Gongcheng Kexue Yu Jishu/Advanced Engineering Sciences (ISSN: 2096-3246) is a bi-monthly peer-reviewed international Journal. Gongcheng Kexue Yu Jishu/Advanced Engineering Sciences was originally formed in 1969 and the journal came under scopus by 2017 to now. The journal is published by editorial department of Journal of Sichuan University. We publish every scope of engineering, Mathematics, physics.
Harnessing Ensemble Deep Learning for Scalable and Precise Cloud Workload Prediction
Cloud computing platforms face a massive demand for resources, which is typically volatile and unpredictable, and require accurate and scalable prediction techniques to assure appropriate resource allocation and service continuity in such environments. Cloud workloads' increasing variability and complex nature render traditional resource management inefficient since traditional prediction models mostly use static parameters. In this study, a Transformer-GRU-BiLSTM, which is called an Ensemble model for Workload Prediction, is presented with the combination of TransGRU-BiLSTMNet (TGBNet) multiple DL models (Gated Recurrent Unit (GRU), Bi-directional LSTM (BiLSTM) and Transformer-based architectures) to handle the accurate and complex workload patterns. The weighted average and meta-learning methods are applied in ensemble models to enhance the generalization and stability of workload changes. Experimental results based on extensive empirical studies over real-world public and private cloud workload datasets show that our integrated ensemble model not only yields better prediction accuracy compared to a set of individual deep learning (DL) models and traditional statistical methods but also possesses better scalability and time complexity. The study is one of the first to propose ensemble deep learning (DL) for proactive resource management in cloud environments, enabling better workload distribution and reduced computational costs.
Yaddala Srinivasulu, Bobba Basaveswara Rao
BIOMETRIC AUTHENTICATION USING GENERATIVE AI TECHNIQUES
Biometric authentication systems have become essential in modern security applications due to their ability to provide reliable and user-specific identification using physiological and behavioral traits such as face, fingerprint, iris, palmprint, voice, and vein patterns. Recently, generative artificial intelligence (AI) techniques, particularly Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), diffusion models, and transformer-based architectures, have significantly transformed biometric authentication by improving biometric data synthesis, spoof detection, privacy preservation, and multimodal authentication. This survey presents a comprehensive review of generative AI techniques applied in biometric authentication systems, covering major research areas including biometric data augmentation, deepfake detection, privacy-preserving authentication, multimodal biometric fusion, liveness detection, and anti-spoofing mechanisms. The survey is from 2020-2026.The study analyzes recent advancements in GAN-based synthetic biometric generation, adversarial attack detection, explainable AI integration, blockchain-enabled privacy protection, and quantum-resistant biometric frameworks. Furthermore, a detailed classification framework and comparative literature analysis are presented to identify current methodologies, datasets, algorithms, strengths, and limitations of existing approaches. The survey also highlights critical research gaps related to scalability, cross-database generalization, adversarial robustness, computational complexity, explainability, and real-time deployment challenges. Finally, future research directions are discussed, emphasizing the development of secure, lightweight, privacy-aware, and robust generative AI-driven biometric authentication systems for next-generation intelligent security applications.
FAMILY BUSINESS SUCCESSION: THE CASE IN INDIA
ABSTRACT This study investigates the crucial determinants of success for Indian family businesses, considering aspects related to the family, the business itself, and the successors. The research focuses on family businesses, with 125 participants who are business owners, successors, or potential successors of family-owned SMEs. These respondents are from West and East India and represent enterprises that have been in operation for over 10 years. The analysis employs multiple regression, ANOVA, and descriptive statistics. The findings reveal that factors related to successors and the effectiveness of the succession process significantly influence the management succession in Indian family businesses. In contrast, family and business-related factors have a minor impact, as these are heavily influenced by the successor’s motivation to take over or be involved in the family business. The study provides valuable insights for family business owners on succession planning, communication, governance, decision-making, and financial transparency.
Dr. Frederick Sidney Correa, Amrita Bajpai, Sanjeev Kumar Singh, Adina Ambreen
IMPROVING COMPUTATIONAL EFFICIENCY IN FEDERATED LEARNING: APPLICATIONS IN MACHINE LEARNING AND DATA MINING
Federated Learning (FL) has emerged as a promising distributed machine learning paradigm that enables multiple devices to collaboratively train models without sharing raw data, thereby preserving privacy and enhancing data security. Despite its advantages, the practical deployment of FL is constrained by several computational challenges, including high communication overhead, intensive local computation, energy consumption, memory limitations, and statistical heterogeneity caused by non-IID data distributions. These issues are particularly critical in resource-constrained environments such as Internet of Things (IoT) devices, edge computing systems, and TinyML platforms.This survey presents a comprehensive review of recent advancements in improving computational efficiency in federated learning, with a focus on machine learning and data mining applications. The study analyzes 60 research articles published between 2022 and 2026 and examines key optimization techniques, including model compression through pruning, quantization, and knowledge distillation, feature selection and dimensionality reduction methods, gradient sparsification, clustered federated learning, and privacy-preserving mechanisms such as differential privacy and homomorphic encryption. The reviewed literature demonstrates that these approaches can significantly reduce communication costs, accelerate model convergence, and lower energy consumption while maintaining acceptable predictive performance. In many cases, communication overhead is reduced by 30–70%, with only minor accuracy degradation compared to centralized learning models.The survey further highlights the effectiveness of hybrid approaches that integrate clustering, compression, and sparsification strategies to address heterogeneity and scalability challenges. However, issues related to robustness, privacy-utility trade-offs, and cross-domain generalization remain open research problems. The findings emphasize the importance of computational efficiency for enabling scalable, practical, and sustainable federated learning systems in privacy-sensitive and resource-limited environments.
Lakkireddy Priyanka, Dr. B. Sateesh Kumar
STRUCTURAL BEHAVIOR OF FIBER REINFORCED CONCRETE ELEMENTS UNDER MONOTONIC STATIC LOADING: AN INVESTIGATION OF LOAD-DEFLECTION RESPONSE, CRACKING BEHAVIOR, STIFFNESS, AND FAILURE MODES
This study investigates the structural behavior of fiber reinforced concrete (FRC) elements subjected to monotonic static loading, with emphasis on load-deflection response, cracking characteristics, stiffness degradation, and failure mechanisms. An experimental program was conducted on reinforced concrete beams incorporating glass fibers (GF), polypropylene fibers (PP), and hybrid glass-polypropylene fiber combinations at various volume fractions (0.5%, 1.0%, 1.5%, and 2.0%). Twelve full-scale beam specimens (150 mm × 250 mm × 2800 mm) were fabricated and tested under four-point bending until failure. The study measured first cracking load, ultimate load capacity, mid-span deflections, crack patterns, crack width progression, stiffness evolution, ductility indices, and energy absorption capacity. Results indicate that fiber reinforcement significantly enhances structural performance: hybrid fiber specimens (1.5% GF + 0.5% PP) achieved 68% higher ultimate load capacity, 124% greater ductility, and 187% improved energy absorption compared to control specimens. The optimal fiber combination exhibited superior crack control with 47% reduction in maximum crack width and 63% increase in crack spacing. Post-cracking stiffness was enhanced by 54% in hybrid FRC beams. Failure mode analysis revealed a transition from brittle concrete crushing to ductile progressive failure with improved post-peak behavior. The study establishes comprehensive relationships between fiber content, structural response parameters, and failure characteristics, providing design guidelines for FRC structural applications.
PERFORMANCE EVALUATION OF CLOUD SIMULATION AND FOG COMPUTING AND ADVANCED FOG WITH AI UNDER DYNAMIC LOAD CONDITIONS
This study presents a comprehensive evaluation of cloud simulation and fog computing systems enhanced with artificial intelligence (AI) under dynamic load conditions. By examining metrics such as latency, CPU utilization, bandwidth, and the System Improvement Factor (SIF), the research highlights the performance improvements achieved through advanced fog computing mechanisms. The findings reveal significant reductions in latency and enhancements in resource efficiency across varying load levels, demonstrating the potential of AI-driven optimizations in fog environments. These results contribute to the understanding and development of scalable, high-performance fog computing systems for real-world applications
AI-DRIVEN PARAMETER LEARNING FOR HIGH-DIMENSIONAL FRACTIONAL SEIR MODELS
This study presents a fractional-order SEIR epidemic model consisting of nine compart- ments for the analysis of complex disease transmission dynamics. The proposed model is formulated using the Atangana–Baleanu Caputo fractional derivative in order to incorporate memory and heredi- tary effects that arise in epidemic processes. A data-assisted computational methodology is employed to estimate both the epidemiological parameters and the fractional order of the system. To efficiently solve the associated inverse problem, a metaheuristic optimization strategy is adopted. Numerical simulations carried out in a Python environment demonstrate that fractional memory effects contribute to reducing infection peaks and significantly influence the temporal evolution of disease spread. The proposed framework provides valuable insight for epidemic forecasting and computational disease modeling.
INVESTATION OF THERMAL PERFORMANCE OF ALUMINIUM CASTED V-SHAPED FIN ARRAYS WITH DIFFERENT NOTCH CONFIGURATION, INCLINATION AND VARIABLE POWER SUPPLY
Heat generation is a common issue in industrial devices, and thus efficient thermal management solutions are sought to improve performance and reduce overheating. Heat sinks that use fins are widely used in applications as heat exchangers, engines, electric motors and electronic devices, the fins being used to promote heat dissipation by naturalor forced convection.Researchershave worked over the years to optimize and understand how fin material, geometry, dimensions, and inclination affect heat transfer. Although the augmentation of natural convection heat transfer in V shaped fins, with varied notch sizes and surface angles is relatively underexplored, however. The effect of geometric parameters such as notch size and surface angle on the natural convection performance of V-shaped fins is analyzed in this experimental investigation. It is found that the heat transfer coefficient and Nusselt number are maximized at 60° surface inclination, and 20 mm notch size, which offers the potential for sustained improved heat dissipation through optimized fin configurations.
EVALUATION OF WASTE HANDLING USING TRASH BARRIERS IN THE LAMPENAI VILLAGE RIVER, EAST LUWU
The lack of public awareness about the prohibition of littering in rivers, especially among those living around the Lampenai River, has resulted in a significant amount of waste flowing into the sea. This behavior, which has been ongoing for years without awareness, has caused river pollution. River pollution can actually be overcome if the government intervenes strongly with the community, while at the same time installing trash barriers at certain points in an effort to prevent trash from flowing into the sea. This study uses observational and quantitative methods, where the researcher directly observes the objects and phenomena being studied without manipulation or special treatment of the variables. This study aims to determine the amount of waste captured by trash barriers in the Lampenai River by linking ecological conditions and average daily waste collection data based on population size. The effectiveness of the trash barrier in collecting heavy waste was 123.2% at station one, 111.4% at station two, 108.1% at station three, and 103.8% at station four.
Muhammad Arijal, Asiyanti T Lando, Ibrahim Djamaluddin
LEAN SAFETY: INTEGRATING LEAN MANUFACTURING AND INDUSTRIAL SAFETY FOR WASTE-FREE AND ACCIDENT-FREE OPERATIONS
Modern industrial organisations face increasing pressure to improve productivity, reduce operational costs, eliminate waste, enhance quality, and maintain high standards of occupational safety. Lean Manufacturing has emerged as one of the most influential management philosophies for achieving operational excellence through the systematic elimination of non-value-adding activities. Simultaneously, Industrial Safety Engineering seeks to prevent workplace accidents, occupational illnesses, equipment failures, and process-related hazards. Despite the shared objective of improving organisational performance, lean manufacturing and industrial safety have traditionally been implemented as separate managerial functions. This separation often creates misconceptions that safety requirements reduce operational efficiency or that lean initiatives prioritise productivity at the expense of worker well-being. This conceptual paper proposes the concept of Lean Safety, defined as the strategic integration of lean manufacturing principles and industrial safety engineering practices to achieve waste-free and accident-free industrial operations. The paper argues that many forms of operational waste identified in lean systems, such as unnecessary motion, waiting, overprocessing, defects, excessive transportation, and poor workplace organisation, are also sources of workplace hazards and safety risks. Consequently, eliminating operational waste can simultaneously improve safety performance and operational efficiency. A conceptual framework is proposed that integrates key lean manufacturing tools, including 5S, Kaizen, Value Stream Mapping, Standardised Work, Total Productive Maintenance, Visual Management, and Continuous Improvement, with industrial safety principles such as hazard identification, risk assessment, safety culture, ergonomics, and accident prevention. The framework introduces Lean Safety as a strategic mechanism for achieving operational excellence, workforce well-being, process reliability, and sustainable industrial performance. The study contributes to the literature by establishing a theoretical linkage between lean thinking and industrial safety engineering while providing a foundation for future empirical investigation. The framework supports organisations seeking to achieve the dual objectives of zero waste and zero accidents within increasingly competitive industrial environments.
Dr. Dipti Acharjya, Sudhanshu Shekhar behera , Little kumar Samal,
SYNTHETIC DATA GENERATION FOR RESPONSIBLE TABULAR MACHINE LEARNING: REVIEW AND RESEARCH DIRECTIONS
Tabular machine learning is widely used in real-world applications such as healthcare, finance, and insurance. However, these systems face significant challenges, including data imbalance, bias, and privacy risks, which may affect model performance and reliability. Synthetic data generation has emerged as an effective solution to address these issues by creating artificial datasets that preserve statistical characteristics while protecting sensitive information. This paper presents a comprehensive review of synthetic data generation techniques for tabular machine learning. It analyzes their effectiveness in improving data quality, mitigating bias, handling class imbalance, and ensuring privacy preservation. In addition, the study provides a structured taxonomy and comparative analysis of existing methods. The review also identifies key research gaps, particularly the lack of unified frameworks and standardized evaluation metrics. It highlights the need for simple, scalable, and integrated solutions that balance performance, fairness, and privacy for real-world applications
SEISMIC PERFORMANCE ENHANCEMENT OF STEEL STRUCTURES USING SHAPE MEMORY ALLOYS FOR IMPROVED POST-EARTHQUAKE RECOVERY
The increasing demand for resilient infrastructure in earthquake-prone regions has driven the development of structural systems capable of both resisting seismic forces and recovering rapidly after seismic events. Conventional steel structures, although ductile and efficient in energy dissipation, often experience permanent deformation and residual drift due to plastic hinge formation, leading to significant repair costs and downtime. This study investigates the integration of Shape Memory Alloys (SMAs) into steel structures as an innovative approach to enhance seismic performance and post-earthquake recovery. SMAs, particularly Nickel–Titanium (NiTi) and iron-based (FeSMA) alloys, exhibit unique properties such as superelasticity and the shape memory effect, enabling large recoverable strains and stable energy dissipation under cyclic loading. A finite element-based analytical framework is employed to evaluate the behavior of SMA-integrated steel systems subjected to simulated seismic loading. The structural response is analyzed in terms of load–displacement behavior, hysteretic performance, and residual drift. Results indicate that SMA-integrated systems significantly reduce residual deformation, enhance self-centering capability, and maintain stable cyclic performance compared to conventional steel structures. The study highlights the potential of SMA technology in transforming traditional steel systems into smart, resilient structures. Practical challenges such as material cost, design complexity, and the need for standardized guidelines are also discussed, providing direction for future research and implementation.
Ms. Oza Jyoti Omprakash, Dr. Manoj Sharma
ANALYSIS OF PRODUCTION RATES AND SCHEDULE DELAYS IN LINEAR ROAD CONSTRUCTION PROJECTS: A CASE STUDY APPROACH
Linear construction projects such as road infrastructure development are highly sensitive to variations in production rates, which significantly influence project duration and scheduling performance. This study presents a detailed case study analysis of production rates and schedule delays in a road construction project. The research investigates the variability in activity productivity and its impact on overall project timelines. Field data related to key construction activities, including earthwork, subgrade preparation, granular sub-base, and bituminous layers, are analyzed using statistical and comparative methods. The study evaluates planned versus actual production rates and identifies deviations contributing to schedule delays. A simulation-based approach is employed to assess the influence of variability on project duration. The results indicate that fluctuations in production rates, particularly in critical activities such as pavement layers, significantly affect project completion time. The study highlights the importance of accurate productivity estimation, effective resource planning, and continuous monitoring to minimize delays. The findings provide practical insights for improving scheduling efficiency and project control in linear infrastructure projects.
Mr. Mehendale Aditya Prasad, Dr. Manoj Sharma
AN EVALUATION OF MACHINE LEARNING ALGORITHMS IN PREDICTING STUDENT PERFORMANCE
This study investigates machine learning algorithms to predict student performance using a publicly available Kaggle dataset containing academic, behavioral, and socio-demographic attributes. Four algorithms—Logistic Regression, Random Forest, Decision Tree, and Gradient Boosting—were evaluated using cross-validation for reliability and accuracy assessment. The Gradient Boosting classifier emerged as the best-performing model, achieving an accuracy of 96%. Its interpretability and simplicity make it well-suited for educational data analysis. Random Forest and Decision Tree provided competitive results, while Logistic Regression demonstrated lower performance due to the dataset’s non-linear patterns. These results highlight the critical role of algorithm selection in student performance prediction and underscore the potential of machine learning in enhancing educational decision-making. Future work could explore advanced models and feature engineering to further improve prediction accuracy.
COMPARATIVE EVALUATION OF SHAP AND LIME FOR EXPLAINABLE TYPE 2 DIABETES PREDICTION MODELS
Type 2 Diabetes Mellitus (T2DM) is a globally escalating chronic metabolic disorder affecting over 537 million adults worldwide as of 2021, with projections surpassing 780 million by 2045. Early and accurate prediction of T2DM through machine learning (ML) models offers significant clinical promise; however, the deployment of opaque, black-box predictive systems in healthcare raises substantial concerns regarding interpretability, accountability, and patient trust. Explainable Artificial Intelligence (XAI) methodologies have emerged as pivotal solutions to address these challenges. This paper presents a rigorous comparative evaluation of two leading XAI frameworks — SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) — applied to multiple state-of-the-art ML classifiers for T2DM prediction, including Random Forest (RF), Gradient Boosting (XGBoost), LightGBM, Support Vector Machine (SVM), and Logistic Regression (LR). Experiments were conducted on the benchmark PIMA Indians Diabetes Dataset (PIDD) and an augmented clinical cohort. Key evaluation dimensions include predictive accuracy, explanation fidelity, computational efficiency, stability, and clinical interpretability. XGBoost with SHAP yielded the highest classification accuracy (AUC = 0.947), while LIME demonstrated superior explanation locality and model-agnostic adaptability. SHAP consistently produced more stable, globally coherent feature attributions compared to LIME's locally bounded, computationally intensive explanations. The findings provide actionable recommendations for clinical AI practitioners selecting XAI tools and highlight avenues for hybrid XAI architectures in diabetes management systems.
A HYBRID SUPERVISED LEARNING APPROACH FOR RELIABLE VEHICULAR CONGESTION PREDICTION IN SMART CITIES
Introduction: Urban vehicular congestion continues to pose a serious problem for transportation systems, causing delays, excessive fuel consumption, and greater environmental impact. Intelligent Transportation Systems (ITS) apply machine learning algorithms to systematically analyze and model traffic behavior. By using automated supervised learning techniques, congestion levels can be identified accurately and predicted in the short term. This study proposes a data-centric approach to enable efficient real-time traffic congestion detection and forecasting. Objectives: The main objective of this research is to develop an automated supervised learning system for traffic congestion detection and prediction. It focuses on evaluating various classification and regression algorithms to determine the most effective model. The study also aims to enhance predictive performance through proper data preprocessing and balancing techniques. Additionally, it intends to classify congestion into different levels to support efficient traffic control and informed decision-making. Methods: Traffic information is gathered from various sources, including road sensors, GPS systems, surveillance cameras, and external factors such as weather conditions and public events. The collected data is then processed through cleaning, feature engineering, and SMOTE-based data balancing to improve model effectiveness. Several supervised learning algorithms—such as Random Forest, Support Vector Machine (SVM), XGBoost, and LightGBM—are trained and validated on the prepared dataset. Performance assessment is conducted using evaluation metrics including accuracy, precision, recall, and F1-score. Results: The experimental findings indicate that ensemble and hybrid approaches outperform standalone classifiers in both detection and prediction accuracy. The proposed system successfully categorizes traffic conditions into multi level like Low, Normal,High and Heavy congestion levels. Techniques such as data balancing and feature selection contribute notably to improved model robustness and overall performance. The developed framework delivers dependable short-term congestion predictions, enabling proactive and efficient traffic management. Conclusions: The research concludes that automated supervised learning techniques offer an effective approach for real-time detection and prediction of traffic congestion. Ensemble and hybrid models improve both accuracy and resilience when dealing with complex and dynamic traffic conditions. Comprehensive data preprocessing and balanced datasets play a vital role in achieving optimal predictive performance. Overall, the proposed framework contributes to smart city development by facilitating intelligent, data-driven traffic management strategies.
CARDIAC DISEASE PREDICTION USING MACHINE LEARNING AND DEEP LEARNING: A COMPREHENSIVE MULTI-MODEL CLINICAL EVALUATION WITH RECALL OPTIMIZATION
Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, claiming approximately 17.9 million deaths annually. Early detection of high-risk individuals is critical to reducing mortality rates, yet this remains challenging due to the multifactorial and nonlinear nature of cardiac risk. This paper presents a comprehensive review and experimental evaluation of machine learning (ML) and deep learning (DL) approaches for cardiac disease prediction, examining 32 model configurations systematically assessed across a five-phase study on 10,585 patient records characterised by 26 clinical variables. Starting from baseline classifiers and building toward ensemble methods, neural networks, automated hyperparameter tuning, probability calibration, and a clinically motivated recall-maximisation framework, the work traces the incremental contribution of each methodological step. Four primary contributions are synthesised: (i) three engineered composite features—Clinical Risk Score, Cardiac Load, and Metabolic Index—constructed from cardiovascular domain knowledge and validated through mutual information analysis; (ii) a comparative evaluation of four class-imbalance correction strategies, from standard SMOTE to an aggressive 3× oversampling scheme with asymmetric cost weighting; (iii) isotonic probability calibration enabling a validated three-tier patient risk stratification in which the high-risk tier demonstrates a 94.2% observed cardiac event rate; and (iv) a threshold-adjusted Random Forest achieving 99.21% sensitivity, with only 6 of 755 confirmed cardiac events undetected. This review demonstrates that data preparation—specifically clinical feature engineering and class-imbalance correction—contributes more to predictive performance than model architecture choice, and traces a reproducible path from general-purpose ML classifiers toward a clinically oriented, patient-safety-governed screening tool.
Ms. Shreyaben Pareshkumar Bhatt, Dr. Kusum Lata Aggarwal
PSYCHOLOGICAL DETERMINANTS OF INVESTOR BEHAVIOR: AN EMPIRICAL INVESTIGATION OF COGNITIVE BIASES IN CAPITAL MARKETS
Investors are treated as rational decision-makers according to anticipated utility theory in traditional financial theory. Contrarily, Behavioral Finance strongly disagrees with this rationale point of view, arguing that investors frequently stray from rationality when making investment decisions. Investors today frequently make foolish investment decisions. Frequently, the choice is based on an assumption that is far from rational. When faced with a risky circumstance, investors' decisions are frequently influenced by their objectivity, emotions, and other psychological aspects. Here the primary data is used to collect data through questionnaires and secondary data is used for literature review. The study examines the impact of behavioral bias on investors based on age and education. The study explores various behavioral biases such as herding, endowment, familiarity, overconfidence, recency, loss aversion, and regret aversion. These biases play a significant role in shaping investors' decision-making, often resulting in irrational actions within financial markets. By analyzing these tendencies, we can better understand how emotions and mental shortcuts influence investment decisions, which in turn can affect both returns and risk management strategies. Results show no significant relationship between age and biases, with the null hypothesis accepted and the alternate hypothesis rejected. Overconfidence bias, recency, familiarity, and loss aversion bias are more influenced by investors with science and diploma qualifications, while commerce background investors are less influenced.
LARGE LANGUAGE MODEL ALIGNMENT AND SAFETY: A REINFORCEMENT LEARNING FROM HUMAN FEEDBACK FRAMEWORK FOR REDUCING HALLUCINATION, BIAS, AND HARMFUL OUTPUT IN DOMAIN-SPECIFIC LLMS
The deployment of Large Language Models (LLMs) in high-stakes domain-specific applications encompassing medical diagnosis support, legal document analysis, financial advisory systems, and educational assessment platforms has surfaced critical safety challenges that general-purpose alignment techniques inadequately address. Domain-specific LLMs exhibit three characteristic failure modes with severe real-world consequences: hallucination of domain-specific facts including fabricated medical dosages, non-existent legal precedents, and incorrect financial regulations; systematic bias reflecting training data skewness including gender bias in medical recommendations and racial bias in legal risk assessments; and harmful output generation violating domain-specific ethical standards. This paper presents SafeAlign-LLM, a novel multi-phase Reinforcement Learning from Human Feedback (RLHF) framework specifically architected for domain-specific LLM alignment, integrating four complementary techniques: supervised fine-tuning on domain-curated demonstration datasets, multi-dimensional reward modeling capturing helpfulness, harmlessness, honesty, and domain accuracy simultaneously, Proximal Policy Optimization (PPO) with KL-divergence constraint for stable policy training, and a Constitutional AI (CAI) layer enforcing domain-specific ethical principles through self-critique and revision. SafeAlign-LLM introduces three novel contributions beyond standard RLHF: a Hallucination Detection and Grounding (HDG) module using Retrieval-Augmented Generation cross-referencing against authoritative domain knowledge bases achieving 94.2% hallucination detection accuracy; a Multi-Dimensional Bias Auditing (MDBA) framework evaluating 12 bias dimensions across demographic axes; and an Uncertainty Quantification (UQ) mechanism enabling calibrated confidence expression. Evaluated across four domain-specific deployments including MedAlign-LLM, LegalAlign-LLM, FinAlign-LLM, and EduAlign-LLM, SafeAlign-LLM reduces hallucination rates by 73.4%, bias scores by 68.2%, and harmful output incidence by 89.7% compared to base RLHF, while maintaining 94.3% domain task performance, establishing a new state-of-the-art in safe domain-specific LLM deployment.
Ms Monika Gharu, Ms Jasmeet Kaur, Ms Tamanna Arora Mr Manish,
REGULATORY COMPLIANCE AND ETHICAL BOUNDARIES OF ARTIFICIAL INTELLIGENCE IN DIGITAL FORENSICS: A Cyber Law Perspective
The integration of Artificial Intelligence into digital forensics practice represents one of the most consequential and legally contested developments in contemporary law enforcement and judicial proceedings. AI-powered tools now perform tasks previously executed exclusively by certified human examiners — automated file carving and recovery, malware behavioral classification, authorship attribution of digital documents, deepfake image and video detection, network intrusion timeline reconstruction, and cryptocurrency transaction graph analysis. While these capabilities offer dramatic improvements in forensic throughput, consistency, and analytical depth, they introduce profound legal, ethical, and technical challenges: the admissibility of AI-generated evidence under established evidentiary standards (Daubert, Frye, Civil Evidence Act), the explainability requirements of black-box AI decisions in criminal proceedings where liberty is at stake, the risk of systematic algorithmic bias producing discriminatory forensic outcomes, and the attribution of liability when AI forensic tools produce incorrect or misleading evidence. This paper presents a comprehensive analysis of the regulatory compliance landscape and ethical boundaries governing AI in digital forensics, proposing the FORENSIS-AI Framework — a five-pillar architecture integrating legal compliance automation, AI evidence analysis, explainable AI, ethics auditing, and blockchain evidence integrity. Drawing upon comparative legal analysis across six jurisdictions (EU, US, UK, Australia, India, Singapore), case law examination of 84 judicial decisions involving AI forensic evidence (2019–2025), expert interviews with 32 digital forensics practitioners and legal professionals, and technical evaluation of eight leading AI forensic tools against our proposed compliance framework, we identify seventeen critical compliance gaps and propose a structured remediation roadmap. Our empirical analysis demonstrates that AI forensic tools meeting the full FORENSIS-AI compliance standard achieve 34.8% higher evidence admissibility rates in contested proceedings and 67.2% lower successful challenge rates compared to non-compliant tools, establishing a quantifiable legal benefit to ethical AI forensics compliance. This research provides essential guidance for digital forensics practitioners, legal professionals, AI developers, and regulatory bodies navigating the complex intersection of artificial intelligence and forensic justice.
Dr. Jitendra H. Darji, Mr. Shanmukaswamy CV Dr. Kamalesh V N, Ankita Patel, Shrushti Vadher, Anshul Surendra Gowda,
DARKNET BEHAVIORAL ANALYSIS: LEVERAGING NATURAL LANGUAGE PROCESSING AND MACHINE LEARNING FOR CYBERCRIMINAL ACTIVITY PREDICTION
The darknet — encompassing Tor-anonymized hidden services, I2P networks, and encrypted peer-to-peer marketplaces — constitutes a dynamic and richly informative ecosystem for cybercriminal activity, hosting marketplaces for malware, ransomware-as-a-service (RaaS) offerings, stolen credential databases, exploit kits, and coordination forums for advanced persistent threat (APT) actors. Proactive threat intelligence derived from darknet behavioral analysis offers security operations centers (SOCs), law enforcement agencies (LEAs), and national cyber defense agencies an unparalleled early warning capability — enabling anticipation of cyberattacks days to weeks before their execution through analysis of pre-attack discussions, tool procurement patterns, and target reconnaissance chatter. This paper presents DarkNetPred, a comprehensive five-module AI pipeline for automated darknet behavioral analysis and cybercriminal activity prediction. DarkNetPred integrates: (1) an automated Tor-crawling data collection framework with multi-language support and PII-preserving anonymization; (2) DarkBERT-CTI, a domain-adapted BERT model fine-tuned on 14.2 million darknet documents for cybersecurity threat intelligence extraction; (3) a Graph Neural Network (GNN) actor network analyzer mapping criminal collaboration structures and identifying key nodes; (4) a hybrid LSTM-Transformer threat prediction engine forecasting attack campaigns with temporal precision; and (5) an automated threat intelligence dashboard generating STIX/TAXII-compatible indicator feeds for SOC integration. Evaluated on a longitudinal dataset of 28.4 million darknet forum posts, 847,000 marketplace listings, and 12,400 confirmed cyberattack incidents spanning 2022–2025, DarkNetPred achieves 87.6% attack campaign prediction accuracy with an average 14.3-day advance warning window, 91.2% threat actor re-identification accuracy across forum pseudonym changes, and 94.8% malware classification accuracy from marketplace listing analysis. The framework represents a significant advancement in proactive threat intelligence capabilities and contributes directly to the PhD research agenda in AI-driven cybercriminal behavior analysis.
Nilesh Parihar, Dr. Annie Sujith, Mr. Shanmukaswamy CV Miki Patel, Puja Chaturvedi Dr. Kamalesh V N,
A COMPREHENSIVE THREAT MODELING AND FORENSIC READINESS FRAMEWORK FOR UNIVERSITY ONLINE EXAMINATION PLATFORMS
Online examination platforms have emerged as critical academic infrastructure, yet they remain acutely vulnerable to a spectrum of cyber threats including identity fraud, content leakage, behavioral cheating, and denial-of-service attacks. Existing security frameworks lack examination-specific threat models and forensic readiness provisions that are legally defensible in academic misconduct proceedings. This paper proposes the Comprehensive Threat and Forensic Readiness Framework (CTFRF), a novel five-layer architecture that integrates STRIDE-extended threat modeling, AI-driven behavioral anomaly detection, Zero Trust access control, and immutable forensic audit logging specifically tailored for university online examination ecosystems. Empirical evaluation on simulated examination environments demonstrates that CTFRF achieves an overall detection precision of 97.0%, an F1 score of 0.967, and reduces mean incident containment time to under 22 seconds across five attack categories. The framework aligns with ISO 27001:2022, NIST CSF 2.0, and India's Digital Personal Data Protection (DPDP) Act 2023, offering a governance-ready, legally admissible forensic evidence pipeline.
ZERO-TRUST ARCHITECTURE IN AI-POWERED CYBERSECURITY SYSTEMS: IMPLEMENTATION, CHALLENGES, AND FUTURE DIRECTIONS
The traditional perimeter-based cybersecurity paradigm — predicated on the assumption that threats originate externally and that internal network traffic is inherently trustworthy — has been conclusively invalidated by the proliferation of advanced persistent threats (APTs), insider attacks, cloud-native architectures, and the dissolution of clearly defined network perimeters through remote work, BYOD policies, and multi-cloud deployments. Zero-Trust Architecture (ZTA), operationalizing the principle of 'Never Trust, Always Verify,' represents the foundational paradigm shift required to secure modern distributed computing environments. However, the complexity and dynamism of contemporary threat landscapes have outpaced human-speed policy enforcement, creating an urgent demand for AI-driven automation of Zero-Trust policy evaluation, enforcement, and adaptation. This paper presents ZeroTrustAI, a novel five-plane architecture integrating Machine Learning (ML) and Deep Learning (DL) into every control layer of a Zero-Trust implementation — from continuous identity risk scoring through AI-driven Policy Decision Points (PDP) to automated micro-segmentation reconfiguration and explainable access control audit. Drawing upon NIST SP 800-207 (Zero Trust Architecture standard) and NIST SP 800-213 (IoT ZTA extensions), ZeroTrustAI addresses three critical limitations of current ZTA implementations: the scalability bottleneck of human-curated policy management, the latency incompatibility of strict verification with real-time application performance, and the explainability deficit of black-box AI access control decisions in regulated environments. Evaluated across hybrid cloud deployments in three enterprise environments (18,400 endpoints, 72 million access requests over 24 months), ZeroTrustAI achieves 99.3% policy enforcement accuracy, reduces unauthorized lateral movement incidents by 91.7%, and delivers access decision latency of 8.3ms — a 94.2% improvement over rule-based ZTA implementations. Critically, the integrated XAI module provides human-interpretable justification for 98.6% of access decisions, satisfying regulatory explainability requirements under GDPR Article 22, EU AI Act Article 13, and SOX Section 302.
Nilam Joshi, Nilesh Parihar, Prof. Srinivasa H P Tithi Patel Dhruvi Vanecha, Dr. Kamalesh V N,
BLOCKCHAIN AND AI-ENABLED EXAMINATION INTEGRITY VERIFICATION: A DECENTRALIZED FORENSIC READINESS FRAMEWORK USING DEEP LEARNING
University examination systems represent high-value targets for sophisticated cyber threats, including identity fraud, result tampering, AI-assisted cheating, and insider log manipulation — attacks whose detection and legal prosecution require both real-time intelligence and forensically admissible, tamper-proof evidence chains. Existing frameworks address either blockchain-based integrity or AI-driven detection in isolation; none provide an integrated, examination-specific, legally defensible architecture. This paper presents DAIF (Decentralized AI-Integrated Forensic Readiness Framework), a novel six-layer architecture that fuses a permissioned Hyperledger Fabric blockchain, a five-model Deep Learning ensemble (FaceNet, LSTM-Keystroke Dynamics, BERT-based AI-text detection, pruned ResNet-18, and Graph Neural Network Sybil scoring), IPFS-based distributed forensic evidence storage, and RFC 3161-compliant cryptographic timestamping to produce legally admissible digital evidence aligned with India's DPDP Act 2023 and Bharatiya Sakshya Adhiniyam 2023. Evaluated on simulated 5,000-concurrent-user examination environments across three university campuses, DAIF achieves an ensemble detection accuracy of 97.2%, F1 score of 0.972, smart contract transaction throughput of 161–210 TPS, and 100% blockchain-verified evidence integrity up to 2,500 concurrent sessions. The framework establishes a new benchmark for examination integrity combining decentralization, deep intelligence, and forensic legal readiness.
Patel Hardik Arunbhai, Vidyashree L, Prof. Srinivasa H P, Miki Patel, Dr.Jitendra H. Darji, Dr. Kamalesh V N,
FEDERATED LEARNING FOR PRIVACY-PRESERVING THREAT INTELLIGENCE SHARING IN DISTRIBUTED CYBERSECURITY ECOSYSTEMS
Effective cybersecurity threat intelligence depends fundamentally on the breadth and timeliness of threat data — yet the organizations most capable of generating actionable intelligence are simultaneously most constrained in sharing it due to privacy regulations (GDPR, HIPAA, PDPA), competitive concerns, legal liability, and national security classifications. This tension between intelligence sharing and data privacy represents one of the most consequential unsolved challenges in cybersecurity: organizations that share threat intelligence detect attacks 2.4 times faster and suffer 47.3% lower breach costs, yet fewer than 23% of enterprises engage in structured threat intelligence sharing due to these barriers. This paper presents FedThreat-AI, a novel federated learning framework enabling privacy-preserving threat intelligence sharing across distributed cybersecurity ecosystems without requiring any organization to expose its raw security data, proprietary detection rules, or sensitive network topology. FedThreat-AI integrates four privacy-enhancing technologies — differential privacy (DP), homomorphic encryption (HE), secure multi-party computation (SMPC), and Byzantine-robust gradient aggregation — into a unified federated learning pipeline trained on distributed threat telemetry across participating organizations. The framework produces a continuously improving global threat detection model incorporating the collective intelligence of all participants, distributed back to each organization as model updates rather than data. Evaluated across a consortium of 24 organizations spanning financial services, healthcare, government, and technology sectors over 18 months (2023–2025), FedThreat-AI achieves global threat detection accuracy of 96.8% — only 1.4 percentage points below a centralized baseline that requires full data sharing — while providing mathematically provable privacy guarantees (ε = 0.8, δ = 10⁻⁵ per training round). The framework further demonstrates resilience against Byzantine poisoning attacks from up to 30% malicious participants and reduces mean time to detect novel threat campaigns by 67.4% compared to organization-siloed detection. FedThreat-AI is fully compatible with STIX 2.1 and TAXII 2.1 standards, enabling integration with existing threat intelligence platforms and ISACs.
Dr. Angira A. Patel, Nilam Joshi, Vaidehi Patel, Avani Vagadiya Dhruvi Pandya, Dr. Kamalesh V N,
PREDICTIVE ANALYTICS FOR SMART FARMING: INTEGRATING IOT SENSOR DATA WITH MACHINE LEARNING MODELS
Smart farming is a modern approach to agriculture that uses advanced technologies to enhance farming processes and boost productivity. In this research, we develop a smart farming system that integrates IoT sensors and machine learning to support farmers in making informed decisions. Sensors are deployed in the field to gather real-time data on soil moisture, temperature, humidity, and other environmental parameters. Additionally, weather forecast data is incorporated to anticipate future conditions. The collected data is cleaned to eliminate errors and then analyzed using machine learning algorithms such as Decision Tree and Random Forest. The system can predict crop conditions and irrigation needs and provides actionable recommendations to farmers. For instance, if the soil is dry but rainfall is anticipated, the system advises delaying irrigation, thereby conserving water. By merging sensor data and weather insights, the system achieves higher accuracy and utility. It minimizes manual labor and optimizes resource utilization, including water and fertilizers. Results indicate that this system delivers more accurate predictions and enables farmers to make timely and effective decisions compared to traditional methods. In summary, this study demonstrates that integrating IoT, machine learning, and intelligent recommendations can establish an efficient and smart farming solution for modern agriculture.
Khushpreet Kaur, Touseef Ahmad Lone, Er Anureet Kaur
TELEMETRY-DRIVEN FINOPS AUTOMATION FOR COST OPTIMIZATION IN MULTI-CLOUD ENVIRONMENTS
Abstract: The dynamic nature of the provisioning and instances mixed with the heterogeneous configurations and cost-based pricing models that have come with the rapid implementation of multi-clouds has presented a tremendous challenge to cost governance. The constant over-provisioning, idle computer resources and the ineffective allocation of storage are often the cause of unnecessary operational cost. In this study, a telemetry-based FinOps automation framework will be proposed gaining insights on structural and behavioural inefficiencies across the Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). Descriptive statistics, correlation modelling, a threshold-based idle detection and a rule-based rightsizing decision framework were used to analyse a production-representative multi-cloud data. The cross-provider cost-efficiency analysis has been based on the standardised metrics, such as cost per vCPU-horn. Findings indicate a moderate on the whole utilisation, heavy reliance on cost in dependence on provided capacity rather than on the actual requirement to work and high potentials of optimisation. The model of simulation of an optimisation impact simulation proves the cost savings that can be measured by the automated policy of idle shutdown and rightsizing. The results indicate that inefficiencies in clouds are mainly due to provisioning methods than provider choice which highlights the necessity of telemetry constantly and automated government. The suggested structure involves the provision of a repeatable and systematic approach to enhancing cost-performance fit in heterogeneous multi-cloud setup and promoting the active implementation of FinOps.
Mr. Gurjot Singh Gill, Mr. Touseef Ahmad Lone
AI-BASED FAULT DETECTION AND PREDICTIVE MAINTENANCE IN SMART POWER DISTRIBUTION SYSTEMS
AI-based fault detection and predictive maintenance have emerged as transformative technologies in modern smart power distribution systems by enabling real-time monitoring, intelligent fault classification, and proactive asset health assessment across distributed electrical infrastructures. As distribution networks become increasingly digitalized through IoT sensors, phasor measurement units, smart meters, and advanced automation, traditional rule-based and periodic maintenance approaches fail to address the rising complexity, dynamic load behavior, and nonlinear fault characteristics of modern grids. Artificial intelligence, deep learning, and edge-cloud analytics offer a scalable and data-driven solution for detecting incipient faults, analyzing transient disturbances, and predicting equipment degradation before critical failures occur. The increasing availability of high-frequency electrical signals, asset condition parameters, and historical maintenance logs has accelerated the demand for intelligent systems capable of integrating heterogeneous grid data for early warning and reliability optimization. This paper presents an AI-driven predictive maintenance framework that leverages hybrid deep learning architectures, multi-sensor grid data, and edge-enhanced anomaly detection to support real-time fault localization, equipment health forecasting, and operational decision-making. The study evaluates system performance, detection accuracy, computational efficiency, and predictive reliability across diverse distribution scenarios, emphasizing the role of AI as a foundational technology for next-generation smart power distribution systems.
AI-Based Fault Detection, Predictive Maintenance, Smart Power Distribution Systems, Deep Learning, Grid Condition Monitoring
Dr. Ravindra Mukund Malkar, Laxman Baburao Abhang, Niran I R
Copyright (c) 2026 Advanced Engineering Sciences
SSL-BASED LOAD BALANCING INFRASTRUCTURE FOR PERFORMANCE AND SECURITY UTILIZATION IN IAAS CLOUD ENVIRONMENTS
Cloud computing has become a strong method for scalable IT related service deployment , mainly through Infrastructure-as-a-Service (IaaS). Traditional load balancing algorithms not ready to assign the encoded workload mention by by Secure Sockets Layer (SSL) and Transport Layer Security (TLS), given result in performance traffic and security attacks. This study gives us an SSL based load balancing nfratsrucure that combines security measures into routing decisions making while performing security tasks to a central SSL gateway. The method is perform in CloudSim with SSL-specific overhead methods and executed verses old strategies. Results gives a 30% minimization in latency, 36% improvement in maximum output, stable CPU utilization, and improved resilience against security attacks. The results implements that SSL-based load balancing provides a extensible and improved solution for modern IaaS environments.
AI-DRIVEN AND MULTIMODAL INNOVATIONS IN BIOMEDICAL IMAGING AND SENSING IN ARTHRITIS
Artificial intelligence (AI) especially deep learning is changing biomedical imaging and quantitative sensing for all types of arthritis such as osteoarthritis (OA), rheumatoid arthritis (RA), psoriatic arthritis and axial spondyloarthritis (axSpA). Recent advances include automated grading of structural damage on X-rays, three-dimensional (3D) segmentation and tissue characterization on MRI and CT, radiomics-based phenotyping and combining imaging with clinical, biomarker and multi-omics data to predict prognosis and treatment response. Deep learning models now match or exceed the performance of expert readers in detecting cartilage lesions, automating Kellgren–Lawrence grading, segmenting cartilage and menisci and predicting radiographic progression and the need for joint replacement from baseline MRI or radiographs in osteoarthritis. AI helps with automated radiographic scoring, MRI detection of inflammatory sacroiliitis and risk stratification for cardiovascular complications in RA and spondyloarthritis using imaging-derived radiomic biomarkers. AI pipelines are being used more and more to analyze multimodal workflows in musculoskeletal imaging that combine radiography, CT, DXA, MRI, ultrasound and clinical data. These pipelines help find quantitative, reproducible biomarkers of joint damage, inflammation and prognosis. This narrative review talks about new AI-driven and multimodal technologies for imaging and sensing arthritis. It also talks about new clinical uses that are coming up and the main problems that need to be solved such as generalizability, bias, interpretability and regulatory translation.
Nitish Sengar, Avijit Mazumder, Saumya Das