and accountabil ity, while aligning with national digital transformation policies.
Authors - Hileni Ihambo, Fungai Bhunu Shava, Gabriel Tuhafeni Nhinda Abstract - Fine-tuning large language models remains costly, and Parameter- Efficient Fine-Tuning (PEFT) techniques have emerged to make this process feasible on limited hardware. Among them, IA3 stands out for its extreme simplicity—it tunes only element-wise scaling vectors—but this design restricts the model to re-weighting features it already knows; it cannot form new ones. In this paper, we present SAMA (Spectral- Aware Minimal Adaptation), an extension of IA3 that introduces a single rank-1 update whose direction is derived from the pre-trained weights through Singular Value Decomposition. Each adapted layer gains only 4d extra parameters (3,072 for d=768), which is roughly one quarter of what LoRA requires at rank 8. We benchmark SAMA against five baselines—LoRA, DoRA, AdaLoRA, QLoRA, and IA3—across BERT, GPT-2, and Flan-T5 on twelve diverse NLP tasks under a low-resource constraint of 1,000 training samples per task. On the decoder-only GPT- 2, SAMA lowers perplexity by 26–34% relative to IA3 on both WikiText- 2 and Penn Treebank. On BERT’s RTE task, SAMA reaches 53.7% accuracy, surpassing IA3 (47.2%) and LoRA (52.6%) despite using 63% fewer trainable parameters than LoRA. We investigate this architecture dependence in detail and distil practical guidelines to help practitioners choose the right PEFT method for their setting.
Authors - M SANTHIYA, V KALAICHELVI Abstract - The wide use of machine learning in the field of medical imaging has caused concern with regard to patient information security, especially when mod els are being trained over multiple health care systems in a distributed manner. Centralized learning requires transferring raw patient data to a central server where there is an extreme risk of data breach and unauthorized access to patients' personal information. Violations of health care regulations (HIPAA and GDPR) can occur in a centralized system because of the transfer of patients' data. Feder ated Learning (FL) addresses these issues by allowing collaborative model de velopment on individual client devices. Therefore, the sensitive patient data will remain at its source institution. This paper provides a thorough comparative study of centralized learning and federated learning methods for detecting pneumonia utilizing chest X-rays from the publicly available Kaggle Chest X-Ray Pneumo nia dataset. Three architecture types (Support Vector Machine (SVM), Convolu tional Neural Network (CNN) and Long Short-Term Memory (LSTM)) were tested in both centralized and federated environments utilizing the FedAvg ag gregation method. Only the model weights were shared between the clients and the central server; therefore, patient data was maintained private through the en tire model training process. Experimental results demonstrated that federated learning produced superior performance than centralized learning for all three architectures (81.1%, 84.6%, and 82.7% for SVM, CNN and LSTM respec tively). The performance metrics for centralized learning were 76.6%, 76.3%, and 81.6%. This superior performance of FL is attributed to the inherent regular ization effect of local class-balancing within the federated clients that reduces the inherent class imbalance in the dataset. Overall, our research demonstrates that FL is not only a viable privacy-preserving solution to centralized training but offers improved generalization in the medical imaging domain with imbalanced classes and is a suitable solution for application in distributed health care envi ronments.
Authors - Vishruth B. Gowda, Sowmya T, Shreyas K, Megha J, Shreenidhi B S, Pranav Srinivas Abstract - Public administrations generate extensive administrative data through routine governance processes yet it is weakly based on verifiable evidence. This paper introduces a human-centric policy intelligence system based on execution-level administrative data for provision of accountable and evidence-based policy-making. The framework brings together governance-conscious data ingestion, cryptographic hash-based verification including permissioned blockchain systems to control the integrity of data, cross-domain data harmonisation to overcome administrative silos, and explainable machine learning models to create interpretable supporting insights. The framework is specifically meant as a human-in-the-loop system, maximizing policy foresight, administrative discretion, and accountability to the law. The validation with actual Mahatma Gandhi National Rural Employment Guarantee Act administrative data of the year 2022–2023 proves that the framework can be used to stress the implementation issues and regional inequalities without computerising policy-related decisions. The suggested solution is lightweight, scaled down to fit in the existing open-sector digital infrastructure.
Authors - Aprna Tripathi, Akhilesh Kumar Sharma, Avisikta Pal, Srikanth Prabhu, Ramakrishna Mundugar, Reet Ginotra Abstract - This paper presents a novel approach to identifying translation errors in Thai-English machine translation through the comparative analysis of multiple automatic evaluation metrics. Using a rank deviation methodology, we evaluate 350 Thai-English translations produced by seven contemporary systems provid ing online translations — including dedicated Machine Translation systems and large language models — across nine automatic evaluation metrics. By ranking translations within each metric and comparing individual metric rankings against the mean average rank, we identify translations that receive solitary punishment from a single metric, isolating these as candidates for manual error analysis. Our results demonstrate that individual metrics exhibit distinct sensitivity to specific error types, and that surface-level metrics retain diagnostic value along side advanced neural metrics. Neural metrics effectively identify meaning and adequacy errors, but surface-level metrics uniquely identify morphological vari ation, word order errors, preposition choice, and number formatting issues that neural metrics fail to penalize. The diversity of metric sensitivity is therefore an asset rather than an inconvenience, enabling a more complete characterization of translation error than any single metric can provide. This research supports the development of high-quality training data for MT fine-tuning by identifying the specific error types that individual metrics can and cannot detect and provides a repeatable diagnostic methodology applicable to other language pairs.
Authors - Bobby A. Eclarin, Mark Justine S. Cudapas Abstract - This research deals with the persistent challenges of document man agement in higher education institutions which focuses on the development of a digital support tool for Mariano Marcos State University (MMSU). Traditional paper-based systems and fragmented repositories often result in inefficiencies, duplication of work, and risks of data loss. The project adopted the Agile Devel opment methodology with emphasis on flexibility, collaboration, and iterative improvement. The d-T.R.A.I.L. system was built using JavaScript, PHP Laravel, HighCharts, and MySQL, integrating features such as tagging, repository man agement, granular access control, and collaborative modules like Teams. These functionalities were designed to streamline document organization, retrieval, and secure sharing across diverse academic and administrative units of the Univer sity. A User Acceptance Test (UAT) was conducted involving 70 participants from different MMSU offices that utilizes a Likert scale to measure satisfaction. Re sults yielded an overall mean score of 4.36 which was interpreted as Very Satis factory. High ratings were recorded for productivity, user-friendliness, and doc ument organization, while scalability received the lowest score which indicates an area for future enhancement of the system. The findings demonstrate that the system effectively improves workflow efficiency, accessibility, and accountabil ity, while aligning with national digital transformation policies.
Authors - Gauthaman S P, Paneer Thanu Swaroop C, Bagavathi Sivakumar P, Anantha Narayanan V Abstract - Psoriasis is a long-term inflammatory skin disease commonly identi fied by red plaques, scaling, and abnormal thickening of the epidermis. Reliable evaluation of disease severity is important for determining appropriate treatment options and for tracking patient response to therapy. In clinical practice, severity is often assessed using the Psoriasis Area and Severity Index (PASI). Although widely adopted, this method largely depends on visual examination and clinician judgment, which may lead to inconsistencies and observer-dependent variations. Recent developments in artificial intelligence and non-invasive dermatological imaging technologies provide opportunities for more objective and automated assessment of skin disorders. In this study, a novel framework is proposed for psoriasis severity evaluation that integrates skin biomechanical characteristics with deep hybrid learning mod els. Biomechanical attributes of the skin, including elasticity, stiffness, and vis coelastic behavior, are obtained through non-invasive measurement techniques and combined with visual information derived from dermatological images. The proposed system employs a hybrid deep learning architecture that incorporates convolutional neural networks (CNN) for image feature extraction along with machine learning classifiers for severity prediction. By jointly analyzing biome chanical and visual features, the framework aims to enhance the precision, con sistency, and reproducibility of psoriasis severity assessment. Experimental anal ysis indicates that the inclusion of biomechanical biomarkers alongside deep learning significantly improves prediction performance when compared with tra ditional image-based models. The proposed approach can support dermatologists in clinical decision-making and may also facilitate applications in tele-dermatol ogy and personalized disease monitoring.
Authors - Vijayanirmala Baddala, Jolakula Asoka Smitha, Bichagal Shadaksharappa Abstract - Accurate State-of-Charge (SoC) estimation is critical for ensuring the reliability, safety, and operational efficiency of lithium-ion batteries in electric vehicles and energy storage systems. While data-driven models offer high precision, centralized approaches are increasingly limited by data privacy concerns, high communi- cation overhead, and poor scalability. This paper addresses these challenges by proposing a comprehensive deep learning and federated learning (FL) frame- work for decentralized SoC prediction using the OSF battery dataset. We use four LSTM architectures: Stacked LSTM, Bidirectional LSTM, Attention-based LSTM, and Stateful LSTM, which are integrated into a federated model to sys- tematically evaluate their performance. These include FedAvg, FedProx, and adaptive methods such as FedAdam and FedYogi. To our knowledge, this is the first study to evaluate these architectures in the context of a federated battery management system (BMS). Results show that The comparative analysis inves- tigates the interplay between model complexity and federated optimization, with a specific focus on predictive accuracy, convergence behavior, and robustness to non-IID data distributions stemming from heterogeneous battery capacities and usage patterns. By benchmarking these combinations, this research identifies optimal strategies for implementing privacy-preserving, communication-efficient, and scalable Battery Management Systems (BMS) at the edge.
