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
Cloud-Native Blueprint for Healthcare Payer CRM and Care Management on Salesforce Industries (Vlocity)
The purpose of this article is to propose a cloud-native blueprint for healthcare payer customer relationship management (CRM) and care management using Salesforce Industries (Vlocity) and related cloud integration patterns. Healthcare payers increasingly need architecture models that can improve member engagement, accelerate service workflows, support interoperability, and preserve governance in regulated operating environments. Existing literature discusses healthcare cloud transformation, interoperability, and Salesforce-centered modernization, but there remains limited scholarly articulation of a payer-focused enterprise architecture model that unifies service operations, guided workflows, integration strategy, and governance-oriented delivery. This article adopts a design-oriented methodology based on enterprise architecture synthesis and platform analysis to build a reusable blueprint for large payer environments. The article finds that configuration-first workflow design, API-led interoperability, unified member-context management, and controlled release governance together form a more scalable and maintainable operating model than fragmented, customization-heavy legacy ecosystems. The significance of the article lies in presenting a practical and domain-specific cloud architecture framework that can guide healthcare payer modernization and contribute to the growing scholarly conversation on cloud computing in regulated health and insurance systems.
Cloud computing, healthcare payer CRM, care management, Salesforce Industries, Vlocity, OmniStudio
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
AI-Driven Precision Health Informatics: An Integrated Framework for Multi-Omics Analytics, Predictive Disease Modeling, Cancer Intelligence, and Data-Driven Drug Discovery
The convergence of artificial intelligence (AI), machine learning (ML), large-scale data analytics, and biomedical informatics is reshaping how clinicians and researchers detect disease, plan treatment, and develop new therapies. Yet much of this progress remains fragmented across separate clinical silos, with genomic pipelines, imaging systems, wearable platforms, and pharmaceutical workflows evolving in isolation from one another. This study proposes an integrated AI-driven precision health informatics framework that draws together methods and insights spanning cancer diagnosis, precision oncology, chronic disease prediction, neurodegenerative disease management, antimicrobial resistance surveillance, cardiovascular monitoring, and pharmaceutical innovation into a single, coherent ecosystem. The framework couples multi-omics data integration with wearable health sensing, genomic analytics, and large clinical datasets, and organizes them across five interoperable layers: data acquisition, integration and harmonization, an AI/ML analytics engine, clinical application domains, and clinical translation. Supervised and deep learning approaches are combined with advanced data-fusion techniques to extract clinically meaningful patterns from heterogeneous biomedical sources. Molecular information is aligned with patient-specific clinical, behavioral, and physiological signals to support risk stratification, early detection, prognosis, and treatment optimization. In oncology, AI-enabled genomic analysis helps surface actionable biomarkers and candidate therapeutic targets; for chronic and neurological conditions, predictive analytics strengthen early intervention and individualized care; and in population health, resistance surveillance and continuous wearable monitoring enable proactive management. The work further examines how AI and generative intelligence accelerate modern drug discovery, from target identification and molecular optimization to patient stratification. By unifying predictive analytics, multi-omics intelligence, and real-time health-data streams, the proposed framework illustrates a credible path toward more efficient, more accurate, and more personalized care. We argue that AI-driven health informatics is becoming a foundational pillar of next-generation precision medicine and intelligent healthcare systems, and we close by discussing the data, methodological, and ethical challenges that must be resolved before such systems can be deployed responsibly at scale.
Artificial Intelligence; Machine Learning; Precision Medicine; Health Informatics; Multi-Omics Analytics; Precision Oncology; Chronic Disease Prediction; Drug Discovery; Wearable Health Technologies; Antimicrobial Resistance Surveillance; Predictive Analytics; Personalized Healthcare
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A Conditional Positional Encoding and Channel Attention Guided Swin Transformer for Breast Cancer Classification Using MRI and Mammography
Automated breast cancer image classification remains challenging when MRI and mammography images must be handled within a single compact architecture while preserving both local lesion texture and global contextual information. This study proposes CPCA-SwinNet, a parameter-efficient Swin Transformer-based framework for benign and malignant breast cancer classification. The model incorporates a Conditional Positional Encoding module using 3 × 3 depthwise convolution to provide resolution-adaptive spatial bias and a Channel Attention Gate that recalibrates feature responses through average- and max-pooling with shared bottleneck transformation. Key training hyperparameters were selected through an offline Grey Wolf Optimizer search, followed by final training with cosine-annealed AdamW. A balanced dataset of 3,800 images was assembled from Breast Cancer MRI, Duke Breast Cancer MRI, and CBIS-DDSM, comprising 1,860 benign and 1,940 malignant images, and split into 70% training, 10% validation, and 20% testing subsets. On the held-out test set of 760 images, CPCA-SwinNet achieved 98.82% accuracy, 99.23% sensitivity, 98.39% specificity, 98.85% F1-score, 0.9763 Matthews correlation coefficient, and 0.9964 AUC-ROC. Five-fold cross-validation yielded 98.77 ± 0.21% accuracy and 0.9962 ± 0.0008 AUC-ROC. Ablation analysis showed a 2.77 percentage-point improvement over the baseline Swin-T model, and McNemar’s test indicated significant gains over nine baseline models after Bonferroni correction. With 31.2 million parameters, CPCA-SwinNet maintains the efficiency profile of Swin-T while improving classification performance. External multi-institutional and prospective validation remains necessary before clinical use.
Breast cancer classification, CPCA-SwinNet, Swin Transformer, Conditional positional encoding, Channel attention gate , Vision transformer
Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
Compliance-Aware Payment Gateway Operations for Healthcare and Medical Device Ecosystems: A DevOps Framework for Resilient Digital Transaction Systems
Healthcare and medical device ecosystems increasingly depend on reliable digital transaction sys-tems for patient payments, provider collections, device-service billing, repair fees, warranty adjustments, refunds, reversals, chargeback evidence, settlement, and reconciliation. In such environments, payment gateway failures are not only technical incidents but also business, operational, and compliance risks. This paper proposes a compliance-aware DevOps framework for resilient digital transaction systems in healthcare and medical device ecosystems. The framework integrates payment gateway operations, transaction lifecycle monitoring, DevOps observability, incident response automation, business-impact scoring, and compliance evidence mapping. A simulation-based evaluation is designed using the PaySim synthetic financial transaction dataset as the base transaction layer. Synthetic healthcare workflow labels, payment gateway operational attributes, DevOps telemetry, and compliance evidence fields are gener-ated through controlled and reproducible rules. Six failure scenarios are injected: authorization timeout, refund failure, reversal delay, settlement mismatch, gateway latency spike, and deployment-induced transaction failure. The proposed framework is compared with two baselines: generic infrastructure monitoring and payment-only monitoring. Across repeated simulation runs, the proposed framework reduces mean time to detect and mean time to recover while improving reversal completion, refund completion, reconciliation handling, and compliance evidence completeness. The study contributes a reproducible business-DevOps model for mapping payment lifecycle exceptions to observability signals, recovery runbooks, business-impact scores, and compliance evidence controls in regulated transaction environments.
Copyright (c) 2024 Journal of Computer Science and Technology Studies
Application and development of reinforcement learning in traffic signal control
With the acceleration of urbanization, the traditional timing traffic signal control strategy is difficult to deal with the complex and variable nonlinear traffic flow. Reinforcement learning (RL) shows great potential in alleviating traffic congestion with its strong adaptive perception and optimization ability. However, reinforcement learning still faces many challenges in practical applications. This paper systematically reviews the latest research progress of reinforcement learning based traffic signal control (RL-TSC), focusing on control efficiency optimization, multi-objective constraints and overall architecture adjustment. Research shows that RL-TSC still has some problems, such as insufficient robustness to real-world noise, a lack of characterization of non-motor vehicle behavior, and a computing bottleneck of edge devices. The significance of this paper is to clearly point out the limitations and technical gaps of the current application in the rl-tsc field, and propose that the future research direction should be towards the interpretable RL, LLM hybrid architecture and global active scheduling, so as to provide theoretical guidance and forward-looking reference for the construction of a smart urban transportation system that takes into account safety, efficiency and sustainable development.
Reinforcement learning, Traffic signal control, Intelligent transportation system
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
AI-Assisted Identity and Access Threat Detection Using the AITIR Framework for Public-Sector Cybersecurity Environments
In public-sector cybersecurity, the password has become the prize. Agencies have moved their services into the cloud and now hand out access to employees, contractors, and members of the public alike, and that change has dragged the fight off the network perimeter and onto the credential itself. We describe AITIR, short for Adaptive Identity-and-access Threat Intelligence and Response, a layered machine-learning framework that watches for identity-based attacks as they happen and coordinates a response inside the governance rules agencies already have to follow. Three models share the detection work: a gradient-boosted classifier reads each access attempt, a bidirectional sequence model reads the order of an account's actions, and a graph-based behavior-analytics module flags the moment a session drifts away from an identity's usual company. Sitting on top of them, an explainable triage layer passes only the high-confidence, well-justified alerts to a person. To test the design, we assembled a corpus of about 14.8 million authentication and access events, part public benchmark data and part synthetic identity telemetry, and pushed credential stuffing, password spraying, privilege escalation, session hijacking, and insider misuse through it. AITIR scored an F1 of 0.967 and an area under the ROC curve of 0.985, ahead of all five baselines. Over a four-quarter deployment simulation it cut successful identity-based intrusions by 31.4 percent, brought mean time to detect down by 62 percent, and trimmed false positives by 41 percent against a conventional operations baseline. A cost-benefit model, scaled to a national public-sector portfolio, points to roughly US$10.2 billion in avoided cost a year and about 79,000 analyst hours given back. Those last two numbers are projections built on stated assumptions, not audited savings, and we say so plainly. The paper closes on the governance, staffing, and validity questions that decide whether any of them survives contact with a real agency.
Identity and Access Management; Machine Learning; Threat Detection; User and Entity Behavior Analytics; Explainable AI; Public-Sector Cybersecurity; Management Information Systems
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Ransomware prediction and detection in healthcare Networking using AI
The healthcare industry's reliance on interconnected digital systems, electronic health records (EHR), and real-time patient data management makes them a prime target for ransomware attacks, which have become one of the most significant cybersecurity threats facing healthcare organizations today. These attacks can cause major disruption to health care, compromise patient sensitive data, and cause major financial and operational losses. Ransomware detection technologies like signature-based detection are ineffective against advanced and mutating ransomware strains, especially zero-day attacks. Thus, the incorporation of Artificial Intelligence (AI) and Machine Learning (ML) techniques has grown more significant to create intelligent and proactive ransomware detection systems in healthcare networking environments. In this research, a framework for prediction and detection of ransomware attacks in healthcare networks driven by a machine learning algorithm are proposed on the Healthcare Ransomware Dataset. This study emphasizes ransomware attack pattern analysis, ransomware frequency monitoring, ransomware backup’s compromised rate, ransomware severity, and ransomware recovery behavior in the healthcare organizations. The machine learning algorithms used consist of Random Forest, Decision Tree, Support Vector Machine (SVM), and XGBoost, which are all well-known and widely-used models for identifying malicious behaviors and predicting ransomware attacks. The framework proposed consists of a data preprocessing, feature engineering, model training and performance evaluation. The effectiveness of the implemented models is assessed using performance metrics like accuracy, precision, recall, F1-score and ROC-AUC. The research goals are to improve ransomware early detection, reduce false positive rate, and increase cyber security resilience of healthcare networks. The results of this study will likely show that AI-driven predictive cybersecurity systems can have a substantial impact on the effectiveness of ransomware defense, as they allow for quicker detection of threats and proactive measures are taken to minimize them. This study can help develop intelligent healthcare cybersecurity solutions and serve as a basis for future investigations into the use of AI in the development of ransomware defense systems for modern healthcare infrastructures.
Ransomware Detection, Healthcare Cybersecurity, Artificial Intelligence, Machine Learning, Network Security and Threat Prediction
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
Generative AI in Business Analytics: Opportunities and Risks for National Economic Growth
Generative Artificial Intelligence (AI) is reshaping business analytics by enabling organizations and governments to generate insights, improve decision-making, optimize resources, and support data-driven economic planning. This study examines the role of generative AI-driven business analytics in national economic development, with particular attention to macroeconomic performance, financial inclusion, human capital, and sustainable growth. Using secondary data from the World Bank World Development Indicators, specifically the Economic Policy and Debt and Financial Sector datasets, the study analyzes five major economies: the United States, China, India, Japan, and Germany. A quantitative and comparative research approach was applied using Python, Tableau, and Microsoft Excel for data cleaning, descriptive analysis, correlation assessment, regression estimation, and visual interpretation. Key indicators included GDP growth, adjusted net national income, education expenditure, financial account ownership, unemployment, and gross savings. The findings indicate that stronger digital readiness, financial inclusion, and investment in education are positively associated with economic performance and innovation capacity. Emerging economies such as China and India demonstrate stronger growth-oriented effects from AI-enabled digital transformation, while developed economies such as the United States, Japan, and Germany show more stable patterns linked to efficiency, productivity, and sustainability. However, the study also identifies critical risks, including data inequality, automation-related job displacement, algorithmic bias, privacy concerns, and weak ethical governance. The results suggest that generative AI in business analytics represents both a strategic opportunity and a governance challenge. Sustainable economic progress will depend on how effectively countries balance AI innovation with human capital development, inclusive digital access, ethical regulation, and long-term policy planning.
Generative Artificial Intelligence, Business Analytics, Economic Growth, Financial Inclusion, Digital Transformation and Sustainable Development
AI-Native ERP Systems: A Design Science Framework for Intelligent Enterprise Decision Automation
Enterprise Resource Planning (ERP) systems underpin the operational fabric of modern enterprises, yet their predominantly rule-based, static architectures constrain adaptability in dynamic market conditions. This paper proposes and formalizes the concept of AI-native ERP—an architectural paradigm in which artificial intelligence is not an optional overlay but a foundational, deeply integrated layer spanning data ingestion, intelligent decision-making, autonomous workflow execution, external integration, and human interaction. We present a five-layer reference architecture, a formal decision-engine model combining Bayesian-calibrated machine learning prediction, constraint-satisfaction rule validation, and multi-objective utility optimization, and a risk-aware escalation mechanism parameterized by a composite risk function. Six enterprise use cases—spanning logistics, accounts payable, tax compliance, procurement, financial operations, and cross-functional intelligence—are analyzed to demonstrate practical applicability. Comparative analysis suggests the proposed architecture has the potential to substantially reduce manual process dependency and improve decision quality relative to AI-augmented ERP systems. Challenges including model governance, adversarial robustness, explainability, and organizational change management are critically examined. Future directions encompassing agentic ERP, federated enterprise learning, and self-healing architectures are delineated.
AI-native ERP intelligent enterprise systems autonomous workflow execution machine learning decision intelligence large language models risk-aware optimization enterprise automation
Copyright (c) 2026 Journal of Computer Science and Technology Studies
FLBEC: A Unified Framework Integrating Federated Learning, Blockchain, and Edge Computing for Privacy-Preserving Cybersecurity Governance in Next-Generation Networks
Across healthcare, telecommunications, and critical infrastructure, organizations are sitting on threat intelligence that could protect their peers and sharing almost none of it. The reason is not selfishness; it is a genuine legal and competitive constraint. Data that reveals one network's vulnerabilities also reveals its patients, its subscribers, or its operational secrets. This paper begins with that everyday reality and asks a practical question: can we build a system that enables organizations to learn from each other's threats without ever exposing the underlying data? We propose FLBEC, a framework that binds together Federated Learning (FL), Blockchain governance (BC), and Edge Computing intelligence (EC) to deliver threat detection that is simultaneously private, auditable, real-time, and ready standards. We evaluate FLBEC across five benchmark datasets, NSL-KDD, CICIDS-2017, CTU-13, UNSW-NB15, and a custom healthcare IoT dataset, measuring detection accuracy, false positive rate, AUC-ROC, inference latency, and communication overhead. We also run a systematic ablation study to isolate what each of the three components actually contributes. FLBEC reaches 96.3% detection accuracy, 3.7% false positive rate, and AUC-ROC of 0.963, clearing every single-paradigm and pairwise baseline. End-to-end response latency sits at 42 ms, and communication overhead drops to 1.8 GB per federated round. Every component passes the ablation test for independent statistical significance at p < 0.05. The results confirm that privacy, governance, and real-time performance are not a trade-off triangle, they become mutually reinforcing when the three paradigms are co-designed from the start. FLBEC maps directly to IEEE 802.1X, 3GPP Release 18, and NIST CSF 2.0, making it deployable in regulated industries without modification.
Federated Learning, Blockchain Governance, Edge Computing, Cybersecurity, Privacy Preservation, 6G Networks, Homomorphic Encryption, Threat Detection, Zero-Trust Architecture, Continual Learning, IoT Security
Decentralizing the Internet of Things: A Systematic Exploration of Blockchain Integration, Architectures, and Cross-Domain Applications
The proliferation of Internet of Things (IoT) devices presents new issues in the areas of security, trust, and scalability for centralized infrastructures. The scope of this paper is a systematic literature review of the usage of blockchains to solve these issues by abandoning the centralized architecture of IoT environments. 87 articles were collected within the time scope 2015–2026 under the PRISMA protocol. Of these, 52 papers were analyzed, and this work highlights five types of blockchain-IoT integration architectures: namely, full blockchain integration, hybrid cloud-blockchain integration, fog/edge blockchain integration, consortium blockchain integration, and sidechain architecture integration. This work also proposes a new layer of IoT and blockchain integration, comprising an IoT device layer, gateway/edge layer, smart contract layer, blockchain network layer, and application layer. Future research issues include healthcare, fintech, smart agriculture, industrial IoT, supply chain management, smart cities, and decentralized vehicle authentication. Finally, this work finds that blockchain integration leads to integrity, trustworthiness, and security improvement, but is accompanied by delay and computational burden; however, new issues such as light-weighted consensus, artificial intelligence and blockchain, and regulation remain to be addressed.
Blockchain; Internet of Things; Decentralization; Smart Contracts; Distributed Ledger Technology; Systematic Literature Review; Cybersecurity; Edge Computing
An Intelligent Machine Learning Framework for Cybersecurity Threat Detection and Monitoring
As the number of IoT devices increases rapidly, cybersecurity threats are becoming more dynamic and intricate, necessitating more sophisticated and intelligent defense mechanisms. This work presents a unique intrusion detection system that enhances the security of the IoT by combining signature-based and anomaly-based techniques with AI-driven threat mitigation. The proposed framework is an amalgamation of Recurrent Neural Network (RNN) and Extreme Gradient Boosting (XGBoost) models that efficiently process network traffic, detect suspicious activities, and allow a real-time response to threats. According to the ToN-IoT dataset, data cleaning, normalization, and data balancing are employed to improve performance. The experimental analysis indicates the high-performance rate where the RNN and XGBoost have 98.5% and 98.7% accuracy respectively. The RNN model exhibits a marginally greater trade-off in terms of precision, recall and F1-score metrics. The efficacy of the proposed approach in the process of achieving high detection performance is validated by the comparative analysis with the currently existing models such as Naive Bayes, LSTM, CNN, and Random Forest. The research as a whole indicates the potential of DL and ensemble methods to improve the process of cybersecurity threat detection in the IoT context, as well as to address the issues of data imbalance and feature relevance.
Cybersecurity, Threat Detection, Anomaly Detection, Data Monitoring, Risk Mitigation, Intrusion Detection, Machine Learning, Predictive Analytics
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
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
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
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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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