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
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
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
AI-Driven Predictive Cybersecurity Architecture for U.S. SDN-Controlled DWDM Datacenter Networks
The growing integration of Software-Defined Networking (SDN) and Dense Wavelength Division Multiplexing (DWDM) systems in modern U.S. datacenter infrastructures has created new cybersecurity threats, threats in speed and volume of network traffic, and networking threats of centralized control. Traditional rules-based security systems no longer provide real-time detection of advanced persistent threats, malware propagation, botnet intrusions, Distributed Denial of Service (DDoS) attacks and other forms of sophisticated attacks. In this regard, this study is aimed at proposing an AI-based predictive cybersecurity architecture for securing SDN controlled DWDM datacenter networks using intelligent threat prediction, anomaly detection and automated mitigation mechanisms to overcome these limitations. The proposed framework combines machine learning and deep learning with SDN-based traffic management to improve the visibility, scalability and adaptive cyber defense capabilities of networks. This study uses the CSE-CIC-IDS2018 data set which includes realistic enterprise network traffic and several attack categories for training and testing predictive models of cybersecurity. A number of AI algorithms are employed, such as Random Forest, XGBoost, Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, to analyze traffic behavior, classify malicious activities, and predict potential cyberattacks. The architecture is segmented into several layers are traffic monitoring to gather data, feature extraction to analyze the data, AI-based prediction to foresee potential threats, threat detection to identify them, and automated response to address them, all aimed at enhancing the security resilience of high-speed optical datacenter environments. Experiments should show that its detection accuracy is higher, it has fewer false positive rates, it can respond quicker to attacks and it can provide superior protection for the network than the traditional intrusion detection systems. This study will help build a next-generation intelligent cybersecurity framework that secures the DWDM datacenter in the United States from new cyber threats while enhancing the efficiency, scalability, and real-time adaptive security management of the system.
Artificial Intelligence, Predictive Cybersecurity, Software-Defined Networking (SDN), DWDM Datacenter Networks, Intrusion Detection System (IDS) and Deep Learning
Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
Multimodal and Hybrid Artificial Intelligence for Real-World Decision-Making: Methods, Evidence, and Applications
Real-world decision-making rarely depends on a single data stream. Healthcare diagnosis, industrial fault detection, agricultural disease monitoring, business intelligence, cybersecurity threat response, and assistive technology all require AI systems capable of integrating heterogeneous evidence from images, text, physiological signals, sensors, graphs, tabular records, and their combinations. Multimodal and hybrid AI systems address this challenge by combining complementary data modalities, complementary architectures, fusion strategies, domain knowledge, and deployment infrastructures. This review identifies and critically examines four categories of directly multimodal or hybrid evidence, multimodal EEG analysis, vision-audio fusion, hybrid multimodal emotion recognition, and privacy-preserving multimodal cancer diagnosis, alongside a broader set of hybrid, ensemble, attention-based, graph-guided, and Bayesian architectures that advance multimodal integration. It situates these within seven application domains and examines the cross-cutting challenges of fusion design, modality alignment, interpretability, robustness, privacy, computational feasibility, and human oversight. Synthesis reveals that while fusion strategies have diversified from feature concatenation through tensor and attention-based fusion to knowledge-guided integration, evidence validation practices, including fusion ablation, modality-dropout testing, and calibrated uncertainty reporting, remain inconsistently applied. A structured research agenda addresses these gaps with eleven actionable future directions.
Multimodal AI, Hybrid AI, Fusion strategies, Explainable AI, Decision support systems, Trustworthy AI, Federated learning, Cross-domain AI taxonomy
From Machine Learning to Foundation and Agentic AI: Evolution of Intelligent Decision Systems Across Domains
Intelligent decision systems have undergone a multi-stage architectural evolution—from conventional machine learning and structured analytics through convolutional deep learning, attention-based transformers, graph neural networks, multimodal fusion systems, federated and privacy-preserving frameworks, to generative AI and emerging agentic decision architectures. This evolution is not merely technical: it changes how systems acquire representations, explain decisions, operate across institutional boundaries, integrate into professional workflows, and support high-stakes decisions in healthcare, business, industry, smart infrastructure, agriculture, cybersecurity, assistive technologies, and sustainability. This review characterizes ten evolutionary stages from structured ML through agentic decision systems—and maps their expression across seven application domains. Synthesis reveals that while deep learning and transformer architectures have substantially advanced representational capability, the deployment-critical properties of trustworthiness, validated explainability, uncertainty quantification, and governance accountability have not evolved at the same pace. Generative AI and agentic systems represent a qualitative shift toward interactive and workflow-embedded decision support, but introduce hallucination risk, accountability gaps, and governance demands that exceed current frameworks. A structured research agenda addresses evolution-aware benchmarks, trustworthy foundation-model adaptation, human-in-the-loop evaluation, federated multimodal intelligence, and governance-aware reporting standards for agentic decision systems.
AI evolution, Foundation models, Agentic AI, Decision support systems, Trustworthy AI, Explainable AI, Federated learning, Cross-domain AI taxonomy
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
An Ensemble of Advanced Machine Learning Models for Telecom Customer Churn Forecasting
In the rapidly evolving telecommunications sector, maintaining profitability and growth depends on customer retention. This study provides an in-depth analysis of customer churn prediction with the aim of determining the important factors impacting customer attrition and developing a practical predictive model. In order to make churn prediction more accurate and consistent, the authors of this paper suggest a hybrid ensemble model (LGBM + RF) that mixes LightGBM with random forests. The model makes use of both approaches to improve the accuracy and generalizability of its predictions. Testing on the IBM Telco Customer Churn dataset shows that the proposed model performs better than both traditional ML and DL models. Compared to its forerunners, LGBM + RF achieves better results with a lower AUC of 0.998%, a higher PRE of 98.85%, a recall (REC) of 99.12%, and an F1-score (F1) of 98.56%. These models were previously known as CatBoost, XGBoost, Random Forest, KNN, and Deep-BP-ANN. Ensemble learning is successful in capturing complicated patterns of consumer behavior, according to the findings. This method offers practical information that telecom companies can apply to develop targeted customer retention mechanisms and enhance customer loyalty, making it a useful and valid tool for churn forecasting programs.
Customer churn, Telecom Industry, Churn Prediction, Machine Learning, Ensemble Learning, Predictive Modeling
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0
Large Language Model (LLM)–Driven Threat Correlation and Governance Automation for Security Operations in U.S. Enterprise Systems
In today's context of cyber threats constantly evolving and enterprise digital infrastructures becoming ever more complex, modern Security Operations Centers (SOCs) face tremendous challenges. Traditional cybersecurity solutions typically lack the ability to effectively and efficiently correlate huge numbers of diverse threat data, causing issues with delayed response to incidents, too many false positives and operational inefficiencies. However, to overcome these challenges in this research, a new Large Language Model (LLM) – Driven Threat Correlation and Governance Automation Framework for intelligent security operations is proposed. The proposed framework combines Artificial Intelligence (AI), Natural Language Processing (NLP), Machine Learning (ML), and Large Language Models (LLMs) to automate processes of threat analysis, incident correlation, governance compliance, and security decision-making processes. The system exploits the UNSW-NB15 intrusion detection dataset and structured security logs and threat intelligence feeds to detect malicious activities and correlate multi-source events that occur in the security space. The advanced LLM capabilities include threat analysis, threat summary, threat prioritization and attack mapping capabilities to cybersecurity frameworks like MITRE ATT&CK and NIST guidelines. The framework also includes automated governance reporting, compliance validation and explainable AI driven recommendations to aid SOC analysts in real-time decision making. Experimental analysis shows that the proposed system increases the accuracy of detection of threats and decreases the rate of alerts fatigue, the time it takes for a response in an incident compared to the conventional SOC methodologies and increases the efficiency of governance automation. The study underscores the potential of LLM-powered cybersecurity solutions to revolutionize security operations in today's businesses, offering them the ability to become more scalable, intelligent, and autonomous. The results are significant for the evolving AI-powered cyber defense space, offering a viable and adaptable framework that enhances threat intelligence correlation, operational resilience, and cyber governance in an evolving threat landscape.
Large Language Models (LLMs), Threat Correlation, Security Operations Center (SOC), Governance Automation, Cybersecurity Intelligence and Intrusion Detection
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0
Identification of Precious Metals using Deep Learning and Image Processing Methods
There are nearly ninety types of minerals in the world. Gold, platinum, palladium, and silver are classified as precious metals. These metals are used in a wide range of fields, including industry, technology, and jewelry. Due to its very high electrical conductivity, gold is used in sensitive technologies such as chips and microprocessors. Platinum and palladium, on the other hand, are used in the coating of aircraft and jet engines, dentistry, and the production of medical devices because they are resistant to high temperatures. The strategic importance of precious metals and their wide range of applications underscore the significance of this study. The primary objective of this study is to perform segmentation of gold, platinum, and palladium metals present in images obtained under a light microscope from samples taken from soil or rock. Through the deep learning-based system developed in this study, the goal is to achieve benefits in terms of time, labor, and cost by performing a preliminary evaluation of precious metals by an expert before initiating testing on advanced and expensive sample analysis devices. Image segmentation was performed using the U-NET architecture, one of the deep learning methods. The results of the system, developed based on the presence of precious metals indicated by the expert, were evaluated using a confusion matrix, achieving an accuracy of 83.84%.
Precious Metals, Deep Learning, Gold, Platinum, U-Net
Quantitative Analysis and Automatic Counting of Microorganisms Using Image Processing Techniques
Microorganisms play critical roles in many fields, including medicine, food, and industrial production. Although the quantitative analysis of these organisms is a fundamental process for scientific research, manual counting through visual observation is a highly time-consuming and subjective process for experts. In this study, an automated counting system based on digital image processing techniques was developed to address the disadvantages of manual counting methods. The study utilized 170 microscopic images obtained from 22 different microfungus species. Two distinct methods based on the Circular Hough Transform (CHT) and morphological operations were tested. The results demonstrated that the CHT method, with a precision value of 0.5, exhibited a high correlation of 0.98 with expert counting.
Image Processing, Microorganism, Microfungus, Hough Transform, Automatic Counting
Comparative Evaluation of CNN and Transformer-Based Models for Brain Tumor MRI Segmentation
Brain tumors represent a critical medical problem with significant impact on human health. Manual assessment processes are insufficient in addressing this problem due to the difficulty in identifying tumor regions and disagreements among experts. This has led to an increasing demand for automated tumor detection or segmentation using machine learning and deep learning methods. This study compares the segmentation performance of selected deep learning models to investigate which model architecture yields better results under specific conditions. Two main approaches are considered: CNN-based (FCN, U-Net, Attention U-Net, DeepLabV3+) and a Transformer-based model (Swin U-Net). To ensure a fair comparison, all models were trained on the same dataset (BRISC 2025) under identical training conditions. Model performance was evaluated using Dice, IoU, Precision, Recall, and HD95 metrics. Among the tested models, DeepLabV3+ achieved the best performance with a Dice score of 0.8575 and an HD95 value of 7.8367. The findings indicate that CNN-based models outperform the Transformer-based model under the given dataset and experimental conditions. The results suggest that the performance of Transformer-based models may be sensitive to dataset characteristics. This study contributes to the literature by systematically evaluating CNN- and Transformer-based models under identical experimental settings.
Brain Tumor Segmentation, U-Net, DeepLabV3+, Swin U-Net, BRISC 2025
A Review of Paradigm Shifts and Collaborative Evolution in Robot Chassis Design Driven by VLA
Vision-Language-Action (VLA) foundation model-driven embodied intelligence is a cutting-edge field at the intersection of artificial intelligence and robotics, endowing robots with powerful semantic understanding and generalization capabilities. However, traditional mechanical body design paradigms are based on assumptions of structured environments and struggle to adapt to the open-task requirements driven by VLA across the dimensions of perception, execution, and interaction. This results in insufficient coordination between intelligent modules and mechanical bodies in open tasks, thereby limiting the overall system performance. This paper systematically reviews the paradigm-shifting challenges posed by the rise of VLA to robotic mechanical body design and explores pathways for their collaborative evolution. First, we dissect the limitations of traditional design in open scenarios; second, we analyze the morphological evolution of the robot body from rigid to compliant and from dedicated to programmable; and third, we elucidate the coordination mechanisms between the intelligent core and the physical shell. This paper aims to provide a theoretical framework for the development of next-generation robot platforms and to advance the transition of general embodied intelligence from theoretical research to practical application.
Vision-Language-Action Model; Robotics; Robot Chassis Design; Embodied Intelligence; Co-evolution; Paradigm Challenge
Data-Driven Prioritization of Grid Interruptions: A Lean Six Sigma Study Using Public Outage Logs
The supply of power that is reliable and uninterrupted is one of the pillars of the contemporary energy infrastructure. Nonetheless, the issue of grid interruptions which are frequent and long-lasting is still compromising the resilience of the system and customer satisfaction. Traditional approaches to outage-management and prioritization tend to be reactive and based on manual evaluation and missing records of events. To overcome such shortcomings, this paper suggests a hybrid meta-learning model that would combine Random Forest (RF), Extreme Gradient Boosting (XGBoost), and a Deep Artificial Neural Network (Deep ANN) to come up with the data-driven prioritization of grid interruptions. The data were processed extensively with the Missing-value treatment, derivation of impact-score, feature scaling using robust scale and SMOTE and controlled Gaussian perturbation using publicly available data 15 Years of Power Outages (2000 - 2014) dataset. The proposed hybrid model integrates RF and XGBOOST in an ensemble layer while capturing the variance decomposition and residual optimization for variance decomposable nonlinearities and the Deep ANN meta-learner captures complex non-linear dependencies. Instead of maintenance, proactive and timely, the system prioritizes the outage events on the basis of the predicted severity of impact in the framework of Lean Six Sigma DMAIC. Comparative analysis with four baseline models which included RF, XGBoost, Deep ANN, and RF-XGB models showed that the hybrid model was better as it had 99.43% accuracy, 98.92% precision, 99.15% recall, and 99.03% F1-score. The obtained results support that the suggested meta-learning technique offers a significant enhancement in the robustness, scalability, and interpretability of prediction of outage impact. This study creates a solid information-driven approach to the implementation of the Six Sigma principles into the contemporary energy-analytics processes that opens the way to increased intelligence and agile power-grid operations.
Power outage analytics, Lean Six Sigma, Random Forest, XGBoost, Deep ANN, hybrid meta-learning, SMOTE augmentation, data-driven prioritization, grid reliability, energy resilience
AI-Driven Predictive Modeling for Solar Power Generation Using Real-Time Photovoltaic Sensor Data
Solar PV systems, as part of modern power grids, demand accurate forecasting in real time to address the intermittency issues and ensure a stable grid. Current forecasting models, however, underperform when put into practice because of incomplete sensor coverage and cloud-induced ramp events, lack of cross-site generalization, and lack of quantified uncertainty. In this article, we propose a new end-to-end hybrid network, namely Missingness-Aware Physics-Guided Graph Transformer (MAPGFormer), to tackle these practical challenges. The core pillars of MAPGFormer are: introducing a novel sensor tokenization layer to separate out power, weather, physics, temporal and missingness signals; developing a missingness-aware reconstruction encoder to effectively exploit the structured missing patterns for the robust imputation and forecasting; designing a static-dynamic graph learner to fuse historical similarity with topologies learned by real-time frame attention for reconstruction and inverter/site level spatial modeling; designing a multi-scale temporal Transformer to perform multi-resolutions temporal signal reconstruction and prediction; designing a novel weather-regime Mixture-of-Experts module with specific experts for clear-sky, cloudy/ramp, low-irradiance and missing-sensor conditions; finally designing a probabilistic forecasting head that is able to generate calibrated quantile outputs. The framework was carefully tested on two complementary open Kaggle datasets: 1) Solar Power Generation Data for primary inverter-level development and benchmarking against classical, recurrent and Transformer baselines, and 2) UNISOLAR dataset for external multi-site generalization and transfer learning with validation using Transformer baselines. On the primary data set, MAPGFormer produced remarkable performance in predicting 15-minute electricity demand, achieving an MAE of 12.44 kW, RMSE of 18.09 kW, nRMSE of 2.13%, sMAPE of 4.02%, and R² of 0.9827, whereas the Vanilla Transformer gave a MAE of 13.61 kW and reduced the MAE by 18.6%. The accuracy was consistently demonstrated in multi-horizon and cross-plant analyses, and in UNISOLAR leave one out and held out site analysis, the R2s were 0.9791-0.9803. The model was found to have good robustness when missingness was simulated up to 40% and good probabilistic intervals. The contribution of each architectural component was validated using ablation studies and make the model explainable by SHAP analysis. MAPGFormer sets a new standard in practical PV forecasting for uncertainty-aware real-time grid management systems.
MAPGFormer, Photovoltaic power forecasting, Missing data robustness, Graph neural network, Mixture-of-Experts, Multi-scale Transformer, Probabilistic forecasting, Ramp events
A Framework for Securing Agentic AI Workflows and Quantum-Resistant Communication in U.S. Critical Infrastructure Networks
For artificial intelligence (AI) systems designed to learn and act without human intervention, the events of this rapid integration into U.S. critical infrastructure networks and the rapid pace of approach to cryptographically relevant quantum computers (CRQCs) create a dual convergence of threat that is not adequately met by current frameworks. Agentic AI systems have additional attack surfaces, such as prompt injection, non-human identity (NHI) exploitation, and cascading multi-agent failures, due to their ability to operate autonomously, take multiple steps, and coordinate with other agents. At the same time, existing public-key cryptographic standards, such as RSA and elliptic curve cryptography (ECC), are under attack with harvest-now-decrypt-later (HNDL) attacks to hack encrypted communications in operational technology (OT) and industrial control system (ICS) environments. This article suggests security in both threat domains by leveraging three layers of security: Layer 1: Security of the agentic AI workflow through identity management, prompt sanitization and behavior monitoring; Layer 2: Quantum resistant communication with migration to NIST standardized post-quantum cryptographic (PQC) algorithms (FIPS 203, 204, 205); and Layer 3: Governance and compliance integration with CISA, NERC CIP and the NIST AI Risk Management Framework. Compliance is verified with a cross-walk analysis to current standards, and the framework is tested in scenarios of representative critical infrastructure in the energy, water, and financial sectors. Results suggest that this framework offers a roadmap for RTOs to use in preparing for the deployment of agentic AI and the migration to post-quantum cryptographic algorithms.
Agentic AI, post-quantum cryptography, critical infrastructure security, zero trust architecture, multi-agent systems, NIST PQC standards, prompt injection, quantum-resistant communication
A Leakage-Aware Machine Learning Pipeline for Credit Default Prediction Using LightGBM
Credit default prediction remains challenging because loan-outcome datasets are typically imbalanced, heterogeneous, and vulnerable to post-origination target leakage. This study proposes a leakage-aware and interpretable LightGBM-based credit-risk modelling framework for binary loan-status classification into Fully Paid and Charged Off/Default outcomes. The proposed workflow integrates rigorous target definition, removal of repayment-derived leakage variables, robust missing-value handling, outlier winsorisation, date-derived credit-history features, log-transformed monetary variables, affordability and utilisation ratios, mixed categorical encoding, FICO bucketisation, class-frequency reweighting, and mutual-information-based feature selection. A large lending dataset of 887,379 completed loans was analysed, comprising 725,223 Fully Paid loans and 162,156 Charged Off/Default loans. Data were stratified into training, validation, and holdout test sets using a 70/15/15 split, with additional stratified five-fold cross-validation repeated across five random seeds, yielding 25 validation runs.LightGBM was selected as the proposed best model after comparison with Logistic Regression, Random Forest, CatBoost, and XGBoost. The model achieved the highest mean cross-validation AUC of 0.762 ± 0.004, outperforming XGBoost, CatBoost, Random Forest, and Logistic Regression, which obtained AUC values of 0.758 ± 0.004, 0.755 ± 0.004, 0.731 ± 0.005, and 0.708 ± 0.005, respectively. On the independent holdout test set, LightGBM achieved an AUC of 0.764, accuracy of 0.853, default sensitivity of 0.730, specificity of 0.880, default F1-score of 0.644, positive predictive value of 0.576, negative predictive value of 0.936, and Brier score of 0.124. Feature-importance and SHAP-direction analysis identified interest rate, sub-grade, debt-to-income ratio, annual income, and FICO range as the dominant risk drivers.
Credit default prediction, LightGBM, machine learning, credit risk modelling, target leakage, imbalanced classification, feature engineering, SHAP interpretability, loan default prediction, financial risk assessment.
A Survey of Next-Generation Electronic Quality Management Systems for Global Regulated Enterprises
Next-generation Electronic Quality Management Systems (eQMS) represent the evolution of traditional paper-based quality systems into intelligent, cloud-based, and compliance-driven digital ecosystems. This study examines the organizational architecture, regulatory alignment, enabling technologies, and implementation challenges associated with next-generation eQMS in regulated industries such as pharmaceuticals and advanced manufacturing. Modern eQMS platforms integrate core quality modules including document control, CAPA, audit management, training, and change control into centralized and automated systems that enhance traceability, data integrity, and operational efficiency. The paper further evaluates compliance with global regulatory frameworks, including ICH E6(R2/R3), USFDA 21 CFR Part 11, EMA Annex 11, and PIC/S guidelines, with a focus on inspection readiness and lifecycle-based validation. Emerging technologies such as artificial intelligence, machine learning, cloud computing, blockchain, digital twins, and the Internet of Things are enabling predictive, real-time, and proactive quality management. Despite these advancements, implementation remains challenging due to legacy system integration, data complexity, resource constraints, and organizational resistance to digital transformation.
Next-Generation eQMS, Regulatory Compliance, Digital Quality Management, Artificial Intelligence, Cloud Computing, Industry 4.0
Transforming Pharma Manufacturing Supply Chains Through AI and ERP Analytics
The pharmaceutical manufacturing business has a complex, international supply chain, constrained by strict regulatory measures, rising costs, and growing pressure on product quality, supply, and efficacy. Conventional planning and control strategies have not been effective at managing demand volatility, equipment reliability, and inventory risk at a large scale. To this end, artificial intelligence and enterprise resource planning systems are emerging as transformative technologies that can revamp supply chain processes in pharmaceutical companies. The next stage of analytics in demand prediction, predictive maintenance, inventory optimization, and production planning is reachable due to the integration of AI and ERP solutions, increasing the efficiency of operations and improving decision-making quality. Based on empirical data by proven pharmaceutical manufacturers, up to 30% of forecast errors, 20-30% of unplanned unforeseen downtime, and ten to 20% of holding inventory costs are reduced after AI-facilitated adoption of ERP. Nevertheless, it has to be well run in data governance, inter-functional collaboration, and change management to facilitate the effective use of the technologies and ensure the reliability of the models, regulatory compliance, and workforce acceptance. This paper combines the empirical evidence base in the industry, practical case study applications, and analysis findings to deliberate on the feasibility of AI ERP integration in the pharmaceutical manufacturing process. It suggests an analytics-driven decision-making model with governance to make supply chains more resilient.
AI in pharmaceutical supply chain, ERP analytics, Pharma manufacturing, Predictive maintenance, Regulatory compliance
A National-Scale AI-Driven Cyber Defense Framework for Protecting U.S. Critical Infrastructure Against Nation-State Attacks
The Cybersecurity and Infrastructure Security Agency (CISA) has predicted a 140% rise in high-impact attacks between 2022 and 2024, signalling an increase in nation-state adversary cyberattacks that affect US critical infrastructures. The sophistication, persistence and ever-evolving nature of nation-states' hacking has outgrown current cybersecurity, which primarily focuses on reactive and perimeter-based approaches. In this paper, we present and test a National-Scale AI-Driven Cyber Defense Framework (NAICDF) a multi-layer, intelligence-based framework with machine learning-based threat detection, federated learning for data sharing across critical infrastructure sectors, Zero Trust Architecture (ZTA) and automated response systems. We compare intrusion detection accuracy, mean time to respond (MTTR) and resilience with traditional approaches using incident reports of 847 confirmed nation-state intrusions in 11 critical infrastructure sectors (2020-2024), publicly available threat intelligence and simulation data from the National Cyber Exercise Program (NCEP) of the Cybersecurity and Infrastructure Security Agency (CISA). Results demonstrate the NAICDF achieves a 94.3% accuracy in detecting intrusions (2.1% false positives) and a 67% reduction in the mean time to respond (MTTR) when compared to conventional security operations center (SOC) systems. We also explore governance and public-private partnership models, and compatibility with existing policy frameworks such as CIRCIA (2022) and the National Cybersecurity Strategy (2023). The framework offers a policy-friendly, scalable model to secure our critical systems in the 21st century.
Critical infrastructure protection, AI-driven cybersecurity, nation-state attacks, Zero Trust Architecture, federated learning, intrusion detection, national cybersecurity policy, advanced persistent threats
An Author-Bounded Systematic Review of Studies on Social Media in EFL Teaching and Learning (2008–2025): Skills, Platforms, and Pedagogical Insights
This study presents a systematic review (SR) of the author’s research on the use of social media (SM) in language teaching and learning published between 2008 and 2025. The SR synthesizes 33 studies distributed across 11 thematic clusters: pronunciation development using YouTube and AI generated videos; listening skill development through TED Talks and news podcasts; reading skill development; writing and creativity; grammar practice through podcasts; translation skills development; multi skill learning through Facebook; communication in instructional contexts; cultural learning through wikis; delivering academic lectures via live social media streaming; and teachers’ professional development on Facebook. The review provides a longitudinal account of how SM has been integrated into language teaching and learning across multiple platforms (Facebook, X, Skype, YouTube, Periscope, blogs, wikis, podcasts) and multiple skills (listening, pronunciation, reading, writing, culture, translation, and multi skill learning), highlighting the pedagogical potential, purposeful instructional designs, and evolving nature of SM tools in EFL education. Across the 33 studies, SM platforms offered accessible, flexible, and interactive learning environments that enhanced students’ engagement, motivation, and exposure to real world language use. Across clusters, students benefited from multimodal input, opportunities for self regulated learning, and increased interaction with peers, instructors, and global communities. SM also served as a powerful supplementary environment for EFL instruction, providing authentic content, learner autonomy, and diverse pedagogical strategies. This SR offers the first longitudinal, author bounded synthesis of 33 studies conducted over 18 years, documenting how one researcher systematically explored and advanced the use of SM in EFL education. It contributes a unified pedagogical perspective that connects platforms, tasks, and outcomes, and provides a rare insider account tracing the evolution of platforms, instructional practices, and innovations across diverse skills and learning environments. It also highlights the need for continued research that keeps pace with rapidly changing technologies and emerging digital practices. Future research may examine how emerging platforms, such as TikTok, Instagram Reels, Threads, vlogs, vodcasts and AI enhanced tools can be integrated into EFL instruction using evidence based pedagogical frameworks and how learners interact with AI mediated social environments in ways that shape language development, engagement, and digital literacy.
Author bounded systematic review, social media in EFL, Facebook, X (Twitter), YouTube, podcasts, wikis, blogs, EFL skills development, pedagogical practices.
A Review of Evolution of Internet of Things (IoT) and its Significant Impact in the Field of Precision Agriculture
Agriculture is swiftly moving towards a digital transformation from conventional mechanization to a prescriptive "Agriculture 4.0" model. This survey offers a critical assessment of the evolution of the Internet of Things (IoT) and its application in Precision Agriculture (PA) systems. We assess the technological evolution from early Wireless Sensor Networks (WSN) to new Low-Power Wide-Area Networks (LPWAN) like NB-IoT and LoRaWAN, which solve the long-standing deployment issue of battery life in rural coverage. This review critically examines more than 50 impactful publications, centering on novel deployments of multi-modal sensors to support site-specific farming, adaptive irrigation and real-time livestock monitoring. Our report differs from existing surveys in explicitly considering the architectural evolution to Edge-Fog computing, driven by the rapid expansion in data volumes of high-resolution soil sensing and atmospheric monitoring. We benchmark the merits of connectivity protocols, showcasing their impact on resource-efficient practices, such as reported reductions in water and fertilizer use. Finally, we idensify key challenges, such as platform interoperability, data security in remote nodes, and the likely integration of 5G-powered robotic swarms. The review provides a guide to researchers and industry partners about the current state and evolution of the agricultural IoT landscape.
Internet of Things (IoT), Precision Agriculture, LPWAN, NB-IoT, Edge Computing, Smart Irrigation, Sustainable Agriculture
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Strategic Framework for Zero-Downtime Modernization of Legacy Enterprise Systems
Legacy enterprise systems provide the stable foundation for mission-critical operations in large organizations, yet they frequently consume 70–80% of IT budgets and limit innovation. This paper presents a practical, proven framework that enables IT leaders to modernize these systems into cloud-native microservices while delivering true zero downtime, zero service disruption, enhanced resiliency, improved latency, and significant cost optimization.The framework is built on three primary architectural transformation strategies — the Strangler Fig Pattern, Domain-Driven Decomposition, and Blue-Green Deployment — supported by Change Data Capture (CDC), the Agile-to-Uplift (A2U) methodology, and targeted network upgrades. Together, these elements allow organizations to extract and migrate business functions incrementally with full confidence. A real-world financial services case study demonstrates complete modernization over 24 months with no customer impact and substantial annual savings.
Legacy modernization, zero-downtime deployment, Strangler Fig pattern, Domain-Driven Decomposition, Blue-Green deployment, Agile-to-Uplift (A2U), resiliency, latency optimization, cost optimization, enterprise architecture
Copyright (c) 2026 Journal of Computer Science and Technology Studies
Securing the Next Generation of Digital Payments: A Threat Model for Virtual Card Ecosystems
The rapid adoption of virtual cards in business-to-business (B2B) payments has created a complex digital ecosystem involving buyers, suppliers, issuers, acquirers, and increasingly, automated processing platforms. While virtual cards offer inherent security benefits, the ecosystem's interconnectedness and reliance on digital channels introduce a new and expanded attack surface. This paper presents a comprehensive threat model for this next generation of digital payments. Using the STRIDE framework, we systematically analyze the security threats inherent in the end-to-end virtual card lifecycle, with a particular focus on novel attack vectors targeting the automated data ingestion and processing stages of platforms like Visa AR Manager. Based on this analysis, we propose a multi-layered, defense-in-depth mitigation strategy. This strategy integrates foundational controls such as PCI DSS compliance, technical solutions including payment tokenization and secure API gateways, and advanced AI-powered fraud detection to create a resilient and secure virtual payment environment capable of withstanding modern threats.
Next Generation of Digital Payments; Threat Model; Virtual Card Ecosystems
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Machine learning based clinical decision support for heart disease prediction using structured patient data
Heart disease remains a leading cause of mortality worldwide, necessitating reliable and efficient predictive models for early diagnosis and clinical decision support. This study presents a comprehensive machine learning framework for heart disease prediction using a structured clinical dataset comprising 920 patient records with diverse demographic, physiological, and diagnostic attributes. The dataset includes both numerical and categorical features, requiring careful preprocessing and encoding to ensure compatibility across different model architectures.A range of classification algorithms is systematically evaluated, including Logistic Regression, K-Nearest Neighbors, Decision Tree, Random Forest, Support Vector Machine, Extreme Gradient Boosting, and Light Gradient Boosting Machine. Model performance is assessed using multiple evaluation metrics, including accuracy, precision, recall, F1-score, and receiver operating characteristic area under the curve, along with five-fold cross-validation to examine stability and generalization behavior.The experimental results demonstrate consistently high predictive performance across most models, with several approaches achieving near-perfect classification metrics and minimal variation across cross-validation folds. In contrast, K-Nearest Neighbors exhibits slightly lower performance, highlighting differences in sensitivity to local data structure. Analysis of feature distributions and pairwise relationships indicates strong separability between classes, particularly driven by clinically relevant variables such as chest pain type, exercise-induced angina, ST depression, and maximum heart rate.Further evaluation using confusion matrices, receiver operating characteristic curves, and precision–recall curves confirms the robustness of the predictive models and their ability to distinguish between diseased and non-diseased cases with high reliability. Despite the strong performance, the study acknowledges potential dataset-specific characteristics that may influence model behavior and emphasizes the importance of external validation for clinical deployment.
heart disease prediction, clinical decision support, machine learning, classification models, structured clinical data, cross-validation, ROC AUC, precision recall analysis, medical diagnosis, predictive analytics
Two Decades of LMS-Supported EFL Instruction: A Systematic-Review of Implementation, Use, Infrastructure, and Success Factors
This study conducted a systematic review (SR) of the author’s research on the use of Online Learning Management Systems (LMSs) in teaching English language skills between 2001 and 2021. The corpus consists of 44 studies categorized into three thematic clusters: implementation of blended learning in teaching language skills; LMS infrastructure, use, and utilization; and success factors in blended learning. The corpus covers experimental studies using LMS; reading and writing studies; investigations of the differential effects of online instruction on multiple EFL skills; studies using iRubrics for assessment; research employing web conferencing tools; extensive reading studies; online collaborative learning and cross cultural communication via LMS; explicit, strategy based online reading instruction; feasibility studies of different LMSs; LMS infrastructure and use; cases in which online learning failed; and studies on success factors. Results showed that the author’s research provides a comprehensive account of how blended learning was implemented across different English language skills (reading, writing, vocabulary, grammar, ESP, translation) using Blackboard, WebCT, Moodle, Nicenet, RCampus, and Elluminate. The studies documented instructional procedures, task types, assessment methods, and patterns of student engagement in online and blended environments. Across twelve experimental studies, students who received a combination of online and textbook based instruction achieved higher gains than those who received textbook only instruction, and active participants consistently outperformed passive participants. Overall, students expressed positive attitudes toward online instruction, and LMS supported learning, enhanced autonomy, motivation, collaboration, and engagement. Additionally, the author’s studies on LMS infrastructure and use identified institutional, technical, and training related factors that affected the utilization of online learning. Studies on success factors emphasized the importance of online collaboration, active participation, motivation, timely feedback, well designed tasks, and supportive learning environments. Notably, collaboration succeeded in the Saudi–Ukrainian–Russian study because interaction occurred in a culturally neutral, non threatening international space, whereas collaboration between King Saud University and Umm Al Qura University failed due to students’ discomfort with cross university interaction in a gender segregated local context, which suppressed participation. In conclusion, the author’s contributions to LMS based EFL instruction provide a foundation for understanding how online and blended learning evolved across different platforms, skills, and pedagogical contexts.
Systematic review (SR), Al-Jarf research program, online courses, Learning Management Systems (LMS), LMS infrastructure, EFL/ESL online instruction, web based tools, technology enhanced learning, online engagement, online collaboration
A Secure and Privacy-Preserving Architecture for Web-Based Remote Learning Systems
The rapid proliferation of web-based remote learning systems (WBRL) has introduced critical security and privacy challenges that threaten the integrity of educational data and the confidentiality of student information. This paper presents SP-WBRL, a Secure and Privacy-Preserving architecture designed for web-based remote learning environments. The proposed multi-layered framework integrates advanced encryption standards (AES-256), role-based access control (RBAC), differential privacy mechanisms, and a zero-trust network model to provide end-to-end security. We formalize the privacy guarantees using (ε, δ)-differential privacy and derive theoretical bounds on information leakage. Extensive experiments conducted over a 12-week deployment with 2,847 participants across three institutions demonstrate that SP-WBRL achieves a 97.3% threat detection rate with only 6.8% average latency overhead compared to non-secure baselines, while maintaining FERPA and GDPR compliance. Comparative evaluation against five state-of-the-art systems shows significant improvements in security coverage (23.1% improvement), privacy preservation (31.5% improvement), and user satisfaction (92.4% approval rating). The results confirm that comprehensive security can be integrated into remote learning platforms without substantially degrading the user experience.
A Secure and Privacy-Preserving Architecture; Web-Based Remote Learning Systems
NLP Framework to Summarize p53–FBW7 Pathway Alterations and Associated miRNAs in CRC
The increasing intricacies of molecular pathways involved in CRC, especially focusing on the p53-FBW7 pathway network and the microRNAs (miRNAs) that regulate them, present significant difficulties when synthesizing knowledge from the exploding body of biomedical literature. Conventional methods of manual literature review have proven inadequate for capturing and analyzing such multidimensional interactions in a timely fashion. In this paper, we describe an innovative NLP-based approach for mining CRC-related pathway changes and miRNA interactions. The architectural framework uses domain-driven data extraction methods along with sophisticated pre-processing, named entity recognition using transformers, and relation extraction techniques to extract important biological entities and their relationships. The relations extracted by the framework are then encoded within a knowledge graph capturing pathway-level dynamics of gene-protein-miRNA relationships, which can provide an integrated perspective on gene-protein-miRNA relationships. Furthermore, the transformer-based summarizer also provides summarized insights related to p53-FBW7 axis. The model aims to bring together computational intelligence and molecular oncology through a comprehensive, automated, and interpretable process for knowledge integration in biomedical sciences. Expected benefits will involve greater efficiency in research, the ability to identify key regulatory mechanisms, and contributions to precision medicine and drug discovery initiatives. Despite being conceptual, the paper introduces a novel method that combines NLP with cancer pathways, holding promise for extensive development in the future.
Natural Language Processing, p53–FBW7 Pathway, Colorectal Cancer, microRNA Regulation, Biomedical Text Mining
Big Data Analytics Using Hadoop and Spark: Applications, Challenges, and Future Direction
Big data describes large datasets that are difficult to manage owing to their diversity, size, and complexity in terms of storage, analysis, and visualisation for future operations. Artificial intelligence (AI) based big data analytics have revolutionised the processing of massive data sets by leveraging distributed computing platforms like Apache Spark and Apache Hadoop. Both HDFS and MapReduce leverage the 5Vs of Hadoop—Volume, Velocity, Variety, and Veracity—to store files and perform batch analysis on massive, heterogeneous datasets. Spark expands upon these capabilities with libraries for graph analytics, streaming, machine learning, in-memory computing, and directed acyclic graphs (DAGs). This paper gives a general outline of the Hadoop and Spark ecosystem, as well as their architecture and how they are used in distributed machine learning, real-time analytics, NLP, and anomaly detection. It also addresses such critical issues as scalability limitations, data protection, resource administration, and complexity of infrastructures. Lastly, the future directions are discussed with a focus on cloud-native designs, edge computing, and hardware acceleration as well as intelligent resource optimization to improve the performance, efficiency, and flexibility of the next generation Big Data systems.
AI–Big Data Analytics, Apache Hadoop, Apache Spark, Streaming Analytics, Data Mining, MapReduce
Stress-Level Classification for Students with Recommendation Outputs
Student mental health has deteriorated measurably and continuously over the past decade. A 2025 survey by SRM University AP, published in the Asian Journal of Psychiatry, found that nearly 70 percent of 1,628 college students across eight major Indian cities reported moderate to high anxiety levels, while over 60 percent exhibited clinically significant depression symptoms. These numbers are not outliers. Cross-national data shows Japan at 80 percent stress prevalence, the United States at 67 percent, and Pakistan at 71 percent. What unites these figures is not geography but the structure of modern academic life: excessive workload, unrelenting performance pressure, financial strain, social comparison amplified by digital platforms, and institutional support systems that were simply not built for this scale of need. The clinical response to this crisis has been slow and resource-constrained. Counseling services are stretched thin. Most students who need help do not seek it, largely because of stigma, lack of awareness, or long wait times. Only 7 percent of college students in the United States access professional mental health support in any given year (EssayPro, 2025). This gap between need and access defines the problem this article addresses. This article presents a machine learning-based classification framework that predicts student stress levels across three tiers: low, medium, and high. The model draws on academic, lifestyle, physiological, and psychometric data collected through structured surveys. It evaluates five classification algorithms, including Logistic Regression, Random Forest, XGBoost, and ensemble methods, with accuracies ranging from 86.2 to 97.92 percent in peer-reviewed studies. The framework also includes a recommendation engine that generates targeted, evidence-based intervention guidance for each stress tier. The goal is to give educational institutions a practical, scalable tool for identifying students at risk before their stress reaches crisis level.
Stress-Level Classification; Students; Recommendation Outputs; academic life
A Systematic Self-Review of Electronic Searching Studies (2002–2021): Training, Infrastructure, and Institutional Contexts
This study conducted a systematic review (SR) of the author’s research program on electronic searching published between 2002 and 2021. A total of seventeen studies were identified and organized into five thematic clusters: electronic information infrastructure, factors affecting electronic searching, electronic searching skills and training, integration of electronic resources in teaching and research, and policies, intellectual property, and e transformation. Together, these studies provide a comprehensive account of the conditions, competencies, and institutional arrangements that shape effective electronic searching in higher education contexts. Findings across the corpus indicate that successful electronic searching involves a combination of individual skill development, supportive institutional environments, and efficient technological infrastructure. Gaps in connectivity, system design, metadata quality, and resource availability directly influence users’ ability to locate and retrieve information. Fragmented databases, limited access to full text materials, and inconsistent archiving practices, were found to hinder researchers’ performance. Results also demonstrate that electronic searching skills do not emerge spontaneously but require explicit instruction, guided practice, and exposure to diverse digital tools that appear to consistently improve users’ confidence, accuracy, and efficiency. Training programs that emphasized search strategies, evaluation of digital sources, and searching specialized databases yielded noticeable gains among faculty, graduate students, and library staff. Additionally, integrating electronic resources into teaching and research both enhances and depends on improved searching practices. When electronic resources are embedded within coursework and research tasks, learners demonstrate stronger engagement and more sophisticated information handling behaviors. However, such integration is only effective when supported by institutional policies, training initiatives, and technological systems that facilitate access and encourage sustained use. Moreover, the SR highlights the role of policy frameworks, intellectual property regulations, and institutional leadership in shaping the broader digital environment. Alignment between pedagogy, infrastructure, and policy was shown to be critical for enabling effective electronic searching across academic settings. Finally, this study recommends that researchers use AI supported search tools. However, they should be aware of their limitations. AI systems may generate incomplete or biased search results, overlook domain specific sources, or misinterpret queries that require disciplinary nuance. They should critically evaluate AI generated outputs, verify retrieved sources through authoritative databases as Google Scholar.
Systematic review (SR), Al-Jarf research program, electronic searching, electronic searching literacy, electronic searching skills, electronic searching infrastructure, intellectual property of digital resources, access to digital resources, electronic databases, specialized database search
Advanced Models & Multimodal Reasoning: A Conflict Resolution–Centric Architecture
Multimodal artificial intelligence has evolved at a fast rate, and currently, advanced models can process vision, language, audio, and sensor data on the scale and accuracy not seen before. Regardless of these developments, multimodal reasoning systems are not well adopted in the real-life context and high-stakes situations. This paper holds the view that the fundamental impediment is not perceptual performance rather than architectural weakness where there is disagreement between heterogeneous modalities. We suggest an architecture-first view where the conflict is not viewed as an exception and must be inhibited but is a normal feature of a multimodal system that must be dealt with explicitly. The key conceptual element of this school of thought is the Conflict Resolution Layer (CRL), which is a system-level architectural primitive that identifies, assesses, and resolves conflicts between expert models of specific specialization acting over common, time-based evidence. In contrast to end-to-end multimodal fusion methods or ensemble learning methods, the CRL will not only allow decision policies to be made deterministically, auditable, and by humans in the loop; it will also allow inference to be completely separated from arbitration. We give formalization and roles of the CRL, examine the performance features of the CRL, and demonstrate that conflict-conscious reasoning is more coordination-bound than it is compute-bound. The paper also shows how CRL-based architectures can facilitate incremental adoption of the industry, starting with advisory systems and then because of conditionally automated decision pipelines, all being vendor-neutral and hardware-agnostic. This contribution by highlighting shift of focus towards model-centric optimization to system level design establishes conflict resolution as a lacking architectural primitive to robust, interpretable, deployable multimodal reasoning systems.
Multimodal Reasoning, Conflict Resolution Layer, Service-Oriented AI, Temporal Semantics, Expert Models, and Human-in-the-Loop
From Monolithic Models to Cognitive Hives: A Framework for Multimodal Reasoning Systems
The increasing fast development of multimodal foundation models has greatly contributed to the development of artificial intelligence systems that have the ability to process and integrate text, vision, audio, and structured information in single architectures. Although they have been performing admirably, these models are becoming more limited in nature as they grow in size especially in performance regarding efficiency, transparency and flexibility. Existing monolithic multimodal architectures demand a lot of computational and financial resources, have inflexible structure of design, and are not that interpretable. Moreover, they also use implicit fusion processes that are blind to modal conflict and inconsistency detection and resolution. The given paper suggests another paradigm, known as cognitive hives, which views intelligent multimodal systems as distributed assemblies of specialised expert models instead of single end-to-end networks. The approach to multimodal reasoning, in this context, is viewed as an issue in distributed systems, in which autonomous domain-specific experts interact via shared representations and explicit coordination protocols. One of the main parts of the suggested architecture is a special conflict resolution layer that would identify, arbitrage, and clarify disputes between expert models. The major contributions that were made in this work are the formal definition of cognitive hive architecture, systematic approach on cross-modal conflict resolution, and analysis of deployment strategies, latency trade-offs and failure modes in distributed reasoning systems. The framework is proposed to be hardware-agnostic, modular and future-proof, and is a scalable and explainable alternative to monolithic multimodal models.
Multimodal Reasoning, Distributed AI Systems, Cognitive Architectures, Conflict Resolution in AI, Modular Artificial Intelligence