Journal of Computer Science and Technology Studies
The Journal of Computer Science and Technology Studies (ISSN: 2709-104X) is a double-blind peer-reviewed, open-access journal published by Al-Kindi Center for Research and Development. It covers the latest developments in the broad areas of computer science and information technology. The journal offers readers free access to all new research issues relevant to computer science and information technology. While the journal strives to maintain high academic standards and an international reputation through the suggestions of the international advisory board, it welcomes original, theoretical and practical submissions from all over the world.
Al-Kindi Center for Research and Development
A Public-Source Comparative Case Study of the DIGS Framework for AI-Enabled Academic Advising Platform Selection
This public-source comparative case study examines whether the Digital Infrastructure Governance and Selection (DIGS) framework can support defensible decisions about AI-enabled academic advising platforms in higher education. Developed by Fatema Akter, DIGS combines noncompensatory assurance requirements with six lifecycle stage gates. The framework responds to three weaknesses in conventional procurement: aggregate scores can conceal failed safeguards, vendor assertions are often treated as verified evidence, and purchase decisions are separated from implementation outcomes. We applied seven decision domains to publicly available documentation: mission and service fit; architecture and interoperability; data governance and privacy; cybersecurity and resilience; accessibility and equity; lifecycle economics and viability; and implementation and benefit realization. Public evidence proved too limited for a complete governance audit. Advisor.AI was the only platform in the reviewed material with a dedicated product page, while disclosures about large language model providers, privacy compliance, cybersecurity certification, and accessibility were absent. The economic evidence was more informative. The sources attributed 70-85% of total AI cost to the operating environment and indicated that recurring run costs can exceed cumulative build costs within 12 to 24 months. A simulated fairness audit using the UCI Student Performance Dataset also produced differences in predicted positive rates by gender and parental education. These results show both the value and the limit of public-source evaluation: DIGS makes missing evidence visible, but institutions still need contractual disclosure, independent assurance, and controlled pilots before approving a platform.
Public-Source Comparative Case Study; DIGS Framework; AI-Enabled Academic Advising Platform Selection
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
Hub-Based Transportation Planning: Network Architecture and Configuration Governance
Direct transportation, where freight moves on a single leg from origin to destination, is the most intuitive planning model but is rarely the most cost-effective at scale. Hub-based transportation planning offers an alternative: by routing freight through intermediate consolidation points, shippers can exploit economies of scale on high-volume trunk lanes, reduce vehicle underutilization on low-density last-mile segments, and enable intermodal mode changes that would be impossible under direct-only planning. This paper examines hub-based transportation planning in enterprise transportation management systems, covering the network architecture that enables it, the decision logic by which the transportation proposal algorithm evaluates hub versus direct routing, the master data configuration that makes hub planning operational, the multi-stage planning sequence that coordinates inbound and outbound legs, and the cost-benefit framework that governs when hub routing delivers genuine value. We draw on logistics network design literature and enterprise systems implementation experience to argue that hub planning is not simply a routing variant but a distinct planning paradigm with its own configuration requirements, tradeoff structure, and governance implications. Poorly configured hub networks yield results worse than direct planning; well-configured ones systematically outperform direct planning in network cost and vehicle efficiency. The difference lies entirely in the quality of the configuration.
Hub-and-spoke networks; Logistics network design; Multi-stage transportation; Transshipment location; Transportation proposal
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
Intelligent Artificial Intelligence-Powered Cyber Threat Detection System for Cloud Security Enhancement
Network intrusion detection is a key part of keeping computer networks and the Internet of Things safe from cyber dangers. When it comes to decision-making, standard machine learning methods can only improve detection accuracy (ACC). This work introduces an explainable intrusion detection method that utilizes the CICIDS2017 dataset and depends on a Random Forest classifier (RF), LightGBM (Light Gradient Boosting Machine), and Voting Ensemble (VE). The data processing, feature selection, data balancing with SMOTE, and ensemble learning techniques of the proposed method are the foundation of intrusion detection ACC. At an ACC of 98.22%, the Voting Ensemble model outperforms the other models mentioned in the discussion (Multiple Baseline models), including the Autoencoder (AE), Decision Tree (DT), Deep Neural Network (DNN), and Logistic Regression (LR) classifiers. To further enhance model transparency and determine the most important network traffic features for attack prediction, SHAP-based explainable AI analysis is used. The proposed framework is able to identify influential traffic aspects that lead to attack prediction, subsequently leading to an accurate, scalable and trustworthy solution for current intrusion detection applications, thereby increasing the interpretability of cybersecurity.
Cyber security, Anomaly Detection, Network Security, Threat Detection, Intrusion Detection System, Machine Learning, CIC-IDS2017
Benchmarking Machine Learning Models for Software Project Schedule Overrun Prediction
Software projects frequently exceed their planned schedules because project performance is shaped by interacting technical, organizational, resource, risk, and stakeholder-related factors. This study benchmarks six regression algorithms—Linear Regression, Regression Tree, Random Forest, Support Vector Regression, Gradient Boosting Machine, and Deep Neural Network—to predict the percentage of schedule overrun in software projects and identify the model that provides the most favorable balance between predictive accuracy and interpretability. All six models were developed and evaluated using a publicly available dataset containing 4,517 software-project records. To ensure a consistent comparison, the models used identical predictors, default hyperparameter configurations, and a 10-fold cross-validation procedure. Predictive performance was assessed using root mean squared error, mean absolute error, correlation, and squared correlation. Gradient Boosting Machine achieved the lowest prediction errors, with an RMSE of 4.578 and an MAE of 3.503. Deep Neural Network produced nearly equivalent performance and recorded the highest squared correlation of 0.875. Random Forest ranked third, whereas Support Vector Regression produced the weakest performance under the adopted default settings. Interpretability analysis further identified Risk Factor, Duration Months, and Client Satisfaction Rating as the most consistently influential predictors across the interpretable models. The findings demonstrate the potential of ensemble and deep-learning methods to estimate the magnitude of schedule overrun using structured software-project data. Considering both prediction error and interpretability, Gradient Boosting Machine provided the strongest overall balance and represents a practical candidate for early-warning systems, risk-based project prioritization, schedule-recovery planning, and portfolio-level decision support. Future research should validate the models using independent organizational datasets, optimize their hyperparameters, and apply consistent explainability methods across all algorithms.
Artificial Intelligence; Machine Learning; Project Management; Schedule Overrun; Gradient Boosting Machine; Deep Neural Network; Predictive Modeling; Data-Driven Decision-Making
Identifying Critical Cybersecurity Threats Impacting Financial Stability in Investment Firms: A Qualitative Risk Management Perspective
Investment organizations now face greater cybersecurity risks as financial services become increasingly digitalized. Wherein the high-value transactions, sensitive client data, third-party platforms, and real-time operational systems are closely interconnected. The opinions of investment firm specialists on which specific risks pose the greatest threat to financial stability have not received as much attention as cyber risk, even though cyber risk has been thoroughly investigated using technological and quantitative approaches. This study used a qualitative case study to address the following research question: Which cybersecurity threats have the greatest impact on the financial stability of investment businesses? Semi-structured interviews with seven professionals in investment company settings were analyzed using thematic analysis informed by the Technology Acceptance Model, Risk Management Theory, and Contingency Theory. The findings demonstrate that the most serious threats are phishing, social engineering, third-party vendor risks, and the development of external threat vectors, as they have the potential to interfere with business operations, jeopardize data integrity, impair regulatory compliance, and erode investor confidence. The report argues that investment firms should view cybersecurity as a fundamental financial stability concern rather than a strictly technical function, and offers qualitative, theory-driven insights into cybersecurity threat prioritization.
Cybersecurity, financial stability, investment firms, phishing, qualitative case study, risk management, third-party risk
Copyright (c) 2026 Journal of Computer Science and Technology Studies
Feedback Control for Adaptive Language Model Routing Under Non-Stationary Workloads
Serving a stream of requests across large language models (LLMs) of differing cost and quality is an online allocation problem, usually framed as multi-armed bandits. We frame it as feedback control: a direct-acting Proportional–Integral–Derivative (PID) controller whose setpoint is the running fleet- average performance and whose bounded output adjusts each model’s allocation share, with requests routed by weighted sampling over the allocation vector. This work contributes (i) a stability analysis of the closed loop — bounded-input bounded-output behaviour by anti-windup, exponential convergence of the performance estimates via a Lyapunov function, and a persistent-excitation condition guaranteeing recoverability after a regime change; (ii) a closed-form, analysis-grounded automatic tuning rule requiring no per-dataset search; and (iii) an honest head-to-head against static, round-robin, random, epsilon-greedy, UCB1, Thompson sampling, and the non-stationary bandits Sliding-Window UCB and Discounted UCB, on GSM8K with a checkable exact-match reward, reporting inference cost and request latency alongside quality. Under transient drift the auto-tuned controller is statistically tied with the best non-stationary bandit at lower cost; under a persistent regime shift it significantly outperforms both (p < 0.03). We further show the integral term helps only under a persistent shift — a proportional controller suffices for transient drift — and evaluate robustness to noisy rewards. The result is an interpretable, stability-analyzed, auto-tuned routing controller competitive with or superior to non- stationary bandits at lower cost.
An Explainable Machine-Learning Framework for Prioritizing Cybersecurity Vulnerabilities in the Post-Mythos Era
AI-enabled vulnerability discovery is increasing both the volume and speed of newly identified security flaws, while organizations continue to face limited remediation capacity. This paper presents EVPD, an explainable machine-learning framework that ranks newly disclosed CVEs by their likelihood of entering the CISA Known Exploited Vulnerabilities Catalog within 180 days. The dataset combines 156,444 CVEs from NVD and MITRE records with historically aligned EPSS observations and CISA KEV dates. After temporal eligibility filtering, 134,875 vulnerabilities were divided chronologically into training, validation, and 2025 test partitions. A stacked ensemble combining a random-forest score, EPSS, and CVSS achieved a test PR-AUC of 0.0600, compared with 0.0337 for a tuned CVSS–EPSS baseline and a no-skill value of 0.0016. A paired bootstrap analysis produced a mean PR-AUC improvement of 0.0242, with a 95% confidence interval of 0.0032–0.0511. When remediation capacity was limited to the top 1% of the test backlog, EVPD identified 27.0% of the vulnerabilities that later entered KEV, compared with 17.6% for CVSS–EPSS and 12.2% for CVSS alone. SHAP analysis showed that historical EPSS, affected-configuration breadth, and CVSS impact measures con-tributed most strongly to the predictions. The results indicate that temporally aligned and explainable ranking can improve early vulnerability selection when remediation capacity is highly constrained.
Vulnerability prioritization, Exploit prediction, Explainable Machine learning, CVSS, EPSS, CISA KEV, SHAP
A Survey of Bias Mitigation and Fairness Techniques in Responsible Artificial Intelligence Systems
The use of artificial intelligence (AI) systems is steadily increasing across critical areas such as criminal justice, education, healthcare, and finance. The systems provide efficiency improvements which seem to deliver fairness yet they maintain the biases which exist in their design and training data. The rapid adoption of AI in important decision-making processes has raised serious concerns about bias and fairness. This paper examines bias in AI systems through its two distinct forms explicit and implicit, while showing how algorithms reproduce existing social inequalities. The research investigates essential fairness metrics together with multiple bias categories which develop during the different stages of the AI lifecycle starting from data acquisition and model building until user engagement. The paper examines how biased AI affects various real-world sectors including healthcare and finance, employment and the justice system. The document emphasizes how statistical analysis together with explainability techniques helps organizations find bias problems while building transparent systems which users can trust. Finally, the study reviews various mitigation approaches pre-processing, in-processing, and post-processing, along with broader fairness concepts such as accountability, intersectionality, group fairness, and individual fairness. Lastly, it identifies some of the most important challenges, such as conflicting fairness criteria, information constraints, scalability challenges, and ethical considerations, where context-aware AI development is necessary to guarantee equitable results of various populations.
Bias Detection Mitigation, Fairness in AI, Fairness Metrics, Artificial Intelligence, social inequalities, AI Accountability, Explainable AI (XAI)
Multi-Agent System Orchestration: A Comprehensive Survey of Architectures and Applications
The orchestration of Multi-Agent Systems (MAS) has become an essential paradigm towards supporting scalable, collaborative, and intelligent problem-solving in complex settings through large language models (LLMs). In this study, a thorough survey of MAS architectures, orchestration mechanisms, communication protocols, and applications of multi-agent systems is given. It focuses on transitioning to multi-agent ecosystems coordinated by agents, which eliminate inefficiency, limited reasoning scope, and the inability to adjust. It addresses centralized, hierarchical and distributed MAS architecture, its significance, especially, orchestration in terms of task assignment, sequencing of workflow, monitoring and fault tolerance. Moreover, it discusses such important communication standards as Model Context Protocol (MCP) and Agent-to-Agent (A2A) protocols, which permit the interoperability and well-organized cooperation between agents and other agents and systems. The survey also explains regarding applications in healthcare, industry and finance, stating how MAS orchestration is transformative. Finally, it determines the key concerns, such as scalability, governance, security, and interoperability, and outlines the new trends.
AI Agents, Orchestration, Multi-Agent Systems (MAS), Agent Communication, Large Language Models (LLMs), Model Context Protocol (MCP)
Artificial Intelligence for Cybersecurity and Cloud Security: Predictive Threat Detection, Risk Analytics, and Decision Support for Critical Infrastructure
Artificial intelligence (AI) can strengthen cyber and cloud security by detecting weak signals, estimating operational risk, and prioritizing interventions before disruptions propagate across critical infrastructure. Yet high benchmark accuracy does not by itself establish operational utility: infrastructure operators must control false alarms, account for concept drift, explain recommendations, and preserve human authority over consequential actions. This study develops a governance-aware architecture that fuses network telemetry, identity events, cloud control-plane logs, vulnerability context, asset criticality, and threat intelligence into a calibrated decision-support pipeline. A design-science methodology is combined with a reproducible synthetic benchmark of 60,000 temporally ordered security events, including an induced distribution shift in the final 20 percent. Synthetic data were used because authentic enterprise telemetry is difficult to release without exposing personal information, regulated health or identity data, network topology, vulnerabilities, and proprietary defensive controls. Logistic regression, random forest, and histogram-based gradient boosting models are evaluated using discrimination, calibration, false-positive rate, and analyst-capacity-constrained triage metrics. Under a top-decile investigation budget, logistic regression achieved the strongest overall discrimination (ROC-AUC 0.736; 95% bootstrap CI 0.724–0.748) and a PR-AUC of 0.343. This moderate performance is intentionally reported rather than inflated: the benchmark contains overlapping classes and temporal shift, and no test-set tuning was performed. The experiment is illustrative, not evidence of field effectiveness; external validation on public and operational datasets remains necessary. The architecture therefore emphasizes shadow deployment, drift monitoring, adversarial validation, audit trails, and human approval for high-impact containment.
Artificial intelligence; Cybersecurity; Cloud security; critical infrastructure; predictive threat detection; cyber-risk analytics; Explainable AI; decision support; intrusion detection; Operational technology
Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
Interpretable Ensemble Learning Approach for Breast Cancer Diagnosis Using SHAP-Based Explainable AI
Early and reliable breast cancer diagnosis is clinically important, but machine learning models intended for healthcare use must offer both predictive accuracy and interpretability. This study evaluated a structured machine learning workflow on the Wisconsin Diagnostic Breast Cancer dataset. After preprocessing and label encoding, five machine learning classifiers—Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and XGBoost—were implemented. Model performance was assessed using accuracy, precision, recall, F1-score, ROC-AUC, Brier score, and stratified cross-validation. SHAP was used to provide both global and local interpretability of the selected tree-based model. On the holdout set, Logistic Regression achieved the best overall discrimination and calibration, with 97.37% accuracy, 95.24% recall, ROC-AUC of 0.9960, and the lowest Brier score of 0.0211. SVM, Random Forest, and XGBoost each achieved 97.37% accuracy with perfect precision, but lower recall (92.86%). Cross-validation results also favored Logistic Regression, which achieved a mean ROC-AUC of 0.9955 ± 0.0038. SHAP analysis of XGBoost identified perimeter_worst, concave points_mean, concave points_worst, and radius_worst as the most influential predictors. The study demonstrates that transparent and comparatively simple machine learning models can achieve highly reliable classification performance on the WDBC benchmark, while SHAP improves interpretability of high-performing ensemble models. Although the results are promising, external clinical validation is required before real-world implementation.
Explainable Artificial Intelligence (XAI)
An Intelligent Machine Learning Framework for Encryption Algorithm Selection Based on Performance Prediction
Selecting an appropriate encryption algorithm is a critical challenge in modern cyber security, as different algorithms exhibit varying levels of computational efficiency and security depending on file characteristics. This study presents an intelligent framework for encryption algorithm selection based on experimental performance evaluation and Machine Learning prediction. Four encryption algorithms, namely AES-256, DES, Blowfish, and Hybrid RSA-2048 + AES-256, were experimentally evaluated using multiple file types and file sizes. Performance was assessed using encryption time, decryption time, CPU utilization, RAM consumption, throughput, and data integrity. The resulting experimental dataset was then used to train and evaluate four regression models: Linear Regression, Decision Tree Regression, Random Forest Regression, and Gradient Boosting Regression. Comparative analysis demonstrated that Random Forest Regression achieved the highest predictive performance among the evaluated models. Based on these findings, a Smart Encryption Recommendation Framework was developed to predict encryption performance and recommend the most appropriate encryption algorithm according to file characteristics before the encryption process is executed. The proposed framework combines experimental cryptographic evaluation with Machine Learning to support intelligent decision-making, improve encryption algorithm selection, and enhance computational efficiency while maintaining reliable data protection. The novelty of this work lies in introducing a predictive decision-support framework that recommends the most appropriate encryption algorithm before execution based on file characteristics and predicted computational performance, thereby extending conventional encryption benchmarking toward intelligent encryption decision support.
Encryption Algorithms; Machine Learning; Random Forest Regression; Performance Prediction; Cryptography; Cybersecurity; AES-256.
LLM-Driven Lean Manufacturing System: Integrating AI, RAG, and Embeddings for Industry 4.0 Decision Support
The integration of Large Language Models (LLMs) with Lean Manufacturing methodologies represents a significant frontier in the evolution of intelligent manufacturing systems within the Industry 4.0 paradigm. This comprehensive research report presents the architecture, design rationale, and empirical evaluation of a web-based Lean Manufacturing System (LMS) that employs LLMs to automate the generation of structured Lean tools, including Value Stream Mapping (VSM), 5W2H action planning, Ishikawa diagrams, Pareto analysis, GUT prioritization matrices, PDCA cycles, Kanban boards, process flowcharts, and Five Whys root-cause analysis, from unstructured natural-language problem descriptions provided by industrial operators. The proposed architecture adopts a modular microservices design, a Retrieval-Augmented Generation (RAG) pipeline, built upon LangChain, FAISS vector stores, and sentence-transformer embeddings, grounding model outputs in curated domain knowledge sourced from internal project documentation and Lean Manufacturing references. Structured prompt engineering with explicit persona definitions, mathematical output constraints, and JSON schema enforcement constitutes the primary mechanism for deterministic output generation. Experimental validation using a representative industrial case, a connector assembly workstation exhibiting a 12% rework rate, demonstrates that the AI-generated Lean artifacts correctly identify root causes, prioritize countermeasures, and project a 65% lead time reduction alongside a 200% productivity gain. Critical technical limitations are identified and discussed, encompassing single-scenario generation, absence of a feedback loop for continuous learning, dependency on prompt-level logic for semantic consistency, and a lack of integration with real-time shop-floor data streams. We conclude the analysis with a roadmap for overcoming these limitations through multi-agent orchestration, reinforcement learning from human feedback, and dynamic sensor integration.
Lean Manufacturing; Large Language Models; Industry 4.0; Retrieval-Augmented Generation; Prompt Engineering; Decision Support Systems; AI-Assisted Process Optimization; Intelligent Manufacturing Systems
AI-Induced Market Thinning: An Empirical Analysis of Trading Participation Using Volume, Trade Frequency, and Activity Metrics
Artificial Intelligence (AI) and algorithmic trading platforms have revolutionized today's financial markets through their growing levels of automation, predictive capacity, and speed. AI-driven trading helps the market run smoothly in normal times, but when these trades are coming from the same predictive signals, it can have unintended consequences of market participation and, in the short term, a liquidity contraction. This paper explores the issue of AI market thinning by conducting an empirical study on trading participation based on different metrics such as volume, frequency of trades, and active trading interval. It's a research that analyzes how market participation changes during periods of high activity, with AI signals, by examining the price movements, trading volume, transaction frequency, and activity indicators of historical intraday market data. Model-based threshold conditions, based on predictive trading indicators and probability-based classification techniques, are used to identify AI-generated buy and sell signals. The study compares periods of normal market activity with periods of strong market activity as signaled by artificial intelligence, and finds that there is no evidence of a depressing effect on market activity from synchronized algorithmic trading. Market thinning can be quantified by trading volume, number of transactions and active trading intervals. Initial estimates suggest there could be substantial contraction of participation, with fewer trades executed and shallower liquidity levels, due to concentrated AI-driven trading signals. The study also analyzes these results in the context of the market microstructure theory, which points to a higher degree of order flow diversity and a lower trading participation as evidence of thinner markets and greater liquidity fragility. This study is meaningful because it combines the advantages of AI signal analysis with empirical market participation data, providing insights into the impact of automated trading systems on financial market liquidity and stability. The results could help regulators, institutional investors and market analysts understand the risks of participation in a synchronized environment with AI-driven trading and enhance the tools they have to monitor liquidity for resilience in increasingly automated financial markets.
Artificial Intelligence Trading, Market Thinning, Trading Volume, Trade Frequency, Market Microstructure and Algorithmic Trading
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
Streaming Telemetry Analytics for AI-Driven Service Performance Prediction in Large-Scale Microservices Architectures
Distributed systems today, such as cloud infrastructures, microservice architectures, and hybrid cyber-physical networks, are increasingly difficult to monitor with respect to fault detection and diagnostics because of their large size, complexity, and dynamics. Telemetry data, a record of detailed operational metrics and event logs, is important for understanding how a system behaves and for proactively maintaining it. The paper describes an AI-based telemetry system capable of predicting service performance in a microservices system. The architecture leverages streaming telemetry, such as system metrics, logs, and traces, from cloud-native setups. The data are cleaned and normalized, and their features are extracted using Principal Component Analysis (PCA) to enhance data quality and reduce dimensionality. To learn temporal patterns of dependencies and identify anomalies in service behavior, an LSTM-based deep learning model is employed. A privacy protection layer is a safety measure that ensures the secure handling of sensitive information. The presented model achieves 94% accuracy with excellent predictive performance, low detection time, and few false positives. The comparative analysis reveals that the framework is superior to existing solutions and can be used to reliably monitor microservice performance in real time.
Microservices Architecture, Service Performance Prediction, Telemetry Data, Artificial Intelligence, Machine Learning, Privacy Preservation, Real-Time Monitoring
A Deep Learning-Based Cyber-Physical Approach for Visual Inspection of Defects in Pad Printing
Industrial quality inspection processes based on manual visual analysis are prone to human error, fatigue, low repeatability, and high operational variability, especially in high-throughput manufacturing environments. This work presents a Cyber-Physical Framework for Intelligent Visual Inspection, an intelligent computer vision system developed for the automated inspection of logos applied via pad printing on air conditioner condenser cabinets. The proposed architecture integrates deep learning techniques, including YOLOv8-based detection, Region of Interest (ROI) extraction, RGB colorimetric analysis, dimensional validation, and the generation of synthetic images using Generative Adversarial Networks (GANs). The system was designed with real-world industry constraints in mind, such as lighting variations, positioning deviations, mechanical vibrations, and takt time limitations. Experiments were conducted using real and synthetic datasets acquired under controlled industrial conditions. The results demonstrated high robustness in defect detection and good operational repeatability, highlighting the feasibility of integrating the solution into industrial environments aligned with Industry 4.0 concepts. In addition, the study discusses the integration of mechanical design, automation systems, and artificial intelligence to ensure operational stability and reliability in real production lines.
Computer Vision; Deep Learning; Industrial Inspection; YOLOv8; GANs; Industry 4.0; Pad Printing; Quality Control.
Cloud-Native Blueprint for Healthcare Payer CRM and Care Management on Salesforce Industries (Vlocity)
The purpose of this article is to propose a cloud-native blueprint for healthcare payer customer relationship management (CRM) and care management using Salesforce Industries (Vlocity) and related cloud integration patterns. Healthcare payers increasingly need architecture models that can improve member engagement, accelerate service workflows, support interoperability, and preserve governance in regulated operating environments. Existing literature discusses healthcare cloud transformation, interoperability, and Salesforce-centered modernization, but there remains limited scholarly articulation of a payer-focused enterprise architecture model that unifies service operations, guided workflows, integration strategy, and governance-oriented delivery. This article adopts a design-oriented methodology based on enterprise architecture synthesis and platform analysis to build a reusable blueprint for large payer environments. The article finds that configuration-first workflow design, API-led interoperability, unified member-context management, and controlled release governance together form a more scalable and maintainable operating model than fragmented, customization-heavy legacy ecosystems. The significance of the article lies in presenting a practical and domain-specific cloud architecture framework that can guide healthcare payer modernization and contribute to the growing scholarly conversation on cloud computing in regulated health and insurance systems.
Cloud computing, healthcare payer CRM, care management, Salesforce Industries, Vlocity, OmniStudio
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
AI-Driven Precision Health Informatics: An Integrated Framework for Multi-Omics Analytics, Predictive Disease Modeling, Cancer Intelligence, and Data-Driven Drug Discovery
The convergence of artificial intelligence (AI), machine learning (ML), large-scale data analytics, and biomedical informatics is reshaping how clinicians and researchers detect disease, plan treatment, and develop new therapies. Yet much of this progress remains fragmented across separate clinical silos, with genomic pipelines, imaging systems, wearable platforms, and pharmaceutical workflows evolving in isolation from one another. This study proposes an integrated AI-driven precision health informatics framework that draws together methods and insights spanning cancer diagnosis, precision oncology, chronic disease prediction, neurodegenerative disease management, antimicrobial resistance surveillance, cardiovascular monitoring, and pharmaceutical innovation into a single, coherent ecosystem. The framework couples multi-omics data integration with wearable health sensing, genomic analytics, and large clinical datasets, and organizes them across five interoperable layers: data acquisition, integration and harmonization, an AI/ML analytics engine, clinical application domains, and clinical translation. Supervised and deep learning approaches are combined with advanced data-fusion techniques to extract clinically meaningful patterns from heterogeneous biomedical sources. Molecular information is aligned with patient-specific clinical, behavioral, and physiological signals to support risk stratification, early detection, prognosis, and treatment optimization. In oncology, AI-enabled genomic analysis helps surface actionable biomarkers and candidate therapeutic targets; for chronic and neurological conditions, predictive analytics strengthen early intervention and individualized care; and in population health, resistance surveillance and continuous wearable monitoring enable proactive management. The work further examines how AI and generative intelligence accelerate modern drug discovery, from target identification and molecular optimization to patient stratification. By unifying predictive analytics, multi-omics intelligence, and real-time health-data streams, the proposed framework illustrates a credible path toward more efficient, more accurate, and more personalized care. We argue that AI-driven health informatics is becoming a foundational pillar of next-generation precision medicine and intelligent healthcare systems, and we close by discussing the data, methodological, and ethical challenges that must be resolved before such systems can be deployed responsibly at scale.
Artificial Intelligence; Machine Learning; Precision Medicine; Health Informatics; Multi-Omics Analytics; Precision Oncology; Chronic Disease Prediction; Drug Discovery; Wearable Health Technologies; Antimicrobial Resistance Surveillance; Predictive Analytics; Personalized Healthcare
Copyright (c) 2022 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0
Artificial Intelligence in Computational Drug Discovery: Reproducibility, Validation, and Decision Stability
Artificial intelligence (AI), machine learning, and molecular modeling have become integral components of modern computational drug-discovery workflows. But, as has been demonstrated in the industry, success in benchmark datasets does not always guarantee success in real screening projects, or in different experimental conditions. Given this limitation, the present review investigates the ability of AI-driven computational workflows to support a stable compound prioritization process in the face of uncertainty, but not just a higher retrospective score. Relevant literature published from 2018 to 2025 is reviewed, in addition to previous docking and virtual-screening studies, and community benchmarks, as well as reports of limitations under practical discovery conditions. The reviewed evidence indicates that unnoticed data leakage, validation approaches that do not mimic real-world utilization, sensitivity to docking protocols and preprocessing, stochastic variation across runs, and selection effects in large-library screening are some of the recurring factors that can lead to overconfidence. In real cases seemingly minor modifications, such as protonation-state assignments, definition of the docking-box, or random-seed selection, could make a significant difference in compound ranking that affects decisions in the lab. Validating the model in the deployment environment, performing replicate analyses, checking for rank agreement, validating the model through appropriate negative controls, and developing calibration methods are also important operational considerations for reliable prioritization. These findings highlight the growing importance of AI-assisted computational workflows that emphasize validation, reproducibility, and reliable decision support in modern drug-discovery research. Reproducibility is still important but the real utility of computational workflows is to help make decisions that are defensible in experimentation under realistic discovery conditions.
Artificial Intelligence; Computational Drug Discovery; Machine Learning; Molecular Docking; Virtual Screening; Decision Support; Reproducibility; Validation
Cloud Security Challenges in Saudi E-commerce Platforms under Vision 2030
Saudi Arabia's e-commerce sector has become one of the most rapidly expanding digital markets in the Middle East, propelled by the Kingdom's Vision 2030 agenda, high internet penetration, and a young, digitally engaged population. As e-commerce operators accelerate their migration to cloud-based infrastructure, they face a sophisticated and intensifying security landscape that threatens both operational continuity and the trust of millions of consumers. This study investigates the principal cloud security challenges confronting Saudi e-commerce platforms operating within the Vision 2030 context. Drawing on a systematic review of academic literature, industry and market research reports, and primary regulatory documents — including Saudi Arabia's Personal Data Protection Law (PDPL) and the National Cybersecurity Authority (NCA) cloud security frameworks — the study identifies and analyses seven distinct challenge domains: cloud misconfiguration and the shared responsibility gap; identity and access management weaknesses; regulatory compliance and data localization complexity; ransomware and distributed denial-of-service threats; third-party and supply chain risk; cybersecurity talent scarcity; and insider threats. The findings reveal a structural misalignment between the pace of cloud adoption and the maturity of security governance among Saudi e-commerce operators, particularly small and medium-sized enterprises. The paper concludes with evidence-based recommendations for platform operators, regulators, and policymakers to bridge this gap and build a cloud security posture commensurate with Vision 2030's ambitions for a trusted, resilient digital economy.
A Conditional Positional Encoding and Channel Attention Guided Swin Transformer for Breast Cancer Classification Using MRI and Mammography
Automated breast cancer image classification remains challenging when MRI and mammography images must be handled within a single compact architecture while preserving both local lesion texture and global contextual information. This study proposes CPCA-SwinNet, a parameter-efficient Swin Transformer-based framework for benign and malignant breast cancer classification. The model incorporates a Conditional Positional Encoding module using 3 × 3 depthwise convolution to provide resolution-adaptive spatial bias and a Channel Attention Gate that recalibrates feature responses through average- and max-pooling with shared bottleneck transformation. Key training hyperparameters were selected through an offline Grey Wolf Optimizer search, followed by final training with cosine-annealed AdamW. A balanced dataset of 3,800 images was assembled from Breast Cancer MRI, Duke Breast Cancer MRI, and CBIS-DDSM, comprising 1,860 benign and 1,940 malignant images, and split into 70% training, 10% validation, and 20% testing subsets. On the held-out test set of 760 images, CPCA-SwinNet achieved 98.82% accuracy, 99.23% sensitivity, 98.39% specificity, 98.85% F1-score, 0.9763 Matthews correlation coefficient, and 0.9964 AUC-ROC. Five-fold cross-validation yielded 98.77 ± 0.21% accuracy and 0.9962 ± 0.0008 AUC-ROC. Ablation analysis showed a 2.77 percentage-point improvement over the baseline Swin-T model, and McNemar’s test indicated significant gains over nine baseline models after Bonferroni correction. With 31.2 million parameters, CPCA-SwinNet maintains the efficiency profile of Swin-T while improving classification performance. External multi-institutional and prospective validation remains necessary before clinical use.
Breast cancer classification, CPCA-SwinNet, Swin Transformer, Conditional positional encoding, Channel attention gate , Vision transformer
Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
Compliance-Aware Payment Gateway Operations for Healthcare and Medical Device Ecosystems: A DevOps Framework for Resilient Digital Transaction Systems
Healthcare and medical device ecosystems increasingly depend on reliable digital transaction sys-tems for patient payments, provider collections, device-service billing, repair fees, warranty adjustments, refunds, reversals, chargeback evidence, settlement, and reconciliation. In such environments, payment gateway failures are not only technical incidents but also business, operational, and compliance risks. This paper proposes a compliance-aware DevOps framework for resilient digital transaction systems in healthcare and medical device ecosystems. The framework integrates payment gateway operations, transaction lifecycle monitoring, DevOps observability, incident response automation, business-impact scoring, and compliance evidence mapping. A simulation-based evaluation is designed using the PaySim synthetic financial transaction dataset as the base transaction layer. Synthetic healthcare workflow labels, payment gateway operational attributes, DevOps telemetry, and compliance evidence fields are gener-ated through controlled and reproducible rules. Six failure scenarios are injected: authorization timeout, refund failure, reversal delay, settlement mismatch, gateway latency spike, and deployment-induced transaction failure. The proposed framework is compared with two baselines: generic infrastructure monitoring and payment-only monitoring. Across repeated simulation runs, the proposed framework reduces mean time to detect and mean time to recover while improving reversal completion, refund completion, reconciliation handling, and compliance evidence completeness. The study contributes a reproducible business-DevOps model for mapping payment lifecycle exceptions to observability signals, recovery runbooks, business-impact scores, and compliance evidence controls in regulated transaction environments.
Copyright (c) 2024 Journal of Computer Science and Technology Studies
Application and development of reinforcement learning in traffic signal control
With the acceleration of urbanization, the traditional timing traffic signal control strategy is difficult to deal with the complex and variable nonlinear traffic flow. Reinforcement learning (RL) shows great potential in alleviating traffic congestion with its strong adaptive perception and optimization ability. However, reinforcement learning still faces many challenges in practical applications. This paper systematically reviews the latest research progress of reinforcement learning based traffic signal control (RL-TSC), focusing on control efficiency optimization, multi-objective constraints and overall architecture adjustment. Research shows that RL-TSC still has some problems, such as insufficient robustness to real-world noise, a lack of characterization of non-motor vehicle behavior, and a computing bottleneck of edge devices. The significance of this paper is to clearly point out the limitations and technical gaps of the current application in the rl-tsc field, and propose that the future research direction should be towards the interpretable RL, LLM hybrid architecture and global active scheduling, so as to provide theoretical guidance and forward-looking reference for the construction of a smart urban transportation system that takes into account safety, efficiency and sustainable development.
Reinforcement learning, Traffic signal control, Intelligent transportation system
AI-Assisted Identity and Access Threat Detection Using the AITIR Framework for Public-Sector Cybersecurity Environments
In public-sector cybersecurity, the password has become the prize. Agencies have moved their services into the cloud and now hand out access to employees, contractors, and members of the public alike, and that change has dragged the fight off the network perimeter and onto the credential itself. We describe AITIR, short for Adaptive Identity-and-access Threat Intelligence and Response, a layered machine-learning framework that watches for identity-based attacks as they happen and coordinates a response inside the governance rules agencies already have to follow. Three models share the detection work: a gradient-boosted classifier reads each access attempt, a bidirectional sequence model reads the order of an account's actions, and a graph-based behavior-analytics module flags the moment a session drifts away from an identity's usual company. Sitting on top of them, an explainable triage layer passes only the high-confidence, well-justified alerts to a person. To test the design, we assembled a corpus of about 14.8 million authentication and access events, part public benchmark data and part synthetic identity telemetry, and pushed credential stuffing, password spraying, privilege escalation, session hijacking, and insider misuse through it. AITIR scored an F1 of 0.967 and an area under the ROC curve of 0.985, ahead of all five baselines. Over a four-quarter deployment simulation it cut successful identity-based intrusions by 31.4 percent, brought mean time to detect down by 62 percent, and trimmed false positives by 41 percent against a conventional operations baseline. A cost-benefit model, scaled to a national public-sector portfolio, points to roughly US$10.2 billion in avoided cost a year and about 79,000 analyst hours given back. Those last two numbers are projections built on stated assumptions, not audited savings, and we say so plainly. The paper closes on the governance, staffing, and validity questions that decide whether any of them survives contact with a real agency.
Identity and Access Management; Machine Learning; Threat Detection; User and Entity Behavior Analytics; Explainable AI; Public-Sector Cybersecurity; Management Information Systems
Ransomware prediction and detection in healthcare Networking using AI
The healthcare industry's reliance on interconnected digital systems, electronic health records (EHR), and real-time patient data management makes them a prime target for ransomware attacks, which have become one of the most significant cybersecurity threats facing healthcare organizations today. These attacks can cause major disruption to health care, compromise patient sensitive data, and cause major financial and operational losses. Ransomware detection technologies like signature-based detection are ineffective against advanced and mutating ransomware strains, especially zero-day attacks. Thus, the incorporation of Artificial Intelligence (AI) and Machine Learning (ML) techniques has grown more significant to create intelligent and proactive ransomware detection systems in healthcare networking environments. In this research, a framework for prediction and detection of ransomware attacks in healthcare networks driven by a machine learning algorithm are proposed on the Healthcare Ransomware Dataset. This study emphasizes ransomware attack pattern analysis, ransomware frequency monitoring, ransomware backup’s compromised rate, ransomware severity, and ransomware recovery behavior in the healthcare organizations. The machine learning algorithms used consist of Random Forest, Decision Tree, Support Vector Machine (SVM), and XGBoost, which are all well-known and widely-used models for identifying malicious behaviors and predicting ransomware attacks. The framework proposed consists of a data preprocessing, feature engineering, model training and performance evaluation. The effectiveness of the implemented models is assessed using performance metrics like accuracy, precision, recall, F1-score and ROC-AUC. The research goals are to improve ransomware early detection, reduce false positive rate, and increase cyber security resilience of healthcare networks. The results of this study will likely show that AI-driven predictive cybersecurity systems can have a substantial impact on the effectiveness of ransomware defense, as they allow for quicker detection of threats and proactive measures are taken to minimize them. This study can help develop intelligent healthcare cybersecurity solutions and serve as a basis for future investigations into the use of AI in the development of ransomware defense systems for modern healthcare infrastructures.
Ransomware Detection, Healthcare Cybersecurity, Artificial Intelligence, Machine Learning, Network Security and Threat Prediction
Generative AI in Business Analytics: Opportunities and Risks for National Economic Growth
Generative Artificial Intelligence (AI) is reshaping business analytics by enabling organizations and governments to generate insights, improve decision-making, optimize resources, and support data-driven economic planning. This study examines the role of generative AI-driven business analytics in national economic development, with particular attention to macroeconomic performance, financial inclusion, human capital, and sustainable growth. Using secondary data from the World Bank World Development Indicators, specifically the Economic Policy and Debt and Financial Sector datasets, the study analyzes five major economies: the United States, China, India, Japan, and Germany. A quantitative and comparative research approach was applied using Python, Tableau, and Microsoft Excel for data cleaning, descriptive analysis, correlation assessment, regression estimation, and visual interpretation. Key indicators included GDP growth, adjusted net national income, education expenditure, financial account ownership, unemployment, and gross savings. The findings indicate that stronger digital readiness, financial inclusion, and investment in education are positively associated with economic performance and innovation capacity. Emerging economies such as China and India demonstrate stronger growth-oriented effects from AI-enabled digital transformation, while developed economies such as the United States, Japan, and Germany show more stable patterns linked to efficiency, productivity, and sustainability. However, the study also identifies critical risks, including data inequality, automation-related job displacement, algorithmic bias, privacy concerns, and weak ethical governance. The results suggest that generative AI in business analytics represents both a strategic opportunity and a governance challenge. Sustainable economic progress will depend on how effectively countries balance AI innovation with human capital development, inclusive digital access, ethical regulation, and long-term policy planning.
Generative Artificial Intelligence, Business Analytics, Economic Growth, Financial Inclusion, Digital Transformation and Sustainable Development
AI-Native ERP Systems: A Design Science Framework for Intelligent Enterprise Decision Automation
Enterprise Resource Planning (ERP) systems underpin the operational fabric of modern enterprises, yet their predominantly rule-based, static architectures constrain adaptability in dynamic market conditions. This paper proposes and formalizes the concept of AI-native ERP—an architectural paradigm in which artificial intelligence is not an optional overlay but a foundational, deeply integrated layer spanning data ingestion, intelligent decision-making, autonomous workflow execution, external integration, and human interaction. We present a five-layer reference architecture, a formal decision-engine model combining Bayesian-calibrated machine learning prediction, constraint-satisfaction rule validation, and multi-objective utility optimization, and a risk-aware escalation mechanism parameterized by a composite risk function. Six enterprise use cases—spanning logistics, accounts payable, tax compliance, procurement, financial operations, and cross-functional intelligence—are analyzed to demonstrate practical applicability. Comparative analysis suggests the proposed architecture has the potential to substantially reduce manual process dependency and improve decision quality relative to AI-augmented ERP systems. Challenges including model governance, adversarial robustness, explainability, and organizational change management are critically examined. Future directions encompassing agentic ERP, federated enterprise learning, and self-healing architectures are delineated.
AI-native ERP intelligent enterprise systems autonomous workflow execution machine learning decision intelligence large language models risk-aware optimization enterprise automation
FLBEC: A Unified Framework Integrating Federated Learning, Blockchain, and Edge Computing for Privacy-Preserving Cybersecurity Governance in Next-Generation Networks
Across healthcare, telecommunications, and critical infrastructure, organizations are sitting on threat intelligence that could protect their peers and sharing almost none of it. The reason is not selfishness; it is a genuine legal and competitive constraint. Data that reveals one network's vulnerabilities also reveals its patients, its subscribers, or its operational secrets. This paper begins with that everyday reality and asks a practical question: can we build a system that enables organizations to learn from each other's threats without ever exposing the underlying data? We propose FLBEC, a framework that binds together Federated Learning (FL), Blockchain governance (BC), and Edge Computing intelligence (EC) to deliver threat detection that is simultaneously private, auditable, real-time, and ready standards. We evaluate FLBEC across five benchmark datasets, NSL-KDD, CICIDS-2017, CTU-13, UNSW-NB15, and a custom healthcare IoT dataset, measuring detection accuracy, false positive rate, AUC-ROC, inference latency, and communication overhead. We also run a systematic ablation study to isolate what each of the three components actually contributes. FLBEC reaches 96.3% detection accuracy, 3.7% false positive rate, and AUC-ROC of 0.963, clearing every single-paradigm and pairwise baseline. End-to-end response latency sits at 42 ms, and communication overhead drops to 1.8 GB per federated round. Every component passes the ablation test for independent statistical significance at p < 0.05. The results confirm that privacy, governance, and real-time performance are not a trade-off triangle, they become mutually reinforcing when the three paradigms are co-designed from the start. FLBEC maps directly to IEEE 802.1X, 3GPP Release 18, and NIST CSF 2.0, making it deployable in regulated industries without modification.
Federated Learning, Blockchain Governance, Edge Computing, Cybersecurity, Privacy Preservation, 6G Networks, Homomorphic Encryption, Threat Detection, Zero-Trust Architecture, Continual Learning, IoT Security
Decentralizing the Internet of Things: A Systematic Exploration of Blockchain Integration, Architectures, and Cross-Domain Applications
The proliferation of Internet of Things (IoT) devices presents new issues in the areas of security, trust, and scalability for centralized infrastructures. The scope of this paper is a systematic literature review of the usage of blockchains to solve these issues by abandoning the centralized architecture of IoT environments. 87 articles were collected within the time scope 2015–2026 under the PRISMA protocol. Of these, 52 papers were analyzed, and this work highlights five types of blockchain-IoT integration architectures: namely, full blockchain integration, hybrid cloud-blockchain integration, fog/edge blockchain integration, consortium blockchain integration, and sidechain architecture integration. This work also proposes a new layer of IoT and blockchain integration, comprising an IoT device layer, gateway/edge layer, smart contract layer, blockchain network layer, and application layer. Future research issues include healthcare, fintech, smart agriculture, industrial IoT, supply chain management, smart cities, and decentralized vehicle authentication. Finally, this work finds that blockchain integration leads to integrity, trustworthiness, and security improvement, but is accompanied by delay and computational burden; however, new issues such as light-weighted consensus, artificial intelligence and blockchain, and regulation remain to be addressed.
Blockchain; Internet of Things; Decentralization; Smart Contracts; Distributed Ledger Technology; Systematic Literature Review; Cybersecurity; Edge Computing
An Intelligent Machine Learning Framework for Cybersecurity Threat Detection and Monitoring
As the number of IoT devices increases rapidly, cybersecurity threats are becoming more dynamic and intricate, necessitating more sophisticated and intelligent defense mechanisms. This work presents a unique intrusion detection system that enhances the security of the IoT by combining signature-based and anomaly-based techniques with AI-driven threat mitigation. The proposed framework is an amalgamation of Recurrent Neural Network (RNN) and Extreme Gradient Boosting (XGBoost) models that efficiently process network traffic, detect suspicious activities, and allow a real-time response to threats. According to the ToN-IoT dataset, data cleaning, normalization, and data balancing are employed to improve performance. The experimental analysis indicates the high-performance rate where the RNN and XGBoost have 98.5% and 98.7% accuracy respectively. The RNN model exhibits a marginally greater trade-off in terms of precision, recall and F1-score metrics. The efficacy of the proposed approach in the process of achieving high detection performance is validated by the comparative analysis with the currently existing models such as Naive Bayes, LSTM, CNN, and Random Forest. The research as a whole indicates the potential of DL and ensemble methods to improve the process of cybersecurity threat detection in the IoT context, as well as to address the issues of data imbalance and feature relevance.
Cybersecurity, Threat Detection, Anomaly Detection, Data Monitoring, Risk Mitigation, Intrusion Detection, Machine Learning, Predictive Analytics