International Journal of Scientific Research in Computer Science, Engineering and Information Technology
International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT) is peer-reviewed, online international journal published Bimonthly by Shauryam Research Institute. IJSRCSEIT is a highly-selective journal, covering topics that appeal to a broad readership of various branches of Computer Science, Engineering, Information Technology and related fields. The Journal has many benefits all geared toward strengthening research skills and advancing academic careers. Journal publications are a vital part of academic career advancement. To maintain a high-quality journal, manuscripts that appear in the IJSRCSEIT Articles section have been subjected to a rigorous review process. Email addresses for submitting articles are ijsrcseit@gmail.com, editor@ijsrcseit.com JOURNAL INFORMATION Title : International Journal of Scientific Research in Computer Science, Engineering and Information Technology Frequency : Bimonthly E-ISSN : 2456-3307 Publisher : Shauryam Research Institute Refereed : Peer Review Copyright : Technoscience Academy, India Starting Year : 2016 Subject : Computer Science, Engineering and Information Technology Language : English Distribution : Open Access Publication Format : PDF & HTML -> Online Document Identifier Type : DOI (https://doi.org/10.32628/IJSRCSEIT ) Email Id : editor@ijsrcseit.com, ijsrcseit@gmail.com Mobile No. : +1 (404) 860-9920 (WhatsApp support only) Website : https://ijsrcseit.com
Real-Time Payment Orchestration and Fraud Governance Framework: Cloud-Native Treasury Optimization with Ensemble Deep Learning Integration
The exponential growth of digital payment ecosystems has created unprecedented challenges in payment orchestration, fraud detection, and treasury management across heterogeneous financial networks. Contemporary banking systems struggle with fragmented payment rails, inadequate real-time fraud governance, and limited integration between physical and digital liquidity channels, resulting in operational inefficiencies and elevated fraud exposure. This research introduces an intelligent cloud-native treasury digitization framework that synthesizes multi-rail payment orchestration, accelerated receivables processing, hybrid cash logistics, and layered fraud governance through ensemble deep learning architectures deployed on distributed cloud infrastructure. The proposed architecture integrates Real-Time Payment networks, Automated Clearing House systems, wire transfer protocols, remote deposit platforms, and physical cash management into a unified orchestration layer governed by convolutional and recurrent neural networks with blockchain-enabled smart contracts for immutable audit trails. Experimental validation demonstrates 99.1% fraud detection accuracy, 34% cost reduction in routing optimization, 47% improvement in liquidity velocity, and 78% reduction in manual exception handling. This research establishes a comprehensive paradigm for enterprise-scale treasury modernization combining artificial intelligence, distributed computing, and financial network integration.
Copyright (c) 2024 International Journal of Scientific Research in Computer Science, Engineering and Information Technology https://creativecommons.org/licenses/by/4.0
Smart Health Insurance Premium Calculation Using Wearable IoT Data
In today’s time, health insurance premiums have become generalized. They are mostly calculated using broad demographic factors such as age, gender, and medical history, which can make the premium unfair or inaccurate for many people. To address this problem, we came up with the idea of a Smart Health Insurance Premium System. The basic concept is to use wearable IoT devices to capture real-time lifestyle and health data, including sleep quality, physical activity, heart rate patterns, calorie expenditure, and habit indicators such as smoking or alcohol consumption. The collected data is processed to generate a point-based score for each user. Based on this score, the system dynamically determines a fair and customized insurance premium. In addition, the system provides lifestyle improvement suggestions with the goal of reducing long-term health risks and lowering future premium values. This proposed framework shows how combining IoT, data analytic, and health monitoring can create a more accurate, transparent, and user-centric insurance model, bridging the gap between traditional static premiums and modern data-driven healthcare systems.
Copyright (c) 2026 International Journal of Scientific Research in Computer Science, Engineering and Information Technology https://creativecommons.org/licenses/by/4.0
A Novel Hybrid Data Mining Framework for Early-Stage Aneurysm Detection and Risk Pattern Recognition
Intracranial aneurysms pose a serious global health challenge due to the risk of rupture leading to subarachnoid hemorrhage, which causes significant morbidity and mortality rates[1]. Early identification of aneurysms before symptom onset is crucial for timely clinical intervention and better patient outcomes[2]. Advances in non-invasive imaging techniques such as Time-Of-Flight Magnetic Resonance Angiography (TOF-MRA) and Computed Tomography Angiography (CTA) have facilitated widespread screening, although the large and complex imaging datasets pose diagnostic challenges to clinicians[3][4]. Recent progress in deep learning (DL) and hybrid data mining frameworks—leveraging supervised, weakly supervised, and self-supervised learning—has spurred the development of automated aneurysm detection systems. These systems further integrate anatomical priors, attention mechanisms, transformer architectures, and heuristic post-processing. This paper reviews the latest hybrid frameworks, evaluates their diagnostic efficacy, and discusses persisting challenges and emerging opportunities toward robust, interpretable, and scalable aneurysm detection solutions .
Transfer Learning-Based Recognition of Bhutanese Sign Language Digits Using Deep CNNs
The hearing and speech-impaired community uses hand gesture-based communication media to communicate with general public. However, the general public finds it difficult to communicate with them due to their difficulties in understanding sign digits, thereby creating a communication gap between the general public and the hearing-impaired community. Therefore, this paper proposes three pre-trained (VGG16, ResNet50, MobileNet) models to train models on the Bhutanese sign digit dataset. In this study, two different datasets (Bhutanese sign digit and Turkish sign digit) were merged and used to train the models. The rationale for merging the datasets is that both datasets use the same representation of sign gestures and have different variations of images. The dataset was split into train and test sets with a ratio of 80:20. The VGG16 network architecture outperformed the other two models with the training and testing accuracy of 96.72% and 95.85%. The trained model was integrated with the Django framework to create a web application for digit recognition.
Convolutional Neural Network and Human Age Detection : A Pilot Study
The problem of age detection in age required job has become a challenging issue in contemporary times. Job seekers falsify their ages in order to secure jobs within the required age limits. Agetoolpie, one of the existing sophisticated age prediction systems available online, captures images improperly. Thus, this research is aimed at designing a deep model to predict age of an individual, using features from image. To achieve this aim, Cross Industrial Standard Process Data Mining (CRISP-DM) is the methodology adopted. Dataset acquired from Kaggle machine learning data repository was used to train & test the system, while the system predicts and from the captured image it predicts the age of individuals based on the Convolutional neural network (CNN or ConvNet) algorithm implemented on the system. The performance of the proposed system, ConvNet AgeDet, was compared with that of Agetoolpie in terms of precision, sensitivity, specificity, and accuracy. The result shows that the proposed system has a better age prediction accuracy of 71% than Agetoolpie’s 67%. The study concludes that artificial intelligence models are capable of solving issues of age falsification. The ConvNet AgeDet is recommended to organizations interested in getting the real ages of job applicants. Researchers are enjoined to delve more into AI-driven age detection and prediction.
A Comparative Study of the Particle Swarm Optimization and Genetic Algorithm for Software Evolution Process
The software industry increasingly emphasizes the improvement of software quality. Modern development practices recognize that software should be built and maintained through a well-defined, structured process, consisting of organized and systematic steps for designing, developing, and evolving software systems. Petri net is applied for basic block of the software process. Path-based testing supports the software process by systematically identifying key execution paths to improve test planning and coverage. Using optimization techniques like Particle Swarm Optimization (PSO) helps efficiently generate test paths, enhancing the quality and effectiveness of process-level testing activities. Search-based optimization techniques improve decision making in the software process by transforming tasks such as cost estimation, release planning, and test generation into optimization problems. These methods efficiently explore large solution spaces to find near optimal solutions. Genetic algorithm (GA) is particularly effective due to their scalability and ability to handle multiple objectives, enhancing process efficiency, resource allocation, and software quality. In this paper, we present the development of a workflow software process modeled using Petri Nets. The approach helps in analyzing, controlling, and improving process execution within software development. We have proposed path testing technique applies PSO and GA to efficiently generate test paths that satisfy the all-uses criterion, improving planning and coverage in the software process. Comparative results show that PSO can achieve complete def–use path coverage with fewer generations than GA, enhancing testing efficiency and process effectiveness. The software industry increasingly emphasizes the improvement of software quality. Modern development practices recognize that software should be built and maintained through a well-defined, structured process, consisting of organized and systematic steps for designing, developing, and evolving software systems. Petri net is applied for basic block of the software process. Path-based testing supports the software process by systematically identifying key execution paths to improve test planning and coverage. Using optimization techniques like Particle Swarm Optimization (PSO) helps efficiently generate test paths, enhancing the quality and effectiveness of process-level testing activities. Search-based optimization techniques improve decision making in the software process by transforming tasks such as cost estimation, release planning, and test generation into optimization problems. These methods efficiently explore large solution spaces to find near optimal solutions. Genetic algorithm (GA) is particularly effective due to their scalability and ability to handle multiple objectives, enhancing process efficiency, resource allocation, and software quality. In this paper, we present the development of a workflow software process modeled using Petri Nets. The approach helps in analyzing, controlling, and improving process execution within software development. We have proposed path testing technique applies PSO and GA to efficiently generate test paths that satisfy the all-uses criterion, improving planning and coverage in the software process. Comparative results show that PSO can achieve complete def–use path coverage with fewer generations than GA, enhancing testing efficiency and process effectiveness.
Fault-Tolerant BMS Modernization in Precision-Controlled Scientific Facilities: Zero-Downtime Migration Architectures
High-precision research facilities demand uninterrupted environmental control during Building Management System (BMS) modernization, yet legacy EcoStruxure platforms with outdated databases and field controllers pose cybersecurity and support risks. Conventional migration approaches relying on planned downtime are incompatible with continuous operations. This paper presents a novel live migration framework enabling zero-downtime transitions while preserving historical trend data and maintaining critical control loops. The methodology integrates parallel infrastructure shadowing, state-preserving database migration, and contingency-driven manual override orchestration to ensure uninterrupted operation of chilled water systems, air handling units, and precision environmental controls. Implementation in a high-precision astronomical facility achieved zero unplanned service interruptions, full preservation of fifteen years of historical records, environmental stability within ±0.008°C, and 99.999% availability during migration. The framework establishes replicable patterns for mission-critical environments—including semiconductor fabs, pharmaceutical clean rooms, and quantum computing centers—providing a validated, risk-free approach to BMS modernization where operational continuity and environmental precision are non-negotiable.
zero-downtime infrastructure modernization
A Smart Energy Consumption System Architecture for Sustainable Semiconductor Manufacturing and AI Workload Operations
Energy consumption in the semiconductor industry, particularly from AI chip fabrication and high-performance computing (HPC) workloads, has risen dramatically, driven by exponential growth in AI model training and inference demands. Traditional energy management systems are inadequate to balance cost, sustainability goals, and performance requirements in such high-complexity environments. This paper proposes a novel Smart Energy Consumption System Architecture (SECSA) tailored for semiconductor fabrication plants (fabs), AI chip consumers, and data center operations supporting AI workloads. SECSA integrates real-time energy monitoring, predictive analytics, hybrid control strategies, renewable energy orchestration, and demand response optimization. Through simulation and architectural analysis, we demonstrate how SECSA can reduce energy costs by up to 35%, lower carbon emissions, improve grid reliability participation, and enable energy-aware workload scheduling. We present design principles, modeling frameworks, integration strategies, and evaluation results showing feasibility and advantages over traditional energy systems.
Copyright (c) 2025 International Journal of Scientific Research in Computer Science, Engineering and Information Technology https://creativecommons.org/licenses/by/4.0
Hybrid ML Models for High-Fidelity Customer Behavior Prediction Using CRM Data Streams and Cloud-Native Feature Stores
Customer relationship management platforms generate continuous streams of behavioral signals that reveal how individuals browse, evaluate, purchase, and disengage across digital and physical channels, yet many predictive systems continue to rely on static historical datasets and isolated algorithms that inadequately capture temporal dependencies, contextual shifts, and cross channel interactions, leading to delayed insights and inconsistent personalization outcomes. This study argues that high fidelity customer behavior prediction cannot be achieved through incremental model tuning alone but requires an integrated architectural approach that unifies streaming data ingestion, governed feature engineering, and complementary learning paradigms within a scalable cloud environment. To address this need, the paper introduces a hybrid machine learning framework that combines real time CRM data streams with cloud native feature stores to enable low latency feature computation, standardized feature reuse, and reproducible experimentation across analytical teams, while simultaneously supporting both batch and online inference workflows. The proposed design incorporates heterogeneous models including gradient boosted trees, sequence aware deep neural networks, and probabilistic estimators to capture short term intent signals, long term behavioral trends, and rare anomaly patterns that often precede churn or conversion events. Empirical patterns suggest that the coordinated use of shared feature repositories and hybrid ensembles improves predictive stability, reduces feature drift, enhances generalization under evolving customer behavior, and shortens decision latency when compared with single model or siloed architectures. In addition to accuracy gains, the framework emphasizes operational reliability through feature lineage tracking, version control, governance checkpoints, and elastic cloud orchestration, thereby strengthening explainability, auditability, and scalability for enterprise deployment. The findings indicate that feature stores and hybrid modeling should be regarded as structural capabilities that transform how customer intelligence systems are designed rather than as auxiliary implementation choices. By synthesizing advances in machine learning systems, real time data engineering, and customer analytics, this research contributes a unified architectural blueprint, methodological guidance, and evaluation protocol that can inform future studies on personalization, churn prevention, and decision intelligence, offering both scholars and practitioners a practical foundation for building trustworthy and high performance behavioral prediction platforms in modern cloud native ecosystems.
Real-Time Enterprise Data Harmonization Using Graph Neural Networks for Cross-System Integration and Customer Intelligence
Enterprise organizations increasingly operate within highly distributed digital environments that integrate enterprise resource planning platforms, customer relationship management systems, human capital management solutions, cloud-native analytics stacks, and real-time streaming infrastructures. This architectural fragmentation introduces persistent challenges in achieving consistent, accurate, and continuously synchronized representations of enterprise entities and customer identities. This study proposes a novel framework for real-time enterprise data harmonization using graph neural networks to enable adaptive cross-system integration and scalable customer intelligence generation. The proposed approach models heterogeneous enterprise datasets as dynamic relational graphs, capturing both structural dependencies and evolving semantic relationships across operational systems. By embedding real-time ingestion pipelines, streaming graph construction, and iterative graph neural network inference, the framework supports continuous entity resolution, contextual attribute alignment, and trust-aware data consolidation. Empirical evaluation across representative enterprise integration scenarios demonstrates substantial improvements in harmonization precision, latency reduction, and downstream analytical enrichment when compared to traditional deterministic matching and probabilistic reconciliation techniques. Furthermore, the framework introduces a governance-oriented feedback loop that ensures traceability, auditability, and policy-conformant data propagation across interconnected systems. The findings establish graph neural networks as a foundational paradigm for next-generation enterprise integration architectures, enabling organizations to achieve resilient interoperability, enhanced customer intelligence, and sustained data quality in real-time operational contexts.
A Distributed AI and IoT Fusion System for Predictive Patient Response Analytics in Cloud-Based Clinical Operations
Healthcare delivery systems are increasingly challenged by the growing complexity of clinical operations, rising patient volumes, heterogeneous data streams, and the demand for timely, personalized medical interventions. Recent advancements in artificial intelligence and Internet of Things technologies have introduced new opportunities to enhance predictive analytics, clinical decision support, and operational efficiency. However, existing solutions often remain constrained by centralized processing models, fragmented data pipelines, limited interoperability, and insufficient real-time responsiveness. This study proposes a distributed artificial intelligence and Internet of Things fusion system designed to enable predictive patient response analytics within cloud-based clinical operations. The proposed framework integrates edge-based sensing, distributed machine learning inference, cloud-native orchestration, and adaptive feedback mechanisms to establish a scalable, resilient, and privacy-aware clinical intelligence architecture. By combining real-time physiological monitoring, contextual clinical data, and continuous learning pipelines, the system supports dynamic patient state assessment, early risk identification, and personalized therapeutic response modeling. This research develops a layered architectural blueprint that incorporates data acquisition, distributed analytics, governance and compliance enforcement, and closed-loop optimization processes, thereby aligning predictive intelligence with regulatory, ethical, and operational constraints. A comprehensive methodological approach is presented, including system design principles, model training strategies, latency optimization techniques, and validation protocols. Empirical patterns derived from simulated clinical workflows and distributed analytics benchmarks indicate substantial improvements in predictive accuracy, inference latency, system scalability, and fault tolerance when compared with conventional centralized architectures. The findings suggest that distributed intelligence paradigms can significantly enhance responsiveness, safety, and operational resilience in cloud-based healthcare environments. This study contributes a foundational framework for next-generation clinical analytics platforms, offering practical design guidance and theoretical insights for researchers, healthcare practitioners, and system architects seeking to advance intelligent, patient-centric, and data-driven healthcare operations.
A LLM-Augmented Code Generation and Optimization Framework for API-First Integration in Java and Python Microservice Pipelines
The rapid evolution of microservice-oriented architectures and API-first development paradigms has introduced unprecedented complexity in enterprise integration landscapes, particularly in heterogeneous technology environments dominated by Java and Python service pipelines. While large language models (LLMs) have demonstrated promising capabilities in automated code synthesis, their systematic integration into production-grade software engineering workflows remains underexplored. This paper proposes a comprehensive LLM-augmented framework for intelligent code generation, validation, optimization, and continuous refactoring tailored for API-first microservice pipelines. The framework embeds generative models within structured development lifecycles, incorporating architectural governance, semantic validation, security compliance enforcement, performance optimization, and observability-driven feedback loops. By unifying contract-driven API design, automated code scaffolding, adaptive optimization heuristics, and runtime telemetry-based refinement, the proposed model enables accelerated development cycles, reduced integration defects, and enhanced system resilience. Experimental evaluation across representative Java Spring Boot and Python FastAPI ecosystems demonstrates measurable improvements in development velocity, integration quality, security compliance, and runtime efficiency. The findings establish a scalable blueprint for operationalizing LLM-assisted software engineering within mission-critical enterprise integration platforms.
Third-Party AI Vendor Risk: Developing Assessment Frameworks for Machine Learning Service Providers
The rapid integration of artificial intelligence (AI) and machine learning (ML) technologies into enterprise operations has fundamentally altered the vendor risk landscape. Organizations increasingly rely on third-party AI vendors for mission-critical processes such as fraud analytics, credit decisioning, underwriting, customer segmentation, and transportation route optimization. Traditional vendor risk methodologies, designed around static information systems, fail to account for model-dependent variability, training data sensitivity, algorithmic opacity, and the dynamic behaviors inherent to continuously learning systems. This research develops a comprehensive assessment framework explicitly tailored to the characteristics of AI vendors. Drawing from more than two hundred vendor assessments performed across the banking, insurance, and transportation sectors, the study identifies four emergent risk domains uniquely associated with AI service providers: model transparency risks, data lineage and provenance uncertainties, algorithmic bias vulnerabilities, and drift susceptibility associated with real-time or periodic retraining cycles. The proposed hybrid conceptual-quantitative framework integrates governance evaluation, model assurance, fairness testing, explainability analysis, and continuous monitoring. The methodology introduces novel mechanisms for assessing proprietary models without requiring model access and presents a composite risk scoring model for quantifying AI-specific vendor exposures. Empirical validation at a Fortune 500 financial services organization demonstrates that the framework enables materially improved risk visibility, more defensible regulatory compliance, and more accurate long-term vendor management strategies. This work provides the first end-to-end, AI-focused vendor risk assessment methodology capable of operating under real-world constraints of opacity and limited disclosure, addressing a significant and urgent gap in third-party risk management practices.
Integrating Artificial Intelligence and Learning Sciences to Reduce Cognitive Load and Achievement Gaps in Data-Driven K-12 Instructional Systems
Persistent achievement gaps in K–12 education are increasingly linked to unequal cognitive load imposed by data-driven instructional systems that scale content without adequately modeling learner cognition. This paper proposes a technical framework that integrates artificial intelligence with established learning sciences to systematically reduce extraneous cognitive load while improving learning equity across diverse student populations. We introduce a novel algorithm, Cognitive Load–Adaptive Instructional Network (CLAIN), which combines graph-based knowledge tracing, multimodal cognitive state inference, and reinforcement learning–driven content sequencing. CLAIN dynamically adjusts instructional complexity, pacing, and representational modality based on real-time estimates of intrinsic, extraneous, and germane cognitive load.The proposed approach is empirically evaluated against widely used baselines, including Bayesian Knowledge Tracing, Deep Knowledge Tracing (LSTM-based), Item Response Theory–driven adaptive testing, and contextual multi-armed bandit recommenders. Using longitudinal classroom datasets with heterogeneous socio-academic profiles, we compare models across predictive accuracy, learning efficiency, and equity-focused metrics such as variance reduction in outcome distributions across demographic groups. Results show that CLAIN achieves superior performance, with lower cumulative cognitive load indices, faster concept mastery curves, and statistically significant reductions in achievement dispersion. Graph-based visualizations illustrate how adaptive pathways differ across learners and how the proposed model reallocates instructional effort to high-risk cognitive bottlenecks. Beyond performance gains, the paper contributes a technically grounded bridge between cognitive load theory and AI system design, demonstrating how learning-science constraints can be encoded directly into model architectures and optimization objectives. The findings suggest that cognitively aware AI systems can move beyond accuracy-centric personalization toward equity-oriented instructional intelligence, offering a scalable pathway for data-driven K–12 platforms to close achievement gaps without increasing learner burden.
QuantileFlow: A Unified and Accelerated Quantile Sketching Framework for Anomaly Detection in Streaming Log Data
Quantile sketching enables scalable estimation of tail latencies such as the 95th and 99th percentiles without storing full streams, making it a practical foundation for anomaly detection in observability pipelines. We introduce QuantileFlow, a unified framework that standardizes ingestion, query, merge, and serialization across multiple quantile sketch families. Using the LogHub HDFS v1 dataset in a production style streaming pipeline, we process 575,059 latency events end to end and benchmark accuracy, memory footprint, throughput, and runtime under identical workloads. We also microbenchmark insertion by adding 1,000,000 log normal samples and attribute execution time to key internal routines. Across experiments, DDSketch provides the strongest throughput while preserving tail fidelity through relative error guarantees. HDR Histogram maintains stable precision across wide dynamic ranges but is more sensitive to configuration and incurs higher overhead. MomentSketch is most compact in memory and efficient for smooth unimodal data, but its quantile accuracy degrades on heavy tailed or multimodal streams. Finally, we show an optimized QuantileFlow DDSketch implementation that improves throughput by 44% over DataDog’s official implementation and 2.67 times a prior internal version.
Cybersecurity Compliance as a Source of Competitive Advantage in Technology Markets
Cybersecurity compliance is increasingly viewed not merely as a regulatory obligation but as a strategic resource capable of generating competitive advantage in technology-driven markets. As digital platforms, cloud services, and data-intensive business models expand, firms operate under heightened scrutiny from regulators, customers, and partners regarding data protection, privacy, and operational resilience. This study examines how adherence to cybersecurity compliance frameworks can be leveraged to enhance market positioning, customer trust, and long-term firm performance. Drawing on strategic management and information systems literature, the paper conceptualizes cybersecurity compliance as an intangible organizational capability that signals reliability, reduces information asymmetry, and lowers perceived transaction risk. Rather than treating compliance as a cost center, leading technology firms integrate standards such as ISO/IEC 27001, NIST-aligned controls, and sector-specific regulations into product design, governance structures, and innovation processes. The analysis highlights three key mechanisms through which compliance creates competitive advantage: trust differentiation, operational efficiency, and market access. First, demonstrable compliance strengthens brand credibility and customer confidence, particularly in markets where data breaches and service disruptions are prevalent. Second, standardized security controls streamline internal processes, improve incident response, and reduce the likelihood and impact of cyber incidents, thereby lowering long-term operational costs. Third, compliance enables entry into regulated or high-assurance markets, facilitating partnerships, cross-border operations, and procurement opportunities that are inaccessible to non-compliant competitors. The paper also discusses dynamic considerations, emphasizing that competitive advantage arises not from static certification alone but from continuous compliance, monitoring, and adaptation to evolving threat landscapes. By framing cybersecurity compliance as a strategic investment rather than a defensive necessity, the study contributes to debates on digital trust, governance, and competitive strategy in technology markets. The findings offer practical insights for managers seeking to align cybersecurity governance with business objectives and for policymakers aiming to design compliance regimes that enhance, rather than hinder, innovation and competitiveness. Overall, the research underscores that proactive, well-governed cybersecurity compliance supports sustainable value creation by aligning risk management with innovation, strengthening stakeholder relationships across supply chains, improving strategic transparency, enhancing investor confidence, and reinforcing organizational resilience in fast-moving, trust-sensitive global technology ecosystems and competitive digital markets worldwide today.
How Do Credit Card Fraud Detection Models Make Decisions? An Explainable Machine Learning Analysis
Credit card fraud detection is an important application of machine learning, where incorrect predictions can lead to financial losses and reduced customer trust. Although many fraud detection models achieve high accuracy, their decision-making process is often unclear, which limits their use in real financial systems. This paper presents an experiment-based study of machine learning models for credit card fraud detection with a focus on explainability. Logistic Regression, Random Forest, and Gradient Boosting models are evaluated using a real-world credit card transaction dataset with extreme class imbalance. Model performance is assessed using precision, recall, F1-score, ROC-AUC, and confusion matrix analysis, with special emphasis on fraud recall. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is applied during training. To understand model decision-making, SHAP-based global and local explanations are generated for the best-performing model. Experimental results show that Random Forest provides the most balanced performance, achieving high fraud recall while controlling false positives. The explainability analysis shows that fraud predictions are driven by a small number of influential features. The study highlights the importance of evaluating both performance and explainability when deploying fraud detection systems in financial environments.
AI and Machine Learning in Cybersecurity: Applications, Challenges and Future Directions
The types of cyber threats are becoming more and more complicated, so the classical security method which rely on signature is less applicable. Artificial Intelligence (AI) and Machine Learning (ML) are a few of technology giant for the world of cybersecurity, allowing the formulation of an effective plan to proactively fight these threats. The aim of this paper is to offer a comprehensive summary on AI/ML in cybersecurity. In this paper, we will examine the current AI/ML technology applied to cybersecurity. This is paper address a few of the dominant challenges in AI/ML, such as adversarial threats, data privacy .1 Introduction The rapid growth and increasing complexity of computing systems have led to significant advances and adaptation in artificial intelligence (AI) and machine learning (M L). This study also attempts to synthesize the current trends in the field by conducting a thorough survey of the literature review. Finally, the future directions in the field include Explainability, AI for security adaptation, and the need for integration with AI. This study highpoint AI/ML is not the silver bullet for the field of cybersecurity, it is imperative that strategic use of AI/ML is made in the design of next-generation cybersecurity systems.
Kannada Speakers Speech Emotion Recognition Using Deep Learning Technique: Anger, Fear, Sad and Neutral state
In this paper, we have experimenting to identifying the Kannada emotional speech signals recognition using deep learning technique. Speech Emotion Recognition (SER) is a hot research topic in the field of HCI (Human Computer Interaction). To experiment we have created our own Kannada Emotional Speech corpus recording in different android mobile phone, with sampling rate 48 kbps and mono channel and speakers are from the Karnataka and Tamil Nadu boarder region, whose accents is a mixer of Kannada, Tamil and Telugu languages. The emotions are anger, fear, sad and neutral is considered. Totally we have 440 Kannada emotional speech signals. To use this speech signal data in our experiment, we have first down sampled all speech signals into 16 kbps, mono channel and stored as wav files. Then we pass the each sample signals into the pre-emphasize phase, then framing and windowing is done, then we extracted features from each voice frame signal like MFCC, Chroma and ‘Mel’ and then passed to CNN (Convolution Neural Network) to train and testing the voice signals. The CNN is capable to handle small amount of data for training, and with smaller number of parameters. The average Kannada emotion recognition accuracy rate of 93.77% is achieved. All computations are done using Python programming language and used Python libraries like Librosa, Keras, pyaudio, soundfile, sklearn.
Design and Development of an AI-Powered Online Doctor Consultation and Booking System
The Healthcare Management Platform is a web-based E-Health Center created under the MERN stack (MongoDB, Express.js, React.js, Node.js) to promote the accessibility and efficiency of healthcare. This paper is a quantitative study aiming to measure the system performance in relation to the user engagement metrics, the rate of successful booking of appointments, the accuracy of chatbots response, and the ability of the system to handle loads. The platform facilitates the interaction between patients and doctors by offering a simple system of booking appointments, a chatbot to analyze the symptoms, and a role-based dashboard to doctors, patients, and administrators. In pilot usability test which included 200 users, the system was found to have a reduction in the time spent in scheduling an appointment by 73 percent over the traditional phone-based systems. When the chatbot was tested against medical databases, the accuracy rate of the symptom-based recommendation was 87%. Moreover, the average response time of the server was 320ms when the simulated load of 5,000 users became active simultaneously, which indicates the scalability of the platform. It was also revealed that the research recorded a 42% growth in patient engagement in terms of repeated platform use and number of interactions per session. Security tests established that it is 99.8 percent effective against the general cyber criminal attacks, assuring data privateness. Moreover, 91 percent of respondent users had a positive user experience, which mentioned simplicity of use, simplified information and reduced time of waiting. These findings indicate the potential of the platform in ensuring access to healthcare and enhance efficiency with a digital transformation. The further development of the work will be aimed at AI-based diagnostics, the integration of telemedicine, and the safety of patient data on a blockchain platform to expand the capabilities of the platform. The results highlight the importance of healthcare solutions based on technologies in enhancing patient outcomes and efficiency in the functioning of a modern healthcare ecosystem.
AI-Driven Adaptive Security Frameworks for Decentralized Edge Computing in IoT Ecosystems
The rapid proliferation of Internet of Things (IoT) devices and the growing adoption of decentralized edge computing architectures have significantly transformed data processing, latency optimization, and real-time decision-making across smart ecosystems. However, this paradigm shift has also introduced complex security challenges, including heterogeneous device vulnerabilities, dynamic network topologies, limited computational resources, and increased exposure to cyberattacks at the edge. This review paper examines AI-driven adaptive security frameworks as a promising approach for securing decentralized edge computing environments within IoT ecosystems. It systematically analyzes how artificial intelligence techniques—such as machine learning, deep learning, federated learning, and reinforcement learning—enable real-time threat detection, anomaly identification, intrusion prevention, and automated security policy adaptation at the network edge. The review highlights architectural models that integrate AI with edge and fog computing to achieve scalable, low-latency, and privacy-preserving security mechanisms while reducing reliance on centralized cloud infrastructures. Furthermore, it evaluates existing frameworks, recent case studies, and emerging trends, emphasizing challenges related to data privacy, model robustness, explainability, energy efficiency, and interoperability. By synthesizing current research and identifying open research gaps, this paper provides a comprehensive foundation for developing resilient, intelligent, and adaptive security solutions that can support the evolving requirements of decentralized IoT edge computing ecosystems.
A Comprehensive Survey of Distributed Consensus Algorithms for Wireless Sensor Networks
Distributed consensus represents a vital approach in Wireless Sensor Networks. By sharing local data, it allows a group of resource-constrained sensor nodes to collaborate and agree on a common value or decision. Consensus methods improve scalability, reliability, and energy efficiency by eliminating the need for centralized data processing systems. This research provides an in-depth review of distributed consensus approaches in WSNs. We examine traditional average and binary consensus algorithms, cluster-based and topology-aware methods, energy efficient solutions, and secure consensus protocols. Architectural frameworks, performance metrics, and an extensive comparative analysis are also presented. Based on convergence, communication costs, fault tolerance and application, a summary table contrasts important algorithms. The survey highlights significant areas where research is lacking and proposes future pathways, underlining the necessity of creating flexible, secure, and effective consensus solutions for next-generation wireless sensor networks (WSNs).
Development of Decision Support System for Duck and Chicken Culinary Businesses Based On Good Manufacturing Practices Using Data Mining
This study presents the development of a Decision Support System (DSS) for duck and chicken culinary businesses through the integration of Good Manufacturing Practices (GMP) and data-driven analysis using K-Means clustering and Association Rule Mining. The system processes production, nutritional, economic, and sensory data collected from processed poultry products to support business decision-making. K-Means clustering successfully segmented 32 samples into four meaningful product groups based on meat attributes, cooking characteristics, cost levels, and quality evaluations. The clustering results distinguished fast-cooking, low-cost chicken products from slow-cooked, high-cost duck dishes, revealing clearly differentiated culinary market segments. Association rules further identified strong correlations among product type, fat level, production cost, and cooking duration, providing valuable knowledge for identifying premium or mainstream culinary categories. When combined, the clustering and rule-based insights enhance GMP implementation by supporting standardization, quality control, and traceability throughout food preparation processes. The resulting DSS enables producers to optimize resource usage, refine menu strategies, and strengthen product consistency while aligning with food safety and hygiene standards. Overall, this research demonstrates that integrating GMP with AI-based analytics provides a practical and scalable approach for improving governance, operational efficiency, and competitiveness in small and medium-scale duck and chicken culinary enterprises.
Advances in Hybrid Machine Learning and Physics-Based Models for Enhanced Reservoir Simulation
Reservoir simulation is critical in optimizing resource extraction and ensuring the efficient management of subsurface reservoirs. While rooted in robust physical principles, traditional modeling approaches often face limitations in handling complex reservoir dynamics, computational intensity, and data sparsity. Recent advances in hybrid methodologies combining machine learning (ML) and physics-based models offer transformative solutions to these challenges. This paper explores the evolution and integration of hybrid approaches in reservoir simulation, highlighting key developments such as physics-informed neural networks, surrogate modeling, and adaptive workflows. The benefits of these models, including enhanced predictive accuracy, computational efficiency, and adaptability, are examined alongside the challenges of implementation, such as computational demands and data quality constraints. Practical implications for the industry are discussed, particularly in improving decision-making and optimizing production strategies. The paper concludes with recommendations for further research and industry adoption, emphasizing the need for interdisciplinary collaboration, advancements in explainable artificial intelligence, and the development of standardized frameworks. These hybrid approaches mark a significant milestone in the evolution of reservoir simulation, offering innovative pathways for sustainable and efficient resource management.
Software Testing Techniques and Strategies
In the Software Development Life Cycle, software testing is the most important stage. The report states that the tested software undergoes several stages, including test preparation, test execution, testing analysis, problem reporting and tracking, and bug fixing. Many studies have been conducted in the past to optimize the testing process in general to enhance software quality as soon as possible. Software testing is the process of running a program to find software defects (errors or other issues). Testing can analyse the software product to evaluate its characteristics and identify any differences between the recommended and actual conditions. Software testing helps decrease errors and reduces software costs. This paper gives the overview of various software testing methods.
Sustainable Healthcare Infrastructure Performance Metrics for Long Term Asset Management and Value Creation
Sustainable healthcare infrastructure requires performance metrics that extend beyond initial capital delivery to support long term asset management, service continuity, and public value creation. This study examines the development and application of sustainability oriented performance metrics for healthcare facilities across their operational life cycle. It integrates insights from infrastructure asset management, health systems planning, and sustainability science to propose a structured metric framework that aligns technical performance, financial stewardship, and social outcomes. The analysis emphasizes the limitations of traditional project focused indicators that prioritize cost, schedule, and short-term efficiency while neglecting operational resilience and lifecycle impacts. Key metric domains are identified, including asset condition and reliability, energy and resource efficiency, environmental emissions, patient safety, service accessibility, and workforce wellbeing. Financial metrics addressing lifecycle cost, maintenance optimization, and risk adjusted value preservation are examined alongside clinical and community outcome indicators. The study highlights the role of digital technologies in enabling continuous performance measurement. Building management systems, asset information models, and data analytics platforms are discussed as tools for real time monitoring, predictive maintenance, and evidence-based decision making. By linking operational data with governance and reporting structures, these tools support transparency, accountability, and informed reinvestment strategies. Sustainability metrics are positioned as decision support instruments rather than static reporting requirements. The framework emphasizes feedback loops that allow infrastructure managers to adapt asset strategies in response to changing demand, climate risks, regulatory expectations, and technological advances. Particular attention is given to aligning performance metrics with long term health system goals, including equity, resilience, and carbon reduction commitments. Overall, the abstract argues that sustainable healthcare infrastructure performance metrics are essential for transforming assets from cost centers into strategic enablers of health system value. By embedding lifecycle oriented, multidimensional metrics into asset management practice, healthcare organizations can improve reliability, optimize resource use, and protect public investment over time. The proposed approach provides practical guidance for policymakers, facility managers, and investors seeking to enhance long term asset performance, fiscal sustainability, and societal value creation within complex and resource constrained healthcare environments globally, across diverse institutional contexts and evolving demographic, economic, and epidemiological conditions worldwide sustainably.
Integrating Healing Centered Design Principles into Diagnostic and Laboratory Facility Planning
Integrating healing-centered design principles into diagnostic and laboratory facility planning has gained increasing attention as healthcare systems recognize the influence of physical environments on patient experience, staff wellbeing, and clinical performance. Diagnostic and laboratory spaces are traditionally engineered for efficiency, biosafety, and regulatory compliance, often overlooking psychosocial factors that shape stress, trust, and cognitive functioning. This paper explores how healing-centered design can be systematically embedded within the planning and delivery of diagnostic and laboratory facilities without compromising technical rigor or safety standards. Drawing on interdisciplinary literature spanning healthcare architecture, environmental psychology, laboratory design, and health services research, the study synthesizes evidence on spatial configuration, lighting, acoustics, biophilic elements, wayfinding, and material choices. The analysis demonstrates that environments designed to reduce anxiety, enhance comfort, and support dignity can improve patient cooperation, diagnostic accuracy, and staff productivity while mitigating fatigue and error. Particular emphasis is placed on laboratory workflows, specimen collection areas, waiting zones, and staff workspaces where stress and cognitive load are highest. The paper further examines regulatory and operational constraints, highlighting strategies for aligning healing-centered features with infection prevention, biosafety, and quality assurance requirements. Case-informed insights illustrate how modular layouts, natural light integration, noise control, and culturally responsive design can be adapted to diverse health system contexts, including resource-constrained settings. A conceptual planning framework is proposed that integrates healing-centered objectives into early needs assessment, functional programming, stakeholder engagement, and post-occupancy evaluation. By reframing diagnostic and laboratory facilities as therapeutic environments rather than purely technical spaces, the study provides practical guidance for planners, architects, laboratory managers, and policymakers. The findings support a shift toward human-centered infrastructure that enhances diagnostic effectiveness, workforce resilience, and patient trust, while maintaining compliance, efficiency, and long-term operational sustainability. Such integration contributes to more compassionate, equitable, and high-performing diagnostic services capable of meeting growing clinical demand and evolving expectations for quality, safety, and holistic care delivery. Future research should evaluate outcomes using mixed methods, longitudinal metrics, and equity indicators, ensuring design decisions are evidence informed, scalable, and culturally grounded, while supporting regulatory compliance, technological adaptability, continuous learning, and measurable improvements in diagnostic quality and patient-centered system performance.
Decentralized Produced Water Treatment Systems for Remote Energy Assets, a Conceptual Design Framework
Produced water (PW) is the largest by-product of oil and gas operations and presents significant management challenges for remote energy assets, where centralized treatment infrastructure is often impractical due to logistical, energy, and environmental constraints. This review proposes a conceptual design framework for decentralized PW treatment systems tailored to remote onshore and offshore operations. The framework emphasizes modular, scalable, and adaptable treatment units capable of addressing the variable physicochemical composition of PW, including high salinity, hydrocarbons, heavy metals, and naturally occurring radioactive materials (NORM). Treatment trains integrate physicochemical processes such as gravity separation, flotation, adsorption, membrane filtration, and advanced oxidation with bio-based polishing systems including biofilm reactors, constructed wetlands, and algal-bacterial consortia. This hybrid approach ensures robust contaminant removal, operational flexibility, and resilience under fluctuating flow rates and water quality typical of remote operations. The framework also considers off-grid energy solutions, leveraging renewable sources such as solar and wind to reduce operational carbon footprints, and incorporates resource recovery pathways, including nutrient extraction, biomass utilization, and salt recovery, supporting circular water management principles. Digital monitoring, automation, and AI-driven process optimization are highlighted as key enablers for real-time control, predictive maintenance, and adaptive operation, reducing risks associated with system failure in isolated locations. Environmental and safety considerations, including minimization of land use, chemical inputs, and residual waste management, are integrated into the design to ensure sustainable operation. Overall, the proposed conceptual framework demonstrates that decentralized PW treatment systems can provide a flexible, energy-efficient, and environmentally responsible solution for water management in remote energy assets. By combining modular engineering, hybrid treatment strategies, and digital intelligence, these systems enable fit-for-purpose water reuse, reduce dependence on freshwater resources, and enhance operational resilience, supporting sustainable and circular water management practices in water-stressed and isolated operational settings.
A Comprehensive Review of Sign Language Translation for Inclusive Education Systems
Sign Language Translation (SLT) has emerged as a critical technological enabler for inclusive education systems, aiming to bridge communication gaps between hearing-impaired learners and mainstream educational environments. Recent advancements in computer vision, deep learning, and artificial intelligence have significantly improved the accuracy, robustness, and real-time feasibility of sign language recognition and translation systems. These systems increasingly support isolated signs, continuous sign language, multilingual alphabets, and sentence-level translation, making them suitable for classroom integration, e-learning platforms, and assistive educational tools. This review paper presents a comprehensive analysis of recent research on sign language translation, with a particular focus on methods applicable to inclusive education systems. The paper systematically examines state-of-the-art techniques, including YOLO-based detection, CNN–LSTM hybrids, graph convolutional networks, attention mechanisms, and optimization-driven feature reduction approaches. Key research findings, challenges, and limitations are critically discussed, highlighting gaps related to scalability, dataset diversity, real-time deployment, and educational usability. By synthesizing current trends and insights, this review aims to guide researchers and educators toward the development of more effective, accessible, and learner-centered sign language translation systems for inclusive education.
A Comprehensive Review of Methods, Frameworks, and Domains for Metadata Extraction from Scientific Texts
Metadata extraction from scientific texts plays a crucial role in enabling efficient organization, retrieval, and analysis of scholarly knowledge. With the exponential growth of scientific publications across disciplines, manual metadata annotation has become infeasible, motivating the development of automated and semi-automated extraction techniques. This review paper presents a comprehensive analysis of recent advances in metadata and structured information extraction from scientific documents. It explores traditional rule-based methods, machine learning approaches, deep learning architectures, and emerging large language model-based frameworks. The paper also examines domain-specific applications, including systematic reviews, digital libraries, scientific repositories, and open journal systems. By synthesizing findings from recent literature, this study highlights key research trends, strengths, and limitations of existing methods. Furthermore, it identifies major challenges such as document heterogeneity, semantic ambiguity, evaluation complexity, and human–machine collaboration. The review aims to provide researchers with a structured understanding of current methodologies and open research directions, thereby supporting the development of robust, scalable, and high-precision metadata extraction systems for scientific knowledge management.