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 Hierarchical Transformer with Residual CGTM Blocks and Gated Dilated Convolutional Refinement for Binary Bone Fracture Classification from Radiographic Images
Automated bone fracture classification from radiographic images remains challenging because fracture evidence is often subtle, spatially confined, and sensitive to projection angle and anatomical variation. While convolutional models and standard transformer-based architectures have advanced radiographic image analysis, they may not jointly capture fine-grained local structural disruptions and the broader contextual dependencies required for reliable fracture recognition. In particular, existing methods do not sufficiently unify localized fracture-sensitive feature extraction, cross-window contextual interaction, and multi-scale structural refinement within a single classification framework. To address this gap, this study proposes a hierarchical transformer-based network for binary bone fracture classification built on residual CGTM blocks. The architecture integrates window-based multi-head self-attention, shifted-window multi-head self-attention, and a Gated Dilated ConvFFN within a four-stage representation hierarchy, with adaptive patch merging between stages and attention pooling for discriminative feature aggregation. The model was evaluated on a bone fracture radiograph dataset using dedicated training, validation, and held-out test subsets, with performance stability assessed through 10-fold cross-validation. Comparative evaluation was conducted against MaxViT and EVA-02 under identical experimental conditions. The proposed model achieved a mean validation accuracy of 99.276% and a held-out test accuracy of 99.25%, together with a ROC-AUC of 99.99% and an average precision of 99.99%, outperforming MaxViT at 97.99% test accuracy and EVA-02 at 93.355% mean validation accuracy. These results indicate that jointly modeling local structural sensitivity and cross-window contextual interaction within a hierarchical transformer framework offers a meaningful advantage for radiographic fracture classification, though broader validation remains necessary before clinical use.
bone fracture classification; hierarchical transformer; residual CGTM blocks; gated dilated convolutional refinement; radiographic image analysis; window-based self-attention
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
API Management as a Strategic Layer: Governing Mainframe Modernization Through Scalable API Platforms
As enterprises modernize legacy mainframe systems, application programming interfaces (APIs) have evolved from integration utilities into instruments of enterprise governance, security, and scalability. Without structured governance, expanding API estates produce inconsistent security controls, duplicated integration logic, and performance bottlenecks that undermine the modernization objectives they were created to advance. This article argues that API management platforms constitute a strategic architectural layer, not an operational utility capable of governing how legacy capabilities are exposed, consumed, and monitored across distributed systems. Drawing on contemporary platform capabilities and practitioner literature, the article demonstrates that centralized lifecycle governance, policy enforcement, traffic control, and observability together deliver measurable outcomes. Representative improvements observed in comparable enterprise API governance programs include API response latency reductions of 30 to 45 percent through payload optimization, integration effort reductions of 20 to 40 percent through API reuse, and mean time to detection improvements of approximately 60 percent through unified observability. Treating API management as foundational infrastructure from the outset of mainframe modernization programs transforms what is otherwise a fragmented, risk-accumulating initiative into a governed, scalable, and continuously improving enterprise capability.
API Management, Mainframe Modernization, API Governance, Legacy System Integration, Enterprise Architecture, Api Lifecycle
Copilot and Gemini’s Translation of English and Arabic "Half" -Based Expressions
This study compares the translation accuracy of two AI models: Microsoft Copilot (MC) and Google Gemini (GG), in translating English and Arabic half‑expressions, the direction that is easier, types of translation errors they commit and the translation strategies they utilize. A sample of 100 Arabic and 100 English half‑expressions was randomly selected from Al‑Maany Dictionaries and Google searches, representing both general‑language and specialized‑domains. Results showed that in the Arabic half‑expressions set, MC and GG produced 80% correct equivalents, consisting of 61% identical equivalents in form and meaning and 19% correct equivalents with variant wording. Additionally, GG supplied 12% extra correct equivalents not produced by MC, raising its total accuracy to 92%, compared to 80% for MC. In the English half‑expressions set, MC and GG produced 56% correct equivalents, including 21% identical equivalents in form and meaning and 35% correct equivalents with variant wording. GG provided 24% additional correct equivalents, while MC supplied 9% additional correct equivalents. Overall, GG achieved 80% accuracy, compared to 65% for MC. Error analysis revealed consistent MC and GG failures in culture‑specific expressions, social and colloquial Arabic expressions, commercial‑domain terminology (furniture, fabrics, vehicles), expressions with polysemous words, word‑order errors, and cases where GG produced mixed correct and incorrect equivalents. The most common translation strategies utilized by MC and GG included literal translation, explanation (specifying the domain, context in which the expression is used, and a definition), giving multiple equivalents, and English equivalents containing semi, hemi, mid, middle, and demi. Transliteration was the least frequent. Arabic‑to‑English translation was easier than English‑to‑Arabic as reflected by a higher percentage of correct equivalents and differences in lexical density and synonymy. Compared with the author’s prior studies, half‑expressions were significantly easier for AI to translate than zero‑expressions, Abu/Umm‑brand names, animal and plant names, and Gaza–Israel war terms, and Arabic grammatical terms used metaphorically. Explanations are provided for the highly identical equivalents given by MC and GG, why Arabic-English translation was easier than English-Arabi translation, and why GG outperformed MC. Recommendations for AI developers and translation students are given as well.
Half-expressions, fixed and formulaic expressions, English-Arabic language pairs, AI translation, Copilot, Gemini, literal translation, contextual translation, machine translation evaluation, blind testing.
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0
An AI-Based Framework for Predictive Scaling and Anomaly Detection in Enterprise Data Platforms
Enterprise data platforms face escalating challenges managing dynamic workloads, ensuring optimal resource allocation, and detecting anomalous behaviors that threaten system integrity and performance. Traditional reactive scaling approaches and rule-based anomaly detection systems struggle to cope with the complexity, velocity, and unpredictability of modern data processing environments. This research presents a comprehensive AI-based framework that integrates predictive scaling mechanisms with intelligent anomaly detection to optimize enterprise data platform operations. The framework employs machine learning algorithms including time-series forecasting models for workload prediction, reinforcement learning for dynamic resource allocation, and unsupervised learning techniques for anomaly identification. Through implementation and evaluation across five enterprise organizations managing data platforms processing over 2.8 petabytes daily, the framework demonstrated 67% reduction in resource over-provisioning costs, 43% improvement in query performance through proactive scaling, and 89% accuracy in detecting anomalous system behaviors with average detection latency under 45 seconds. The predictive scaling component accurately forecasted workload spikes 20-45 minutes in advance, enabling preemptive resource allocation that prevented performance degradation during demand surges. Anomaly detection modules identified security threats, data quality issues, infrastructure failures, and performance bottlenecks with significantly lower false positive rates than traditional threshold-based systems. However, implementation challenges emerged including model training data requirements, computational overhead of real-time predictions, integration complexity with legacy systems, and calibration needs across different workload patterns. This research contributes practical architectural designs, algorithm selection guidance, and operational frameworks for organizations seeking to implement AI-driven optimization in their data platforms while maintaining reliability and cost-effectiveness.
Predictive Scaling, Anomaly Detection, Enterprise Data Platforms, Machine Learning, Resource Optimization, Data Infrastructure
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
Architecting Scalable BI Ecosystems for Clinical Billing Integrity: A Unified Data Architecture for Multi-Channel Healthcare Claims Management
Healthcare billing integrity is fundamental to ensuring financial sustainability, regulatory compliance, and operational efficiency within modern healthcare systems. However, the increasing fragmentation of healthcare data across clinical encounter records, pharmacy dispensing systems, and insurance claims platforms has created significant challenges for maintaining consistent, accurate, and auditable billing processes. Disconnected information systems often result in data inconsistencies, duplicate records, delayed claim reconciliation, and limited visibility into billing workflows. Although previous studies have explored healthcare analytics and fraud detection, relatively limited attention has been given to a scalable Business Intelligence (BI) architecture that unifies heterogeneous healthcare data before advanced analytical methods are applied. This paper presents MultiStream-BI, a scalable BI ecosystem designed to integrate clinical encounter data, pharmacy dispensing records, and insurance claims into a unified data architecture for clinical billing integrity. The proposed architecture employs a multi-channel data ingestion framework, standardized data normalization, a cross-stream reconciliation engine, dimensional data warehouse modeling, and an integrated reporting layer for operational and strategic decision support in multi-payer environments. The proposed architecture establishes a reliable data foundation that enhances billing transparency, improves claims reconciliation, strengthens audit readiness, and provides the infrastructure upon which future machine learning-based healthcare fraud detection systems can be developed and deployed.
Business Intelligence (BI); Clinical Billing Integrity; Healthcare Claims Management; Healthcare Data Integration; Healthcare Data Warehouse; Dimensional Modeling; Multi-Channel Data Architecture
Machine Learning for Real-Time Anomaly Detection of Phantom Billing in Health Insurance Claims: An Isolation Forest Approach with Empirical Validation
Phantom billing remains one of the most pervasive forms of healthcare fraud, resulting in substantial financial losses for health insurance providers and undermining the integrity of healthcare reimbursement systems. Conventional fraud detection approaches predominantly rely on rule-based auditing and manual claim reviews, which are time-consuming, difficult to scale, and ineffective against emerging fraudulent patterns. The growing volume and complexity of electronic health insurance claims has created a need for intelligent, real-time anomaly detection techniques. This study proposes a machine learning framework for real-time anomaly detection of phantom billing using the Isolation Forest algorithm. The framework integrates claims data preprocessing, feature engineering, anomaly scoring, and empirical validation to identify abnormal billing behaviors without requiring pre-labeled fraud datasets. Relevant claim attributes, including billing frequency, claim amount, diagnosis and procedure codes, provider utilization patterns, and patient service history, are analyzed to distinguish legitimate claims from potential fraud. The model is evaluated using precision, recall, F1-score, ROC-AUC, and PR-AUC, and compared against four baseline anomaly detection methods. The proposed framework provides a scalable, computationally efficient approach for strengthening healthcare claims verification and reducing fraudulent reimbursements. The findings demonstrate the potential of unsupervised machine learning to enhance healthcare fraud detection while establishing a practical foundation for future explainable AI and real-time fraud prevention systems.
Machine Learning; Isolation Forest; Healthcare Fraud Detection; Phantom Billing; Health Insurance Claims; Anomaly Detection; Healthcare Analytics; Explainable Artificial Intelligence
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
Federated Intrusion Detection for Internet of Medical Things Networks: Differential Privacy, Non-IID Robustness, and Cross-Device Generalization
Internet of Medical Things (IoMT) deployments combine clinically relevant sensing with heterogeneous wireless protocols and resource-constrained devices, creating a difficult setting for centralized intrusion detection. Federated learning can retain network-flow records at participating sites or gateways, but statistical heterogeneity and privacy protection can reduce utility. This study evaluates centralized and federated binary intrusion-detection models on a reproducible 71,326-row sample derived from the WiFi/MQTT portion of CICIoMT2024. After excluding 14,400 profiling-only benign flows, 28,740 training and 28,186 test flows covering 18 attack types and benign traffic were modeled with 44 predictors. Seven non-identically distributed client partitions were generated with a Dirichlet concentration of 0.30 over fine-grained labels; six participated in training and one was withheld from all model fitting. Centralized logistic regression and random forest were compared with FedAvg, FedProx, and record-level differentially private FedProx over five partition seeds. The private mechanism clipped per-record gradients, added Gaussian noise calibrated to replace-one-record sensitivity, and used zero-concentrated differential privacy composition, yielding (ε,δ)=(4.106,10^(-5) ) for 40 local mechanisms per client. Centralized random forest provided the empirical ceiling (mean F1 0.975; 95% CI 0.970–0.979). FedAvg and FedProx achieved mean F1 values of 0.855 and 0.855, respectively, showing no robustness gain from the selected proximal term. Differentially private FedProx achieved mean F1 0.822 (95% CI 0.818–0.826) and held-out-partition F1 0.844 (95% CI 0.797–0.891). In the primary partition, the private model’s F1 was 0.033 lower than FedProx (paired bootstrap 95% CI −0.035 to −0.031; exact McNemar p<10^(-200)); across only five seeds, the paired Wilcoxon test was not significant at 0.05 (p=0.0625). Critically, CICIoMT2024’s released processed table contains no verified physical-device identifier. Therefore, the withheld partition measures cross-partition generalization under synthetic label shift, not validated generalization to a new physical device. The study offers an auditable baseline and identifies device-aware metadata, stronger heterogeneity methods, and operational validation as necessary next steps.
Copyright (c) 2026 Journal of Computer Science and Technology Studies
DevSecOps for AI Systems: Security Automation, Model Protection, and Governance Frameworks
The rapid adoption of Artificial Intelligence (AI) into the contemporary software ecosystem has augmented the necessity of strong security, governance, and compliance frameworks across the lifecycle of the system. In this paper, an in-depth overview of DevSecOps practices associated with AI systems will be provided, with a focus on automated security testing, model protection, and governance frameworks. It presents intelligent security automation with SAST, DAST, and IAST with the assistance of AIOps to provide real-time monitoring, anomaly detection, and incident resolution in CI/CD pipelines. The paper also analyzes the privacy-saving model protection methods, such as watermarking, federated learning, and encryption-based safe training. The most important security threats in Large Language Models, including prompt injection, data leakage, model extraction, and API misuse, are discussed along with the method of adversarial defence. Besides, the paper outlines deployment-time protection, real-time monitoring, and threat modelling for resilient functioning. Lastly, it discusses governance frameworks, including automation, Infrastructure as Code, regulatory compliance, and Responsible AI in a way that deploys AI across cloud-native environments in a manner that is both trustworthy, ethical, and secure and retains development agility.
DevSecOps, Artificial Intelligence (AI) Security, Automated Security Testing, Model Protection, Large Language Models (LLMs), AI Governance.
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
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
An Intelligent Risk Taxonomy for Enterprise Cybersecurity Using Advanced Deep Learning Techniques
A sharp rise in sophisticated cyber intrusions, coupled with the well-known shortcomings of conventional defensive techniques, has pushed organizations to adopt smarter and self-adjusting strategies in their security operations. This study puts forward an intelligent risk taxonomy framework built on advanced deep learning methods to support precise threat identification across enterprise environments. Input records are sourced from the publicly available IDS Intrusion Dataset and then routed through a preprocessing module that handles missing entries and cleanses the raw values. A refined version of the Zebra–Gooseneck Barnacle Optimizer, here designated EZ-GBO, is applied to isolate ten informative attributes from the cleaned data. The reduced feature set is then passed into a Scalable and Adaptive Graph Neural Network (SAGNN), which both forecasts the risk category and labels every record under one of seven classes: DoS, DDoS, Brute Force, SQL Injection, Infiltration, Benign, and Bot. The entire pipeline is realized in Python and benchmarked against several competing techniques. Experimental outcomes show that the proposed scheme reaches an accuracy of 98.3 percent while keeping error magnitudes notably low.
Artificial Intelligence, Predictive Cybersecurity, Software-Defined Networking (SDN), DWDM Datacenter Networks, Intrusion Detection System (IDS) and Deep Learning
Continual Federated Learning for Network Intrusion Detection: An Open-Index Systematic Map
Purpose: Continual federated learning (FCL) combines decentralized model updating with sequential adaptation, but network-intrusion studies use incompatible definitions of drift, retention, and evaluation. This open-index systematic map examines how such systems operationalize non-stationarity, preserve earlier knowledge, and report empirical and reproducibility evidence. Method: Eight queries were executed in OpenAlex, Crossref, and DBLP on 26 July 2026. The interfaces returned 8,819 records within documented result limits; deduplication left 3,075 unique records. The initial concept-block rule excluded 2,865 records. A rule-based sensitivity audit reclassified two false negatives, yielding 2,863 automatic exclusions and 212 records for title/abstract screening. Thirty-one database-derived and nine supplementary reports were sought; 24 were not retrieved. Sixteen full texts were assessed, two were excluded, and 14 primary studies were mapped. Results: The corpus comprised three studies from 2023, six from 2024, one from 2025, and four from the partial 2026 search year. Nine studies tested retention after later tasks or classes; five reported adaptation without a prior-task retention outcome. Within the nine retention-tested studies, replay-based designs most often reported favorable within-study stability results, but heterogeneous scenarios and comparators do not establish cross-study superiority. Only two studies reported standardized forgetting or backward transfer, two supplied study-specific code, and one tested malicious clients. Conclusion: The accessible open-index evidence supports FCL as a prototype strategy under constructed benchmark sequences. It does not establish cross-network robustness, formal privacy, or deployment-scale reliability. A targeted supplementary update on 27 July 2026 found no additional eligible, accessible full text. Interpretation is constrained by capped interfaces, single-reviewer screening, and 24 unretrieved reports.
continual federated learning; network intrusion detection; concept drift; catastrophic forgetting; systematic mapping
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
Contextual AI Assistants for Multilingual Support: Architectures, Applications, Challenges, and Future Research Directions
There is a growing trend of contextual AI assistants in multilingual support for the education, health care, customer service, public administration, tourism, enterprise communication sector. Unlike scripts-driven or question-answer specific chatbots, contextual AI assistants response in a more relevant and adaptive fashion, using conversation history, user intent, domain expertise, retrieved documents, cultural signals, and task-specific, pertinent information. Transformer models that address languages, multilingual pre-trained models, large language models, and retrieval-augmented generation have all made great strides in improving the ability of AI assistants to comprehend and respond in various languages. However, support for multiple languages is not consistent, particularly for languages of low resource status, non-native languages, code-mixed language and culturally appropriate language. This review consolidates and summarizes literature on the evolution, their domains of use, methods of evaluation, and ethical challenges of multilingual language models (MLM), context-aware dialogue systems, retrieval-augmented generation, and applications of contextual AI assistants. While existing systems are becoming more effective at cross-lingual transfer, more effective ability to reason from context, and domain-grounded response generation, they have a number of limitations, primarily in the areas of hallucination, language bias, privacy, explainability, benchmark imbalance, and cultural adaptation. This paper outlines and categorizes contextual multilingual AI assistants and outlines future research directions for creating more reliable, inclusive, transparent, and human friendly AI assistance programs. The results point to the potential for a more powerful race of multilingual AI assistants in the future, integrating these features: language support, context management, retrieval grounding, culturally-appropriate interaction design, and comprehensive assessment tools and dialog systems designed for different linguistic communities.
Contextual AI assistants; multilingual support; conversational AI; large language models; retrieval-augmented generation; context-aware dialogue systems; low-resource languages; multilingual NLP; human-computer interaction; AI ethics
Ammar Khalid Ammar khalid sheikh khalid ayub
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
Industry 5.0: Human-Centric Manufacturing for Sustainable and Intelligent Production Systems
Industry 5.0 has become a revolutionizing manufacturing paradigm which goes further from the automation-oriented strategy of Industry 4.0 by putting human welfare, sustainability, and resilience at the core of intelligent production systems. Instead of replacing humans with autonomous technologies, Industry 5.0 aims at collaboration between humans and intelligent digital technologies for better productivity, agility, and sustainable value creation. The present paper provides a comprehensive review of human-centric manufacturing and explores its importance in creating sustainable and intelligent production systems. The principles of Industry 5.0 are discussed, and the roles played by the major enabling technologies such as Artificial Intelligence (AI), cobots, Digital Twins, IIoT, Big Data Analytics, AR, VR, and Edge–Cloud Computing are analyzed. The applications of the mentioned technologies to the main manufacturing industries such as automotive, aerospace, electronics, additive manufacturing, medical devices, and food processing are thoroughly reviewed. In addition, the key implementation issues connected to cybersecurity, workforce competency, interoperability, ethical AI, and technology adoption, as well as promising research opportunities are identified. A conceptual framework based on the reviewed literature, combining the principles of human-centric manufacturing, enabling technologies, intelligent manufacturing processes, and sustainable production outcomes, is developed. It can be used to guide the implementation of the Industry 5.0.
Industry 5.0; Human-Centric Manufacturing; Sustainable Manufacturing; Intelligent Production Systems; Artificial Intelligence; Collaborative Robots; Digital Twin; Industrial Internet of Things; Smart Manufacturing.
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)
Strengthening U.S. Digital Infrastructure Through AI-Powered Cloud-Native Software Resilience and Secure Delivery Systems
The growing dependence of government, healthcare, finance, energy, communications, transportation, and other critical sectors on software-intensive services has made cloud-native resilience and secure software delivery important components of U.S. digital infrastructure. Microservices, containers, orchestration platforms, and continuous integration and delivery pipelines improve scalability and release velocity, but they also introduce complex service dependencies, fragmented telemetry, cascading failures, vulnerable software components, and software supply-chain integrity risks. This study develops the AI-Powered Cloud-Native Resilience and Secure Delivery Framework (AICR-SDF), a conceptual artifact that connects critical-service requirements, cloud-native infrastructure, observability, AI-assisted operational intelligence, secure software delivery, and policy-governed response. The framework is developed using Design Science Research Methodology supported by an integrative literature review. Its technical foundations draw on established reliability, resilience, software-delivery, network-analysis, cybersecurity, and governance concepts, including availability and reliability functions, service-level objectives and error budgets, burn-rate analysis, DORA software-delivery metrics, network centrality, likelihood–impact risk assessment, the Common Vulnerability Scoring System, Supply-chain Levels for Software Artifacts, the NIST Secure Software Development Framework, and resilience-loss analysis. Four qualitative scenarios demonstrate how the framework distinguishes normal deployment, service degradation, an unverified software artifact, and cascading microservice failure. Ex ante evaluation examines requirements coverage, internal consistency, standards alignment, scenario applicability, governance adequacy, and implementability. The study contributes an integrated and policy-aware conceptual foundation for the future prototyping and empirical evaluation of resilient and securely delivered cloud-native services.
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/