Frontiers in Computer Science and Artificial Intelligence
Frontiers in Computer Science and Artificial Intelligence (FCSAI) is an international, peer-reviewed, monthly, open-access journal dedicated to advancing research and innovation in all areas of computer science and artificial intelligence. The journal provides a global platform for researchers, scholars, practitioners, and industry professionals to disseminate original and high-quality scientific contributions that address contemporary challenges and emerging trends in the digital world. FCSAI publishes cutting-edge research articles, review papers, case studies, technical reports, and short communications covering a broad spectrum of topics including—but not limited to—machine learning, deep learning, data science, natural language processing, computer vision, robotics, cybersecurity, software engineering, cloud and edge computing, intelligent systems, human–computer interaction, and emerging AI applications across disciplines. The journal is committed to promoting ethical research practices, fostering innovation, and encouraging interdisciplinary collaboration. Through a rigorous double-blind peer-review process, FCSAI ensures the publication of impactful, reliable, and scientifically sound research that contributes to the advancement of knowledge in computer science and artificial intelligence. As a monthly journal, FCSAI offers frequent publication opportunities, rapid dissemination of new findings, and up-to-date coverage of evolving research trends. The journal welcomes submissions from authors around the world and provides timely review, high visibility, and broad dissemination of published works. As part of Al-Kindi Center for Research and Development, UK, the journal benefits from a strong academic network and a commitment to supporting global scholarly communication.
Al-Kindi Center for Research and Development
Investigating the Security Risks of Gen-AI-Powered Phishing Attacks on University Students
Generative Artificial Intelligence (GenAI) is a paradigm shift in the cyber-threat environment as it allows generating hyper-realistic, personalised and scalable phishing campaigns. This is a study that explores how this technological development has intersected with the human susceptibility that has been there in the realm of higher education. This paper, which targets university students as a target population with a high level of digital exposure but possibly low levels of security awareness, also adopts a positivist, quantitative approach in its attempt to investigate empirically the security risks that AI-driven phishing presents to them. It was a cross-sectional online survey (N=63), in which a simulated GenAI phishing mail was used as a behavioural stimulus. Descriptive and inferential statistics demonstrate a crucial lack of connection: self-reported confidence in detection was at a moderate level (M=3.13/5), whilst behavioural susceptibility to it is alarmingly high, with 81.7 percent stating that they were likely to choose to click the malicious link. It was discovered that there was a deep institutional training deficit, as 90.1% of respondents had not been trained in cybersecurity at university and analyses (kh2(1)) demonstrated that prior training had no significant protective effect on phishing experience (kh2(1) =0.948, p=.330). Moreover, existing guidance in universities was perceived as insufficient by 76.1% of them. The results highlight a severe overconfidence paradox and a systemic defect in the modern pedagogical models to help counteract AI-improved threats. This paper has determined that the human firewall within the academic context is highly misaligned and recommends an immediate, strategic move to compulsory, simulation-based training programmes that are specifically crafted to take into account the advanced affordances of GenAI in social engineering attacks.
AI-Powered Phishing, Generative AI, Cybersecurity Awareness, University Students, Human Factor, Susceptibility, Cybersecurity Training
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Machine Learning Framework for Liver Cirrhosis Stage Prediction Using Clinical and Biochemical Features
Chronic liver disease is characterized by liver cirrhosis, a progressive condition associated with high morbidity and mortality rates globally. Accurate classification of disease stage is essential for timely clinical intervention, comprehensive disease assessment, and the development of individualized treatment plans. This study proposes an interpretable machine learning model to predict the stage of liver cirrhosis using routinely collected clinical and biochemical parameters. A comprehensive exploratory data analysis was conducted to identify patterns and relationships among variables and to examine feature distributions across different stages of disease progression. After data preprocessing, including handling missing values, feature encoding, and normalization, multiple machine learning algorithms were evaluated, including Logistic Regression, Random Forest, Support Vector Machine, K-Nearest Neighbors, Gradient Boosting and Extreme Gradient Boosting (XGBoost). Model performance was assessed using accuracy, precision, recall, F1-score, and confusion matrix analysis. Experimental results demonstrated that ensemble-based models outperformed traditional machine learning methods, with XGBoost achieving the highest performance in multi-class stage prediction. Feature importance analysis identified clinical blood parameters such as bilirubin, albumin, platelet count, prothrombin time, and cholesterol as significant predictors of disease severity. The proposed framework is reliable, interpretable, and suitable for automated liver cirrhosis stage classification, offering valuable support to clinicians in early diagnosis and risk stratification. Results show the promising contributions of machine learning methods to decision support systems in healthcare, as well as their applicability to enhance the management of chronic liver diseases via leveraging data and predictive modelling.
Liver Cirrhosis, Disease Stage Prediction, Clinical Decision Support System, Healthcare Analytics, Machine Learning, Multi-Class Classification, XGBoost.
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Autonomous Decision Intelligence for Secure and Resilient Digital Enterprises
Few forces have reshaped organizational life as quickly as digital transformation. The way firms create value, manage risk, and hold their competitive ground now depends on systems that grow more entangled with one another every year. A typical enterprise sits at the center of a constant flow of data drawn from its operations, its cloud platforms, the sensors embedded in its products, its planning systems, and the many places where it meets its customers. Artificial intelligence (AI), machine learning, big data analytics, blockchain, and cybersecurity have each made it easier to turn that flow into useful judgment. Yet most organizations still adopt these tools one at a time, and the habit quietly erodes the strategic payoff that integration could deliver. This paper sets out an Autonomous Decision Intelligence (ADI) framework that gathers AI, cybersecurity, big data analytics, blockchain, and management information systems (MIS) into one coherent architecture built for resilient digital enterprises. The argument rests on a synthesis of recent work in decision intelligence, predictive analytics, business intelligence, federated learning, cloud computing, blockchain governance, cyber threat intelligence, and enterprise risk management. From that body of evidence, we construct a conceptual model for organizational decision-making that is trustworthy and capable of improving itself over time. The framework gives weight to secure data governance, explainable AI, privacy-preserving analytics, blockchain-based trust, cyber-resilience, and intelligent automation. It then asks how such a design might reinforce critical infrastructure protection, supply chain resilience, economic sustainability, IT project governance, and day-to-day agility. The contribution is at once theoretical and practical, because it shows how converging technologies can turn conventional decision-support systems into adaptive ecosystems that learn. Organizations that pair AI-driven analytics with strong security and decentralized trust look best placed to absorb uncertainty, keep operating under stress, and pursue digital transformation that lasts.
Autonomous Decision Intelligence; Artificial Intelligence; Big Data Analytics; Blockchain; Cybersecurity; Management Information Systems; Digital Transformation; Business Intelligence; Enterprise Resilience
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Artificial Intelligence in Crime Scene Reconstruction: Using Machine Learning for Predictive Analysis and Scenario Simulation
Crime scene reconstruction remains a critical yet complex component of forensic investigation, requiring the integration of heterogeneous evidence sources to infer plausible sequences of criminal events. Traditional reconstruction methods rely heavily on expert interpretation, which can introduce subjectivity and limitations when dealing with large-scale or incomplete forensic data. This study proposes a machine learning-based framework for automated crime scene reconstruction, integrating predictive analytics and scenario simulation to enhance investigative decision-making. The framework leverages structured forensic features, including evidence distribution, temporal response characteristics, and scene complexity indicators, to model reconstruction outcomes using a combination of classical machine learning, ensemble methods, and deep learning architectures. In addition, sequential models and hybrid neural networks are employed to capture temporal dependencies and local evidence patterns, enabling more robust interpretation of event sequences. Scenario simulation is incorporated to generate multiple plausible crime narratives under varying evidence conditions, supporting probabilistic reasoning in uncertain investigative environments. The findings indicate that advanced ensemble and hybrid deep learning models offer superior capability for capturing nonlinear and temporal relationships in forensic datasets, leading to more consistent and reliable reconstruction outcomes. The proposed approach demonstrates the potential of artificial intelligence to augment forensic investigation processes by improving reconstruction accuracy, enhancing scenario exploration, and supporting more structured evidence interpretation in complex crime scenes.
Copyright (c) 2026 Frontiers in Computer Science and Artificial Intelligence
Cost-Aware Prompt Engineering: A Governance Architecture for Optimizing Enterprise AI Agents
As enterprises deploy large language model (LLM)-based and agentic AI systems across critical business functions, prompt engineering has emerged as a significant operational bottleneck. Prompts that are poorly structured, excessively verbose, or manually tuned without systematic comparison introduce hidden costs: elevated token consumption, increased inference latency, inconsistent output quality, and deployment risk. Despite its importance, prompt engineering in most enterprise environments remains an informal, trial-and-error discipline without governance, reproducibility, or measurable quality criteria. This paper proposes a Cost-Aware Prompt Engineering Governance Architecture (CAPEGA) designed for enterprise production environments. The architecture integrates four core components: iterative prompt optimization using SELF-REFINE techniques, structured prompt compression with instruction-preservation controls, metric-driven prompt comparison with cost and latency artifact generation, and CI/CD-ready governance workflows for prompt promotion and rollback. CAPEGA's contribution is architectural integration rather than novel algorithmic techniques: it combines established optimization methods into a unified, production-ready governance pipeline. Together, these components transform prompt engineering from an ad hoc craft into a reproducible, auditable engineering discipline. The framework is model-agnostic in design, model-specific in execution, and intended for integration into existing enterprise AI release pipelines. By establishing measurable quality gates for prompts, CAPEGA enables organizations to reduce token overhead, improve output consistency, control inference cost, and generate audit-ready evidence for production deployment decisions. An implementation architecture with component-level design is presented alongside the conceptual framework.
Prompt Engineering, Enterprise AI, Cost-Aware AI, SELF-REFINE, LLM Optimization, AI Governance, CI/CD, Agentic AI, Token Efficiency, Production Readiness
Deep Learning and Explainable Benchmarking for Early Parkinson’s Disease Detection Using Speech Signals in the United States
Early-stage Parkinson’s disease (Early PD) detection using speech analysis has emerged as a promising and non-invasive approach for improving neurological healthcare in the United States. However, existing studies remain difficult to compare due to variations in datasets, speech tasks, languages, evaluation strategies, and definitions of Early PD. To address these limitations, this study proposes a comprehensive benchmark framework for speech-based Early PD detection using speaker-independent evaluation protocols to ensure fair, reproducible, and clinically reliable comparisons. The proposed benchmark evaluates multiple speech tasks under different training-resource settings and provides multidimensional performance analysis based on dataset characteristics, gender, aggregation level, and disease severity. Experimental findings offer actionable insights into the robustness and generalizability of speech-based Parkinson’s detection systems. The proposed benchmark establishes a reliable reference framework for advancing explainable, scalable, and clinically meaningful Early PD detection technologies within modern U.S. healthcare and neurological diagnostic systems.
Early-Stage Parkinson’s Disease, Parkinson’s Disease Detection, Speech-Based Diagnosis, Neurological Disorder Detection, Machine Learning, Deep Learning, Speech Analysis, Explainable Artificial Intelligence (XAI), Benchmark Framework, Speaker-Independent Evaluation, Healthcare AI, U.S. Healthcare System, Clinical Decision Support, Voice Biomarkers, Neurological Healthcare
Explainable Deep Fusion Transfer Learning for Automated Lung Disease Classification in the U.S. Healthcare Environment
Lung diseases continue to impose a significant healthcare burden in the United States, making early and accurate diagnosis essential for effective treatment and patient management. This study proposes an explainable fusion-based transfer learning framework for automated lung disease classification using chest X-ray and CT scan images. The proposed system integrates state-of-the-art deep learning architectures, including ResNet50, ResNet101, and EfficientNetB0, along with feature-level and decision-level fusion strategies to enhance classification accuracy and robustness. Experimental results demonstrate that the hybrid fusion models outperform standalone architectures, achieving 95.68% accuracy for chest X-ray classification and 99.61% accuracy for CT scan classification. To improve transparency and clinical reliability, Explainable Artificial Intelligence (XAI) methods such as SHAP, LIME, and Integrated Gradients were incorporated to visualize diagnostically relevant regions. The proposed framework offers an accurate, interpretable, and reliable AI-driven solution for pulmonary disease diagnosis in modern U.S. healthcare systems.
Lung Disease Classification, Chest X-ray, CT scan, Transfer Learning, Deep Learning, Explainable Artificial Intelligence
AI-Based Schedule Overrun Prediction in Technical Projects Using Gradient Boosting Machine for Decision Support
The increasing complexity of technical projects has challenged the effectiveness of traditional project management approaches, particularly in accurately predicting schedule performance. Conventional methods often rely on expert judgment and static planning techniques, limiting their ability to capture dynamic project conditions and complex relationships between variables. This study investigates the role of Artificial Intelligence, specifically the Gradient Boosting Machine (GBM), in enhancing schedule management through a data-driven approach. A predictive model was developed using a publicly available project management dataset obtained from Kaggle, consisting of 4517 project instances. The dataset includes key variables such as project duration, total cost, team size, risk factor, and client satisfaction. The proposed model was applied to predict Schedule Overrun (%) as a measure of project performance. The results demonstrate strong predictive performance, with an R² value of 0.835, RMSE of 7.128, and MAE of 5.946. These findings indicate that the model is capable of capturing complex patterns and relationships within project data. Feature importance analysis revealed that risk factor and project duration are the most influential variables affecting schedule performance, followed by client satisfaction. These results highlight the importance of managing project uncertainty and timeline characteristics to improve overall project outcomes. The study confirms that AI-based models can provide valuable predictive insights to support proactive, data-driven decision-making in project management. Future research may focus on expanding the dataset, incorporating additional contextual variables, and exploring advanced modeling techniques to further enhance predictive accuracy and practical applicability.
Artificial Intelligence; Machine Learning; Project Management; Schedule Overrun; Gradient Boosting Machine; Predictive Modeling; Data-Driven Decision-Making
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API-Driven Enterprise Integration Architecture for Digital Transformation in Manufacturing: A Practitioner Framework Based on Microsoft Azure Integration Services
As the information technology (IT) and operational technology (OT) environment becomes more diverse, the modern manufacturing enterprise is being challenged to connect data across systems in real time and to deliver operational agility. Enterprise integration architecture — the field of designing, deploying, and governing the mechanisms that connect the various systems to exchange information and services — has thus become a key component of a manufacturing organization's strategic efforts to achieve digital transformation goals. This paper outlines a full, practitioner-based framework for API-driven enterprise integration in the manufacturing environment based on real-world experience designing and delivering enterprise-wide integration programmed across over 300 systems across more than 30 different business units and third-party ecosystems. It positions Azure API Management, Azure Logic Apps, Azure Service Bus, and Azure Function Apps, as well as Azure Data Factory into the unified architecture of Azure, which includes the messaging strategy, identity governance, DevOps automation, and operational observability. A tenfold hierarchy of Azure integration services is provided with each service placed in its manufacturing application context. The paper also explores the special integration challenges of manufacturing environments, such as co-existence of on-premise legacy applications with cloud-native architectures, Electronic Data Interchange (EDI) requirements of supply chain partners, and governance needs for large volumes of APIs. The empirical results of a real-world programme, including deployment of more than 50 APIs and workflows, achieved an uptime of 99.9 per cent, are provided to confirm the proposed framework. The paper wraps up with some forward-looking comments on the role of event-driven architecture, artificial intelligence and autonomous integration in the manufacturing enterprise of the future.
Enterprise Integration Architecture, Azure Integration Services, API Management, Digital Transformation, Manufacturing IT, Logic Apps, Service Bus, ETL, EDI, Zero Trust, DevOps, CI/CD, IaC, Event-Driven Architecture.
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Modern Manufacturing Cloud Applications to Improve Reliability
One of the biggest technological trends of the 21st century is rapid cloudification of advanced manufacturing. This paper will review the cloud-based applications that are transforming reliability in today's modern manufacturing environments and analyze them in the context of the author's current industry experience in enterprise integration architecture, designing solutions across Microsoft Azure and AWS, and personal experience in large-scale digital transformation programs within the chemical manufacturing sector. The study covers five main technology areas: Cloud Manufacturing Execution Systems (MES); Industrial Internet of Things (IIoT) and edge computing; predictive maintenance platforms with AI and machine learning; digital twin technology; and cloud Enterprise Resource Planning (ERP) with supply chain management. The paper outlines the theoretical underpinning, the deployment status, reliability results, and implementation issues, particularly security, for each domain. The quantitative results include an increase in Overall Equipment Effectiveness (OEE) by 16% with cloud-connected systems, a decrease of 39% in unplanned IT downtime, and improvements in OEE by 5-12 percentage points with the use of a digital twin. The research also shows that manufacturing has been the most cyber-attacked industry in the world for the past four years, which means that reliability improvements must be weighed against new attack vectors. Finally, the paper presents a cloud adoption strategy and research directions.
Cloud computing, manufacturing reliability, predictive maintenance, Industry 4.0, digital twin, IoT, cloud MES, OEE and cyber security.
AI-Orchestrated Enterprise Integration Platforms for Intelligent Automation
The rapid growth of digital transformation has increased the demand for intelligent, scalable, and adaptive enterprise integration systems capable of connecting diverse applications, cloud services, APIs, and business processes. This paper explores the concept of AI-Orchestrated Enterprise Integration Platforms for Intelligent Automation, focusing on how Artificial Intelligence (AI) enhances enterprise interoperability, workflow optimization, and decision-making across modern organizations. Traditional integration platforms often rely on static configurations and manual process management, which limit flexibility and responsiveness in dynamic business environments. In contrast, AI-driven orchestration introduces intelligent automation capabilities such as predictive analytics, autonomous workflow execution, anomaly detection, intelligent API management, and real-time data synchronization. The study examines the integration of machine learning, natural language processing, robotic process automation (RPA), and cloud-native architectures within enterprise integration platforms to enable self-optimizing business ecosystems. It also highlights how AI-powered orchestration improves operational eﬃciency, reduces human intervention, enhances scalability, and accelerates digital innovation across industries including finance, healthcare, manufacturing, logistics, and e-commerce. Furthermore, the paper discusses key challenges such as data security, governance, interoperability, ethical AI adoption, and infrastructure complexity.By analyzing emerging trends and practical implementation strategies, this research demonstrates that AI-orchestrated integration platforms represent a transformative approach to intelligent enterprise automation, enabling organizations to build adaptive, resilient, and data-driven operational frameworks suitable for the evolving demands of Industry 4.0 and cloud computing environments.
Artificial Intelligence (AI), Intelligent Automation, Enterprise Integration Platforms, API Orchestration, Cloud Computing, Machine Learning, Robotic Process Automation (RPA), Workflow Automation, Digital Transformation, Enterprise Systems, Cloud-Native Architecture, Predictive Analytics, Business Process Automation, Industry 4.0, Smart Enterprises.
Explainable Deep Learning and Transformer Models for Applied AI: A Review Across Medical, Agricultural, Industrial, and Business Domains
Explainable deep learning and transformer models are increasingly applied to consequential decisions in healthcare, agriculture, industry, business, smart infrastructure, and human-centered AI. Their widespread adoption is motivated not by architectural novelty alone, but by the recognition that deep representations must be interpretable, auditable, and trustworthy in domains where model outputs influence patient diagnoses, crop management, infrastructure safety, organizational strategy, and accessibility. This review identifies and critically examines direct explainable AI evidence—spanning post-hoc interpretability in oncological imaging, attention-based explanation in transformer-based disease classifiers, visual XAI in lightweight agricultural transformers, and ensemble post-hoc analysis in affective computing—alongside transformer evidence across Swin, MaxViT, EfficientFormer, and hybrid vision transformer architectures. It situates these within a broader applied-AI ecosystem including conventional ML baselines, graph neural networks, Bayesian physics-guided models, privacy-preserving systems, and enterprise AI. Synthesis reveals that while explainability mechanisms have diversified across visual, attentional, post-hoc, and knowledge-structured approaches, explanation validity, the degree to which explanations faithfully represent model reasoning—is rarely formally evaluated. A ten-direction research agenda addresses this gap alongside robustness, privacy, edge deployment, human oversight, and governance-aware reporting.
Explainable AI, deep learning, Vision transformers; Post-hoc interpretability; medical AI, agricultural AI, applied AI, trustworthy AI
Cross-Domain Applications of Artificial Intelligence: A Systematic Review of Models, Data Modalities, and Deployment Readiness
Artificial intelligence is applied across a wide and growing range of domain healthcare, agriculture, industry, business analytics, cybersecurity, smart infrastructure, assistive technologies, education, and sustainability, yet cross-domain synthesis of AI evidence has lagged behind domain-specific development. A systematic cross-domain review must classify studies not only by application area, but also by data modality, model architecture, decision-support function, deployment pathway, and deployment-readiness concern, to reveal the structural patterns and gaps that single-domain reviews cannot expose. This review presents systematic-style evidence mapping analysis of corpus using a seven-axis taxonomy: application domain, data modality, model family, decision-support function, deployment pathway, deployment-readiness concern, and evidence role. Seven application domains are synthesized, healthcare and biomedical AI, human-centered and assistive AI, agriculture and sustainability, industrial monitoring, smart infrastructure and IoT, business and enterprise analytics, and cybersecurity and distributed intelligence, across ten model families from conventional ML through vision transformers, graph neural networks, Bayesian physics-guided models, generative AI, and federated learning systems. Synthesis reveals that model architecture diversity has advanced substantially, while deployment-critical properties, validated explainability, privacy-preserving inference, robustness, human oversight, and governance-aligned reporting, remain inconsistently addressed. A ten-direction research agenda identifies the most consequential future priorities for cross-domain deployable AI. Note that full PRISMA reporting would require explicit database-search records, screening procedures, and eligibility criteria beyond what this curated corpus provides.
Cross-domain AI, Systematic evidence mapping, Deployment readiness, Explainable AI, vision transformers, Federated learning, Trustworthy AI, Data modalities
Artificial Intelligence for High-Stakes Decision Support: Architectures, Applications, and Deployment Challenges
Artificial intelligence (AI) is increasingly embedded in consequential decision-making processes across healthcare, assistive technologies, smart infrastructure, agriculture, business analytics, cybersecurity, and sustainability. Unlike general-purpose AI deployments, high-stakes decision support demands not only predictive accuracy but also explainability, robustness, privacy, scalability, human oversight, and governance readiness. This structured critical review synthesizes to map the current landscape of AI for high-stakes decision support using a four-axis taxonomy: application domain, data modality, architecture family, and deployment concern. The review identifies six application domains, healthcare and biomedical decision support, human-centered and assistive AI, smart infrastructure and cyber-physical systems, agriculture and sustainability, business and enterprise decision support, and cybersecurity and distributed intelligence, and eight architecture families ranging from conventional machine learning and convolutional neural networks to vision transformers, graph neural networks, Bayesian models, generative AI, and federated learning systems. The synthesis reveals that while significant architectural advances have been made, deployment-critical properties such as uncertainty quantification, privacy-preserving inference, real-time feasibility on edge devices, and governance-aligned reporting remain inconsistently addressed. Future research must prioritize cross-domain benchmarking, trustworthy and auditable AI pipelines, human-in-the-loop frameworks, and evidence maturity standards appropriate for high-stakes contexts. This review provides an evidence-grounded taxonomy and actionable research agenda for researchers and practitioners to build the next generation of responsible AI decision-support systems.
Artificial intelligence; High-stakes decision support; Trustworthy AI; Explainable AI; Human-in-the-loop AI; Federated learning; Graph neural networks; Vision transformers; Uncertainty quantification; AI governance
Adaptive Threat Detection Framework for IoT-Enabled Healthcare, Financial, and Connected Systems in the United States
Internet of Things (IoT) technologies have significantly transformed healthcare, finance, and smart connected systems by providing real-time communication and automation and intelligent data processing. Although all these developments have occurred, the fact that such a large number of IoT devices are becoming interconnected has created significant cybersecurity concerns and new opportunities for sophisticated cyber threats like Distributed Denial of Service (DDoS), botnet attacks, brute force attacks, spoofing, and malware attacks to target critical infrastructures. In IoT environments, which can be dynamic and heterogeneous, traditional intrusion detection systems are not effective in detecting new and changing attack patterns. This work introduces an Adaptive AI-Driven Threat Detection Framework aimed at safeguarding IoT healthcare, financial, and connected systems, which entails intelligent detection of anomalies and threat analysis in real time. The proposed framework leverages a realistic IOT network traffic dataset called CICIoT2023 that includes a wide variety of attack categories and realistic network traffic for modern cybersecurity research. Technologies such as Advanced machine learning algorithms, deep learning algorithms including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) and Random Forest are applied to classify malicious traffic and benign traffic efficiently. The framework includes adaptive learning mechanisms that are able to continually analyze the behaviors of the network and increase the accuracy of intrusion detection and decrease false positive rates. To improve the performance of the system and to optimize the computational time, data pre-processing, feature extraction, normalization, and model optimization techniques are used. Experimental results prove that the proposed scheme is accurate, precise, recall and F1-score in the detection of cyber threats in heterogeneous IoT networks. This study plays a key role in advancing the field of scalable and intelligent cybersecurity solutions designed to safeguard sensitive healthcare data, financial transactions, and smart infrastructures that are integrated into the digital landscape. The proposed architecture also offers important lessons in the integration of AI and adaptive security mechanisms into future IoT cybersecurity solutions.
Internet of Things (IoT), Adaptive Threat Detection, Artificial Intelligence, Intrusion Detection System, Deep Learning and Cybersecurity
Generative AI and IoT-Driven Autonomous Manufacturing Ecosystems Transition from Industry 4.0 to Industry 5.0
There are ongoing significant changes happening in the manufacturing sector due to fast-moving digital innovation. The introduction of the concept of smart manufacturing in the Industry 4.0 model happened due to the integration of the Internet of Things, cyber-physical systems, cloud computing, robotics, and big data analytics. Nonetheless, there are a number of drawbacks in conventional Industry 4.0 systems, such as low adaptability, a lack of human-focused collaboration, security issues, and dependency on existing automation schemes. Therefore, it is evident that Industry 5.0 is coming into play. This paper explores the potential of Generative Artificial Intelligence (Generative AI) and the Internet of Things (IoT) in creating autonomous manufacturing systems and facilitating the shift from Industry 4.0 to Industry 5.0. The use of Generative AI provides functionalities including autonomous decision-making, dynamic production planning, process optimization using artificial intelligence, and product design aided by artificial intelligence, whereas IoT offers features such as data gathering in real-time, machine connectivity, predictive maintenance, and decentralized communication in manufacturing systems. A conceptual and literature-based research approach is used in this paper to examine the current trends in smart manufacturing technologies and present a framework for the integration of Industry 5.0 manufacturing systems. Human-AI cooperation, intelligent automation, and sustainability are emphasized as important components in the future of manufacturing. The presented framework shows how Generative AI and IoT can transform the smart factory into an intelligent, autonomous, and sustainable manufacturing system.
Operationalizing Post-Quantum Cryptography in Multi-Cloud Environments
The emergence of quantum computing is a serious threat to traditional cryptographic algorithms, making it critical to transition to post-quantum cryptography (PQC). This work proposes a holistic conceptual and analytical framework for deploying PQC in multi-cloud environments where the complexity of different infrastructure, workloads and key management schemes pose significant security and efficiency problems. The proposed framework for crypto-agility follows a layered approach, with a cryptographic abstraction layer, a policy-based orchestration engine, a centralized hardware security module (HSM) management plane, and an ongoing monitoring module to facilitate a smooth transition from classical to hybrid and fully quantum-resistant cryptographic systems. He researches identifies and critically analyzes major challenges in deploying PQC technologies in multi-cloud environments, such as inter-cloud interoperability, computational complexity, latency, key management, and regulatory and compliance considerations. The framework tackles these challenges by incorporating hybrid cryptographic protocols that blend classic cryptographic algorithms with NIST-approved PQC algorithms, namely ML-KEM (FIPS 203) and ML-DSA (FIPS 204), to maintain backward compatibility and increase security against quantum-powered adversaries. Moreover, the framework proposes dynamic policy-driven algorithm selection to implement context-sensitive cryptographic enforcement based on sensitivity labels, security threats, and regulatory considerations. An experimental framework is proposed to assess the efficiency and scalability of PQC algorithms deployed in a multi-cloud environment, including metrics such as CPU usage, memory usage, latency and throughput with different workloads. Although this research is conceptual, it draws on previous benchmarking to discuss the securityperformance trade-offs associated with PQC. The results highlight the need for centralized trust anchors, crypto-agility and hybrid deployment models to address risks from cryptographic diversity and ensure the sustainability of security operations. This study advances the research on quantum-safe cloud security by offering a systematic, scalable and policy-oriented framework for PQC deployment. It provides practical guidance for businesses, cloud architects, and security professionals on how to safeguard against quantum vulnerabilities and ensure efficient operations in distributed systems by implementing quantum-safe cryptographic infrastructures.
Post-Quantum Cryptography (PQC), Multi-Cloud Security, Crypto-Agility, ML-KEM, ML-DSA, Hybrid Cryptography, Hardware Security Module (HSM), Quantum-Safe Security, Cloud Computing, Key Management, Cryptographic Migration, Distributed Systems Security, Policy-Driven Security, Quantum Computing Threats 1
Artificial Intelligence and Data Analytics in U.S. Mental Health Insurance: Predictive Risk Modeling, Cost Efficiency, and Equitable Access
The integration of Artificial Intelligence and data analytics into the United States mental health insurance system is becoming structurally necessary. Traditional actuarial models rely heavily on backward looking claims data, which fail to capture early stage mental health risks and lead to delayed interventions, rising costs, and unequal access. This study examines how Artificial Intelligence driven predictive risk modeling can transform mental health insurance by identifying high risk individuals earlier, improving resource allocation, and enhancing care outcomes while controlling costs. Using machine learning techniques such as supervised learning, natural language processing, and behavioral pattern recognition, insurers can integrate diverse data sources including electronic health records, social determinants of health, and real time behavioral indicators into dynamic risk assessments. The paper also evaluates the economic impact of Artificial Intelligence adoption and shows that predictive analytics can reduce avoidable hospitalizations, stabilize claims patterns, and improve cost efficiency through targeted preventive care. However, these benefits are not automatic. Without careful design, Artificial Intelligence systems can reinforce existing disparities, especially among underserved populations with limited data representation. This study therefore examines issues related to algorithmic bias, transparency, and regulatory compliance, emphasizing the importance of ethical frameworks and governance mechanisms. Finally, the research explores how Artificial Intelligence can promote equitable access by supporting personalized care pathways and expanding insurance inclusivity, particularly in rural and low income populations. The findings indicate that Artificial Intelligence has strong potential to reshape mental health insurance, but its success depends on disciplined implementation, policy alignment, and accountability. The study concludes that the real advantage for insurers lies not in adopting Artificial Intelligence, but in using it responsibly to balance financial performance, risk management, and social equity.
Data Analytics, Mental Health Insurance, Predictive Risk Modeling, Cost Efficiency, Algorithmic Bias, United States Healthcare System, Insurance Analytics, Behavioral Health Data
Artificial Intelligence and Big Data for Precision Medicine: A Review of Bioinformatics-Driven Healthcare Applications
Healthcare is in the middle of a quiet but profound shift. Genomic sequencers, hospital information systems, wearables and imaging archives now generate data faster than clinicians can read it, and that flood is reshaping what “evidence-based care” means. We review more than forty recent studies that bring artificial intelligence (AI), machine learning and big-data analytics into bioinformatics and precision medicine, spanning oncology, drug discovery, cardiology, neurology, public-health surveillance and healthcare operations. Reported accuracies and AUCs range from roughly 80% in early drug-discovery pipelines to above 94% in deep-learning-based pancreatic and breast imaging. Yet our reading also suggests a more cautious story: many models still suffer from limited external validation, opaque decision logic and uneven access to high-quality multi-omics data. We propose a layered conceptual framework that connects heterogeneous data sources, federated and privacy-preserving pre-processing, predictive and explainable AI engines, and downstream clinical applications. The paper closes with a discussion of remaining barriers, interpretability, fairness, regulatory uncertainty and workflow integration and outlines research directions for the next several years.
Precision medicine, bioinformatics, machine learning, multi-omics, explainable AI, big data analytics, clinical decision support
Failure-Mode Analysis, Hallucination Detection, and Prompt-Injection Testing: A Production-Readiness Framework for Enterprise Agentic AI
As large language models increasingly move from experimental chat interfaces into enterprise workflows, the primary challenge is no longer model capability alone but production trustworthiness. Agentic AI systems introduce new risk surfaces because they can retrieve information, reason across context, call tools, act on user intent, and interact with external systems. This article proposes a practical production-readiness framework centered on three pillars: failure-mode analysis, hallucination detection, and prompt-injection testing. Failure-mode analysis helps teams identify where agentic systems can break under real operating conditions. Hallucination detection evaluates whether generated outputs remain faithful, factual, and grounded in approved sources. Prompt-injection testing examines whether malicious or conflicting instructions can override system intent, expose sensitive information, or trigger unauthorized actions. Together, these practices convert AI reliability from an informal review process into an evidence-driven engineering discipline. The article argues that enterprises should treat these controls as mandatory gates before deploying large language model and agentic AI systems into high-impact business workflows.
Agentic AI; LLM evaluation; hallucination detection; prompt injection; AI red teaming; enterprise AI governance; failure-mode analysis; production readiness
AI-Based PCB Layout Constraint Manager: Intelligent Automation for High-Speed PCB Design Rule Enforcement
Modern printed circuit board (PCB) design for high-performance computing systems — including server-grade processors, graphics processing units, and AI accelerators — demands increasingly rigorous layout constraints. Traditional constraint management systems rely on static rule sets, manual configuration, and repetitive engineer oversight, all of which introduce bottlenecks and errors at scale. This article presents an AI-based PCB Layout Constraint Manager (AI-PLCM), a system developed at Advanced Micro Devices, Inc. (AMD) to intelligently automate the generation, validation, and optimization of PCB layout constraints. The proposed system leverages machine learning models trained on historical board designs, natural language processing for constraint specification, and graph-neural-network-based routing analysis to deliver real-time constraint checking and adaptive recommendations. Results from deployment across multiple AMD product lines demonstrate a 47% reduction in design rule violations at tape-out, a 31% improvement in first-pass layout success rate, and significant reductions in engineering review cycle time. This work underscores the transformative potential of AI integration in electronic design automation (EDA) workflows.
PCB design, layout constraints, design rule checking (DRC), electronic design automation (EDA), machine learning, graph neural networks, natural language processing, high-speed design, signal integrity, AMD
Intelligent Cloud Monitoring Using AIOps in the Microsoft Azure Ecosystem
The growing complexity of cloud-native applications has made conventional monitoring techniques ineffective for ensuring system performance and reliability. This research explores the use of Artificial Intelligence for IT Operations (AIOps) in the Microsoft Azure environment to achieve intelligent, automated and predictive cloud monitoring. Our system leverages Azure Monitor, Log Analytics and Azure Machine Learning to ingest and analyses extensive telemetry data, such as logs, metrics and distributed traces. An integrated machine learning framework, which combines unsupervised anomaly detection (such as clustering and statistical thresholding) with supervised learning models is used to detect anomalies in real time. The framework is validated with cloud workload datasets from cloud-native microservices deployed in the Azure Kubernetes Service (AKS). Self-healing mechanisms through automation workflows using Azure Automation and Logic Apps help automate responses. The results of the experimental studies show that the proposed AIOps approach enhances anomaly detection rates up to 92% and lowers mean time to resolution (MTTR) by around 30-40% when compared to traditional monitoring systems. The framework is scalable, flexible and cost-effective in cloud ecosystems. This study demonstrates the value of combining AI analytics with cloud-based observability tools to shift IT operations from reactive to proactive and self-healing modes.
Keywords AIOps; Cloud Computing; Microsoft Azure; Azure Monitor; Log Analytics; Machine Learning; Predictive Analytics; Anomaly Detection; Cloud-Native Systems; IT Operations Automation; Self-Healing Systems; Observability.
B2B Integration Patterns in the Chemical Industry
B2B integration patterns in the chemical industry are structured methods, models and communication protocols that support the exchange of information and coordination of activities between manufacturers, suppliers, distributors, logistics partners, and regulatory agencies. The chemical industry is tightly regulated, safety-sensitive and globally integrated, necessitating robust B2B integration patterns to enable efficient operations, compliance, and supply chain resilience.This paper explores the main B2B integration patterns used in the industry, such as point-to-point, hub-and-spoke, enterprise service bus (ESB), and API-led integration. Although traditional systems like Electronic Data Interchange (EDI) are still prevalent for exchanging standardized documents, they are increasingly augmented or replaced by more agile integration approaches that use Application Programming Interfaces (APIs), cloud-based integration services (iPaaS) and event-driven integration patterns.The paper also discusses the importance of industry standards like GS1 (formerly Chemical Industry Data Exchange - CIDX) that specify data exchange protocols and formats for the chemical supply chain. The paper assesses the impact of these integration patterns on key processes such as order-to-cash, procure-to-pay, stock management, and regulatory compliance. Special attention is given to the secure sharing of safety and regulatory data such as Safety Data Sheets (SDS) to comply with international regulations such as REACH and the Globally Harmonised System of Classification and Labelling of Chemicals (GHS). Additionally, the research examines emerging patterns such as digital transformation programs, integration in the cloud, blockchain for traceability, and artificial intelligence for predictive analytics in supply chain. It also considers key issues with data integration, legacy systems and security. Particular emphasis is placed on contemporary security considerations in B2B integration, such as data sovereignty, partner identity and access management, and API security (including OAuth 2.0 and certificate authentication), which are critical for safeguarding data in a connected ecosystem. Overall, successful integration patterns improve visibility, agility and collaboration within the chemical supply chain. Through the integration of strategies with industry standards and the adoption of emerging security and digital technologies, companies can enhance efficiency, compliance and maintain their competitive edge in a rapidly evolving data-driven world.
Keywords: B2B integration, chemical industry, Electronic Data Interchange (EDI), Application Programming Interfaces (APIs), supply chain integration, data interoperability, cloud integration (iPaaS), digital transformation, regulatory compliance, Chemical Industry Data Exchange, REACH Regulation, Globally Harmonized System of Classification and Labelling of Chemicals, event-driven architecture, enterprise integration patterns, chemical supply chain.
Governance Readiness Beyond Predictive Performance: An Empirical Benchmark for Higher-Education Early Warning Systems
Higher-education early warning systems (EWS) are predominantly evaluated on predictive discrimination, yet institutional deployment requires that models simultaneously satisfy calibration, explanation stability, subgroup fairness, and operational feasibility. This paper proposes and empirically evaluates a structured governance-readiness benchmark comprising four measurable domains applied to the 2020 Beginning Postsecondary Students Longitudinal Study (BPS:20/22), a nationally representative complex survey of approximately 22,320 first-time undergraduates representing roughly 3.3 million students nationally. The study compares a gradient-boosted classifier (XGBoost) and an institution-aware multilevel logistic regression under a survey-weighted evaluation protocol with balanced repeated replication (BRR) variance estimation and bootstrap explanation stability testing. BPS:20/22 is used as a national governance benchmark; it does not represent a real-time campus intervention system. Findings indicate that the model achieving higher discrimination (AUC: 0.814 vs. 0.772, BRR 95% CIs: [0.801, 0.827] and [0.758, 0.786]) exhibits substantially lower explanation stability (rank-stability ρ_S: 0.713 vs. 0.893), larger subgroup disparities across race/ethnicity and income dimensions, and lower operational efficiency within the constrained alert threshold. Under four of five institutional priority scenarios summarizing predictive, calibration, stability, and fairness dimensions, the governance-readiness composite favors the institution-aware model. Operational actionability, evaluated as a parallel deployment constraint, similarly indicates that accuracy-based selection may underestimate accountability risk under discrimination-centered evaluation. The paper concludes with a recommendation for Algorithmic Impact Statements as a recommended minimum governance disclosure practice and provides a reproducible benchmark framework subject to restricted-use data access for institutional EWS review.
Early warning systems; governance readiness; explainability stability; algorithmic fairness; complex survey design; higher education analytics; BPS:20/22; benchmark evaluation
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
A Systematic Literature Review on the Taxonomy of Ransomware Attacks
Ransomware attacks actively threaten different entities including individuals along with businesses while endangering fundamental infrastructure through software weaknesses and deceptive email practices. The absence of standardized classification criteria in modern ransomware taxonomies hinders effective threat information sharing. This lack of uniformity also complicates response coordination. The research develops an extensive classification structure to systematize ransomware attack categorization by examining vector encryption techniques and ransom payment approaches. The research adopted a systematic literature review approach which aligns with PRISMA standards to study existing taxonomies before suggesting an enhanced classification scheme. Standardizing threat classification establishes better techniques to detect attacks respond to incidents and develop security policies which enable intelligence sharing. Security professionals will receive an organized system for ransomware classifying through these findings which leads to enhanced threat forecasting and cyber defense capabilities against developing threats. Keywords: Taxonomy, Ransomeware attack, vulnerabilities, Detection, Malware.
Systematic Literature Review;Taxonomy of Ransomware Attacks
Market basket analysis for healthcare services to identify bundled care offerings
The complexity of inpatient care, which has recently increased and is accompanied by the growing demand for cost-effective and coordinated treatment models, has enhanced the necessity of the use of evidence-based bundled care services in hospitals. Market Basket Analysis (MBA) is a time-tested data mining method that offers a systematic method of finding naturally occurring co-occurring clinical services, which respectively create care bundles. This study uses the association rule mining on the 2010 New York State SPARCS Hospital Inpatient Discharge data, which is a de-identified, large scale dataset of more than 2.6 million inpatient records including detailed data on diagnosis, procedures, service use, level of severity and payment source. Service-level item sets were generated using Apriori and FP-Growth algorithms, whereby CCS procedure codes, diagnostic groups and treatment occasions within each inpatient stay were clustered together and allowed identification of commonly occurring combinations of services. This study will identify the clinically significant trends in inpatient episodes that may be used in the design of cost-efficient bundled care. The association rules were tested by means of support, confidence and lift measurements in order to identify high-value service pairs and clusters within large inpatient categories like cardiology, orthopedics, obstetrics and general surgery. Findings indicate that there are strong synergies, e.g., cardiovascular imaging is always accompanied by a particular lab panel and surgical procedure, whereas orthopedic surgery visits often involve diagnostic tests and after-surgical care. These lessons point to service patterns that hospitals can use to develop standardized, value-based care bundles that can decrease fragmentation, improve clinical coordination, and help to optimize resources. This study illustrates the possibility of an MBA to change the approach of inpatient service analysis, which has been focused on descriptive reporting to direct clinical pathway design. Utilizing data on pre-2025 hospitals occurring in the real world, the results are used to advance data-driven bundled care models in accordance with the modern healthcare changes. The model can be expanded to multi-year datasets, outpatient services and predictive models of proactive care planning.
Market Basket Analysis, Bundled Care Offerings, Healthcare Data Mining, SPARCS Inpatient Dataset, Association Rule Mining and Hospital Service Utilization
Cognitive Hives: A Distributed Systems Theory for Conflict-Aware Multimodal Intelligence
The very recent developments in large-scale foundation models have shown impressive language, vision, and multimodal reasoning. But current architectures are still fundamentally monolithic and rely on centralized parameter scaling rather than structural distribution. Multimodal complexity heightens the constraints of monolithic systems with respect to their latency, interpretability, internal conflict management and scalability. The paper presents the idea of Cognitive Hives - a theory of distributed systems of conflict-aware multimodal intelligence. A Cognitive Hive is a system of special-purpose expert models that run with common temporal synchronization and defined conflict-arbitration rules. We define the architectural layers, communication semantics, arbitration functions, and temporal cohesion mechanisms required for stable distributed reasoning. The framework comprises a common time-based backbone, message relaying, and graphical conflict detection to enable scalable cooperative intelligence. We also examine infrastructure needs, relative structural features, and implications for the enterprise. Cognitive Hives signify a shift from scale-through-size to scale-through-structure and provide a principled approach to distributed artificial cognition.
Distributed AI, Multimodal Reasoning, Conflict Resolution, Temporal Synchronization, Cognitive Architecture, Hybrid Temporal Tokenization, Distributed Systems Theory, Arbitration Models
Artificial Intelligence for Sustainable and Climate-Resilient Apparel Supply Chains: A Narrative Review and Integrative Framework
This study examines the role of artificial intelligence (AI) in enhancing environmental sustainability and climate resilience in apparel supply chains. Although prior research has extensively explored sustainable supply chain management, circular economy practices, and AI-driven supply chain resilience, these streams remain largely fragmented. To address this gap, this paper adopts a narrative review approach, analyzing 25 peer-reviewed studies published between 2015 and 2021 across databases including Scopus, Web of Science, and Google Scholar. The findings reveal that apparel-specific research is strong in identifying sustainability challenges, such as environmental degradation, labor issues, and governance complexity, while AI and resilience studies provide advanced insights into predictive analytics, optimization, and risk management but are predominantly situated in general supply chain contexts. There is a clear lack of integration among AI capabilities, environmental sustainability, and climate resilience within apparel supply chains. This paper contributes by synthesizing these disconnected streams and proposing an integrated perspective that links AI technologies to supply chain functions, sustainability outcomes, and resilience capabilities. The study highlights that AI can serve as a bridging mechanism by improving forecasting, resource efficiency, and proactive risk management, while also identifying key barriers such as data limitations, lack of transparency, and organizational readiness. The findings have important implications for researchers, managers, and policymakers seeking to develop greener, more resilient apparel supply chains.
Artificial Intelligence (AI); Apparel Supply Chains; Sustainable Supply Chain Management (SSCM); Climate Resilience; Circular Economy; Supply Chain Resilience; Environmental Sustainability
Copyright (c) 2022 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
Artificial Intelligence in Project Management: A Systematic Review of Applications, Challenges, and Future Directions
Artificial intelligence (AI) has emerged as a transformative technology in project management, offering advanced capabilities for data-driven decision-making, predictive analytics, and process automation. This study presents a systematic literature review of AI applications in project management, focusing on peer-reviewed journal articles published between 2015 and 2021. A total of 25 relevant studies were selected following PRISMA guidelines and analyzed using both thematic and bibliometric approaches. The findings reveal that AI is predominantly applied in areas such as project scheduling, cost estimation, risk management, safety monitoring, and project portfolio selection, with a strong concentration in construction project management. Machine learning and deep learning techniques are widely used, enabling enhanced prediction accuracy and real-time monitoring. The thematic analysis highlights key benefits, including improved efficiency, better decision-making, and enhanced safety performance. However, significant challenges remain, particularly related to data quality, model interpretability, organizational readiness, and ethical concerns. The bibliometric analysis further demonstrates that research in this field is structured around four main dimensions: AI techniques, application domains, performance outcomes, and emerging challenges. A temporal shift from traditional optimization methods to advanced data-driven approaches is also observed. This study contributes to the existing literature by providing a comprehensive synthesis of AI applications in project management and identifying critical research gaps. The findings suggest that future research should focus on explainable AI, integration with emerging technologies such as digital twins and IoT, and the development of human–AI collaborative systems to enhance project management practices.
Artificial intelligence, project management, machine learning, systematic review, bibliometric analysis, construction management
Copyright (c) 2022 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0
AI-Driven Digital Twin Framework for Real-Time Optimization of Smart Manufacturing Systems in Industry 5.0
There has been tremendous improvement in terms of automation and connectivity owing to the implementation of smart manufacturing as part of Industry 4.0; however, some issues continue to hamper the process of real-time adaptation, prediction, and decision making based on humans. Industry 5.0 represents the new era where there is the need for manufacturing systems that are not only resilient but sustainable and human-centric. This calls for frameworks combining AI in cyber-physical systems. In this paper, a novel Digital Twin with AI technology is introduced to provide an optimized approach for manufacturing systems following the Industry 5.0 guidelines. This approach consists of an intelligent multi-level architecture including physical systems, IoT-based data acquisition, digital model building, AI-based analytics techniques, and human-in-the-loop decision-making. It allows constant synchronization between the two worlds of physics and cyberspace, leading to proactive decision making and better system performance. A systematic approach is described here including data pre-processing, modeling, use of machine learning, and improvements to the system based on feedback. The analysis using simulation techniques shows the ability of the approach to increase efficiency, reduce downtime, and optimize predictive maintenance through real-time optimization. Additionally, human-AI interaction provides for flexibility in dealing with situations in manufacturing settings. The suggested solution addresses the main challenges that exist in existing Digital Twin applications by emphasizing such key factors as scalability, interoperability, and intelligence. This research provides a thorough conceptual framework that closes the gap between AI-powered analysis and manufacturing systems designed to serve humans. As it can be concluded from the results, Digital Twins enabled by artificial intelligence are expected to play a crucial role in future smart manufacturing.
AI-driven Digital Twin, Smart Manufacturing
Real-Time Optimization, Human-AI Collaboration, Predictive Analytics