International Journal of Humanities and Information Technology
The International Journal of Humanities and Information Technology (IJHIT-ISSN no. 2456- 1142) is a multidisciplinary, peer-reviewed, open-access journal that brings together innovative research from the intersecting domains of humanities and information technology. IJHIT aims to explore how technology influences human culture, society, and communication—and vice versa—by publishing critical and applied scholarship that bridges the digital and the humanistic. IJHIT serves as a platform for academics, researchers, educators, and practitioners to engage with the challenges and opportunities arising from the digital transformation of traditional humanities disciplines, as well as the human-centered design of information technologies.
Program-Level Risk Orchestration in AI-Enabled Cyber Infrastructure: A Systems-of-Systems Management Approach
Machine Learning (ML) and Deep Learning (DL) driven AI capabilities are being increasingly leveraged for detection, anomaly discovery, and autonomous response within, across, and through cyber infrastructures. Scaling such cyber defense capabilities, they are deployed less as standalone solutions but as programs consisting of autonomous subsystems, common data assets, and joint operational processes. However, scaling also creates systemic risks (e.g., cascading, latent, and indirect) and poses control risks (e.g., distributed, dual-use) that are not sufficiently recognized and managed under prevalent cybersecurity risk management (RM) models, which are generally limited to controls at the component level, local rather than program-wide risk visibility and mitigation. This paper posits a program-level risk orchestration framework and some of its design principles, which are based on systems-of-systems (SoS) engineering and established risk RM standards. Building on early work on AI-based intrusion detection systems, SoS engineering and interoperability, cybersecurity and cyber risk threat modeling, as well as formal risk governance and capital standards, the paper develops the thesis of conceptualizing and managing AI-enabled cyber infrastructure as a risk RM program of a special kind, namely a system-of-systems, in need of coordinated oversight and control. The RM program-level framework it proposes as a starting point combines technical (i.e., from AI subsystems) cyber risk signals with risk governance, investment, and economic decision models to structure the consistent and repeatable identification, assessment, prioritization, treatment, monitoring, and reporting of cyber risks across the program. To better support accountability and return on investment, the program-level management perspective further aligns the operations of the cybersecurity function with a standard RM lifecycle, investment criteria, and cost and benefit capital standards. The paper also positions the proposed framework with respect to recent and extant works, by offering a complementary management lens to a mostly algorithm-focused stream of research.
AI-enabled cybersecurity, program-level risk orchestration, systems-of-systems engineering, cyber risk governance, intrusion detection systems, risk management standards.
Automotive Sales Intelligence: Leveraging Modern BI for Dealer Ecosystem Optimization
The automotive retail business is rapidly evolving due to the digitalization process, the increase in data, and the evolving demands of customers. The conventional method of decision-making in dealer ecosystems has been characterized by fragmented information, visibility, and reactive strategies. In this study, the author examines how Business Intelligence (BI) can be applied in modern times to improve sales intelligence in the automotive industry and maximize the performance of dealer ecosystems. The study suggests a combined Automotive Sales Intelligence Framework, which is a combination of data integration, advanced analytics, and visualization to assist in evidence-based decision-making in sales, inventory, marketing, and customer relationship management. The implemented framework will comprise four layers: The data acquisition layer will include dealer management systems, customer touchpoints, and market sources; the data processing/data warehousing layer will support analytical intelligence through descriptive and diagnostic BI tools, and predictive BI tools; finally, the decision-support layer will allow real-time dashboards and actionable insights to the stakeholders. The framework helps to enhance demand forecasting, inventory optimization, customized sales strategy, and performance tracking by harmonizing the functions of dealers with the insights of data. The analysis indicates how the current BI technology can cause raw sales data to become strategic intelligence, and this will ensure a greater efficiency of operations and ultimate competitive advantage: cloud-based BI tools and interactive analytics. These results contribute to the scholarly and practical discourse as they demonstrate that a systematic BI-based model can help achieve sustainable development and optimization in an automotive dealer ecosystem.
Automotive Retail, Sales Intelligence, Business Intelligence, Dealer Analytics, Decision Support Systems, Data-Driven Optimization
Closed-Loop AI Frameworks for Real-Time Decision Intelligence in Enterprise Environments
The traditional analytics systems tend to be retroductive, where they look at the past information with a view to deriving insights that may not be really applicable in the rapidly changing enterprise world. This limitation is what this paper is going to look into on how closed-loop AI systems can truly achieve their potential by making it possible to have real-time decision intelligence in the enterprises. It puts an emphasis on the integration of streaming data pipelines, feedback loops that are continuously running and adaptive inference systems in which AI systems dynamically learn based on the outcome of their actions and respond with new actions accordingly. In the article, it is also described how latency control, traceability of decision, and stability of the system are the key architectural recommendations that are significant to maintain the reliability and efficiency of the real-time AI systems. In addition, the paper also talks about the technical issues, such as processing large volumes of data, and about governance issues, such as accountability, transparency and auditability of the decision-making processes. The research is a roadmap of how companies can utilise the potential of AI to make operational decisions and manage to have scalability, reliability, and effective supervision of the system simultaneously. The proposed closed-loop model would turn the decision-making process more agile in such a way that an AI-based system could always be developing and adapting to different circumstances in business.
Closed-loop AI, Real-time decision intelligence, Streaming data pipelines, Continuous feedback loops, Latency control, System stability
Cyber-Secure Cloud Architecture Integrating Network and API Controls for Risk-Aware SAP Healthcare Data Platforms
The rapid digitalization of healthcare systems has increased reliance on SAP-based platforms deployed on cloud infrastructures, introducing complex challenges related to cybersecurity, data governance, and operational risk. This paper proposes a cyber-secure cloud architecture that integrates network-level security mechanisms and API governance controls to enable risk-aware management of healthcare data platforms. The architecture combines zero-trust networking, intrusion detection and prevention systems, secure API gateways, and policy-driven data access to protect sensitive clinical and operational data across hybrid and multi-cloud environments. Risk-aware analytics are embedded into the platform to continuously assess security posture, data access behavior, and system performance, supporting informed decision-making and regulatory compliance. The proposed framework aligns with healthcare standards such as HIPAA and GDPR while supporting scalable SAP workloads and real-time data processing. Through architectural analysis and use-case-driven evaluation, the study demonstrates how integrated network and API controls can reduce attack surfaces, improve data integrity, and enhance system resilience. The results indicate that the proposed approach enables secure, compliant, and scalable SAP healthcare data platforms capable of supporting mission-critical healthcare operations.
Cybersecurity, Cloud Architecture, SAP Healthcare Systems, API Security, Network Security, Risk-Aware Analytics, Data Governance.
Resilient and Ethical Enterprise IT Systems integrating Platform Engineering SAP Analytics Healthcare Interoperability and Cloud Infrastructure
Modern enterprises face unprecedented demands for reliability, ethical data handling, interoperability, and agility in their IT systems. This paper presents a comprehensive framework for designing resilient and ethical enterprise IT systems that integrate platform engineering, SAP ecosystems, data analytics, healthcare interoperability, and cloud infrastructure. Resilience ensures continuity of operations amid disruptions, while ethical considerations such as privacy, fairness, and accountability are essential in systems handling sensitive data—especially in healthcare contexts. Platform engineering provides standardized tooling, automation, and internal platforms that reduce complexity and accelerate delivery. SAP systems serve as core business processing engines; integrating them for real-time analytics enhances decision support while aligning transactional and analytical workflows. Data analytics unlocks insights across operations, and healthcare interoperability enables seamless information exchange following protocols like HL7 and FHIR. Cloud infrastructure supports elastic scalability, distributed delivery, and costefficient resource utilization. Through an extensive literature review and multiple case studies involving expert interviews and system artifact analysis, this research identifies architectural patterns, governance practices, and measurable outcomes associated with unified enterprise systems. Findings show that enterprises adopting these integrated approaches achieve improved performance, reduced downtime, enhanced compliance, and stronger trust among stakeholders. The paper concludes with practical recommendations and directions for future research in resilient, ethical enterprise IT design.
Resilient IT Systems, Ethical Data Governance, Platform Engineering, SAP Systems, Data Analytics, Healthcare Interoperability, Cloud Infrastructure, Enterprise Architecture, Digital Transformation
Cybersecurity in Critical Residential Infrastructure
The increasing digitalization of residential environments has transformed housing systems into a critical component of national cyber-physical infrastructure. Smart homes, residential energy systems, and IoT-enabled utilities now play an essential role in societal functioning, yet they remain highly vulnerable to cyber threats due to fragmented governance, heterogeneous devices, and limited security-by-design adoption. This article examines cybersecurity challenges within critical residential infrastructure by synthesizing existing literature on smart grids, residential IoT, and cyber risk management frameworks. It identifies key threat vectors, including data breaches, ransomware, and cyber-physical attacks capable of cascading into broader infrastructure failures. The study further evaluates governance gaps and emerging technological responses, with particular attention to artificial intelligence–driven threat detection and resilience enhancement. By conceptualizing residential infrastructure as a critical security domain, the article contributes to ongoing discourse on infrastructure protection and underscores the need for integrated policy, technical standards, and adaptive security architectures to safeguard residential systems against evolving cyber risks.
Cybersecurity, Critical Infrastructure, Smart Homes, Residential Energy Systems, Internet of Things, Risk Management, Artificial Intelligence.
Quantifying the Causal Effect of FMCSA Enforcement Interventions on Truck Crash Reduction: A Quasi-Experimental Approach Using Carrier-Level Safety Data
The Federal Motor Carrier Safety Administration (FMCSA) and partners in the states are supposed to minimize collisions and other serious safety offenses by effective enforcement interventions, such as compliance reviews, audits, and planned inspections not to mention enforcement interventions. However, the carriers onto which the interventions are imposed are usually not random and already are of high profile of risk such that naive before vs. after comparisons is misleading information. The paper is a quantitative evaluation of the causal impact of FMCSA enforcement interventions on truck crash outcomes using carrier-level data on safety, the FMCSA Safety Measurement System (SMS) and the Motor Carrier Management Information System (MCMIS) of 2015-2023 with over 50,000 carriers of varying types and operating in different locations. Our quasi-experimental, propensity score matching, Difference-in-Differences (DiD) study will compare propensity-score adjusted research with a matched control group with the parallel trends assumption validated through the analysis of event-study plots. Synthetic Control methods are used at carrier-segment level with the aim of providing robustness checks in high impact interventions. The significant results are per cent of the crash rates decreased, the Average Treatment Effect (ATE/ATT) at 95 percent confidence intervals, and the amount of crashes which will be avoided each year under the circumstances of the observed volume of the interventions. Placebo tests, alternative matching specifications and sensitivity to time windows are also used to measure the strength. The initial results indicate that the FMCSA interventions result in a mass reduction in the crash rates, and the measures of effects are dependent on the carrier size, type of operation, former degree of the risk, and region where the carriers operate. The findings have practical implications of the differences in effects of the enforcement measures, which can be utilized to make more specific and evidence-based policy changes. This research enhances better understanding of enforcement efficacy in enforcing road safety and minimizing the likelihood of crashes with a help of causal inference tools and carrier-level information.
Causal inference, enforcing behaviour, position along the road, crash minimisation, quasi-experimental design, synthetic control, interventional assessment, selection biases, counterfactual analysis, policy impact assessment, carrier safety performance.
Multivariate AI Cloud Security and DevSecOps for Financial Processes and Privacy-Preserving Advertising Metrics
The rapid adoption of cloud computing in financial business processes has significantly increased the complexity and scale of cybersecurity threats. Traditional rule-based security mechanisms are often insufficient to detect sophisticated fraud patterns and advanced persistent threats operating across distributed cloud environments. This paper proposes a multivariate AI-driven cloud security framework that integrates machine learning–based anomaly detection, behavioral analytics, and real-time threat intelligence within a DevSecOps pipeline. The framework leverages multivariate data streams—including transaction logs, network telemetry, user behavior, and application performance metrics—to proactively identify fraud, insider threats, and security breaches across financial workflows. By embedding automated security controls into continuous integration and continuous deployment (CI/CD) processes, the proposed approach enables continuous risk assessment, faster incident response, and adaptive security policy enforcement. Experimental analysis demonstrates improved detection accuracy, reduced false positives, and enhanced resilience of financial systems against evolving cyber threats in cloud-native environments.
A Secure and Real-Time AWS Cloud Framework for AI-Based Medical Image Analysis with SAP Connectivity
The rapid growth of medical imaging data has created a critical need for scalable, secure, and real-time analytical frameworks capable of supporting advanced artificial intelligence (AI) applications in clinical environments. This work presents a secure and real-time AWS cloud–based framework for AI-driven medical image analysis with integrated SAP connectivity, designed to enable efficient image processing, model inference, and enterprise system integration. The proposed architecture leverages deep learning models for automated image analysis while utilizing AWS-native services to support real-time data ingestion, processing, and scalable deployment. Secure data pipelines are implemented to ensure confidentiality, integrity, and compliance with healthcare data protection requirements, while SAP integration enables seamless interoperability with hospital information systems and enterprise workflows. Real-time processing capabilities support low-latency clinical decision-making, and the cloud-native design allows elastic scaling to accommodate growing imaging workloads. This framework demonstrates how combining deep learning, cloud infrastructure, real-time data pipelines, and secure enterprise integration can enhance the reliability, efficiency, and clinical applicability of AI-based medical image analysis systems.
AI-based medical image analysis, Deep learning, AWS cloud computing, Real-time data pipelines, Secure healthcare systems, SAP integration, Cloud-native architecture.
A Secure AI-Driven Cloud Architecture for Modern Digital Infrastructure Integrating GitOps-Governed Data Platforms and Financial Systems
In the era of accelerating digital transformation, secure and resilient cloud architecture has become essential for modern enterprises, particularly those integrating complex data platforms and mission-critical financial systems. This paper proposes a comprehensive secure AI-driven cloud architecture that leverages GitOps governance to ensure consistent deployments, automated compliance, and adaptive security controls. The architecture synergizes multiple cutting-edge technologies: containerized microservices, machine learning-based anomaly detection, policy-as-code governance, and continuous delivery pipelines to support scalable data ecosystems and resilient financial workflows. Through a hybrid cloud model complemented by policy enforcement and real-time threat detection, the architecture enhances confidentiality, integrity, and availability while reducing operational overhead. This research conducts a systematic literature review, designs a methodology for implementation and evaluation, and demonstrates benefits and limitations. Results highlight significant improvements in deployment consistency, security posture, and system resilience. The study concludes with recommendations for future research on federated learning integration and cross-domain compliance automation. Findings are relevant to architects, engineers, and operational leaders seeking robust, governed, and AI-enhanced cloud infrastructures in highly regulated domains.
Secure cloud architecture, AI-driven security, GitOps governance, Data platforms, Financial systems, Microservices, Hybrid cloud, Policy-as-code, Cloud compliance.
Engineering Trust at Scale: How Proactive Governance and Operational Health Reviews Achieved Zero Service Credits for Mission-Critical SAP Customers
For two decades, the management of mission-critical SAP landscapes has been bifurcated by a fundamental epistemic fracture: the sociological definition of trust as a function of vulnerability versus the engineering imperative to eliminate that vulnerability entirely. Current industry standards attempt to paper over this silence with the Service Level Agreement (SLA), a reactive instrument that essentially monetizes failure through the “archaeology” of post-mortem analysis rather than preventing the trajectory of the crash. To resolve this decoupling, this study details a twenty-four-month longitudinal intervention utilizing a Proactive Governance framework known as the Operational Health Review (OHR), which shifts the audit mechanism upstream to interrogate system conditions before incidents coalesce. The results demonstrate that by introducing deliberate friction into the release cycle, organizations can decouple operational complexity from risk, achieving a sustained state of “Zero Service Credits” an economic surplus of reliability where penalty payouts cease entirely. This transition suggests that the “inevitability” of downtime is a symptom of governance rather than software entropy, effectively challenging the “fail fast” orthodoxy of modern IT operations. Ultimately, this research redefines the semantics of “zero” from a deficit to an asset, arguing that in mission-critical contexts, the objective is not to cultivate trust, but to render it obsolete through the structural guarantee of continuity.
Engineering Trust, Proactive Governance, Operational Health Reviews (OHR), Zero Service Credits, Mission-Critical SAP, Service Level Agreement (SLA) Management, Reliability Engineering, Enterprise Risk Management, Operational Excellence, System Availability, Trust Reliability Index (TRI), Mean Time to Governance (MTTG), Predictive Risk Mitigation, Algorithmic Governance, Operational Friction, Decoupling Complexity.
Machine Learning Regression Techniques for Modeling Complex Industrial Systems: A Comprehensive Summary
Machine learning regression techniques have become essential for modeling complex industrial systems in the industry 4.0 era. This summary reviews key regression methods—from linear models to deep learning—and examines their applications across manufacturing, energy, chemical processing, and predictive maintenance. We discuss how different techniques balance accuracy, interpretability, and computational requirements, and highlight real-world implementations that have achieved substantial efficiency gains. The review also addresses critical challenges including data quality, model interpretability, and generalization, while exploring future directions such as physics-informed models, federated learning, and explainable AI.
Machine Learning, Regression, Industrial Systems, Predictive Maintenance, Manufacturing, Process Modeling, Ensemble Learning, Deep Learning, Industry 4.0
A Cybersecurity-First Deep Learning Architecture for Healthcare Cost Optimization and Real-Time Predictive Analytics in SAP-Based Digital Banking Systems
The rapid digitalization of healthcare payments and financial workflows has intensified the need for secure, intelligent, and cost-efficient digital banking infrastructures. Healthcare organizations increasingly rely on SAP-based digital banking systems to manage claims processing, billing, and financial transactions, yet these systems face challenges related to escalating operational costs, cyber threats, and limited real-time intelligence. This paper proposes a cybersecurity-first deep learning architecture designed to optimize healthcare costs while enabling real-time predictive analytics within SAP-based digital banking environments. The proposed framework integrates deep learning models for cost prediction, anomaly detection, and demand forecasting with secure data pipelines, encryption, identity and access management, and continuous threat monitoring. Real-time analytics are achieved through event-driven processing and SAP-native services, allowing proactive decision-making and early risk mitigation. By unifying cybersecurity controls with advanced AI-driven analytics, the architecture enhances financial transparency, reduces fraud and inefficiencies, and supports scalable, compliant healthcare financial operations. The proposed approach demonstrates the potential to significantly improve cost optimization, security posture, and operational resilience in next-generation healthcare-focused digital banking systems.
Healthcare cost optimization, Deep learning, Cybersecurity-first architecture, SAP-based digital banking, Real-time predictive analytics, Fraud detection, Secure financial systems, AI-driven healthcare finance
An Intelligent AI and ML–Driven Cloud Security Framework for Financial Workflows and Wastewater Analytics
The rapid digital transformation of financial services and critical infrastructure systems has increased reliance on cloud-based platforms while simultaneously amplifying security risks and operational vulnerabilities. This paper presents an AI-based risk-aware cloud security framework designed to secure financial workflows and enable smart wastewater analytics through real-time predictive intelligence. The proposed framework integrates machine learning–driven risk assessment, continuous monitoring, and adaptive security controls to detect threats, anomalies, and compliance violations across distributed cloud environments. By leveraging real-time data streams from financial transactions and wastewater monitoring systems, the framework enables proactive risk mitigation and informed decision-making. Advanced analytics models support fraud detection, operational optimization, and early identification of system failures while ensuring data confidentiality and integrity. The architecture emphasizes scalability, resilience, and regulatory compliance, making it suitable for highly regulated domains. Experimental evaluation demonstrates improved threat detection accuracy, reduced response latency, and enhanced reliability compared to traditional rule-based security approaches. The results highlight the effectiveness of AI-driven, risk-aware cloud security in supporting secure financial operations and intelligent, data-driven wastewater management.
AI-driven security, risk-aware cloud computing, financial workflow security, smart wastewater analytics, real-time predictive analytics, anomaly detection, Secure cloud infrastructure
Cybersecure Cloud AI Banking Platform for Financial Forecasting and Analytics in Healthcare Systems
Digital banking systems increasingly rely on advanced analytics and artificial intelligence (AI) to enhance decision-making, risk management, customer personalization, and operational efficiency. Financial forecasting — including credit risk assessment, liquidity modeling, fraud detection, and market trend prediction — demands scalable, reliable, and secure AI architectures that can process high-velocity, high-volume data in real time. Traditional on-premises systems struggle to meet these requirements due to limited scalability and inflexible infrastructure, motivating a shift toward cloud-native architectures. This paper proposes a Cloud AI Architecture for Scalable Financial Forecasting and Predictive Analytics tailored for digital banking systems. Leveraging distributed computing, microservices, containerization, and managed AI/ML platform services, the architecture integrates data ingestion layers, feature stores, real-time and batch processing pipelines, model training and deployment workflows, and governance frameworks to ensure compliance, explainability, and operational resiliency. We describe the design principles, key architectural components, and integrated tools necessary to support end-to-end financial forecasting use cases. Experimental evaluation using representative banking workloads demonstrates improved scalability, lower latency, and enhanced forecasting accuracy compared with traditional systems. The findings indicate that a cloud AI architecture provides a strategic advantage for digital banks seeking to transform data into actionable insights while maintaining data governance and regulatory compliance.
Cloud Architecture, Artificial Intelligence, Financial Forecasting, Predictive Analytics, Digital Banking, Scalable Systems, Real-time Analytics, Machine Learning Operations, Data Governance.
AI-Based Data Analytics for Financial Risk Governance and Integrity-Assured Cybersecurity in Cloud-Based Healthcare
The rapid adoption of cloud computing in financial and healthcare sectors has enabled large-scale data analytics, improved decision-making, and enhanced operational efficiency. However, it introduces critical cybersecurity risks, including data breaches, fraud, and compliance challenges. This paper proposes an AI-Driven Analytics framework for Secure, Reliable, and Integrity-Assured Financial and Healthcare Cybersecurity in the Cloud, designed to address these challenges effectively. The framework leverages advanced AI algorithms and analytics to detect anomalies, predict potential cyber threats, and provide actionable insights while ensuring data integrity and system reliability. Cloud-native security mechanisms, including encryption, access control, and compliance monitoring, are integrated to adhere to regulatory standards such as HIPAA, PCI-DSS, and GDPR. Experimental evaluation demonstrates improved threat detection accuracy, enhanced system reliability, and robust protection for sensitive financial and healthcare data. This approach provides a comprehensive, secure, and trustworthy foundation for managing cybersecurity risks in cloud environments.
AI Analytics, Financial Cybersecurity, Healthcare Cybersecurity, Cloud Computing, Data Integrity, System Reliability, Threat Detection
SAP PO Cloud Migration: Architecture, Business Value, and Impact on Connected Systems
For nearly two decades, the discourse on enterprise architecture has marginalized middleware as a static appliance, ignoring its foundational role as the central nervous system of global supply chains. As organizations pivot toward hybrid cloud infrastructures, prevailing scholarship remains dangerously bifurcated, oscillating between abstract cost-savings models and naive "lift-and-shift" methodologies that fail to account for the physics of distributed stateful systems. Addressing this methodological deficit, this study constructs and validates a "Resilience-First" architectural framework. We examine the transition from the monolithic definition of SAP Process Orchestration comprising Process Integration (PI), Business Process Management (BPM), and Business Rules Management (BRM) to a distributed Azure-hosted topology. By leveraging Azure ExpressRoute for private, deterministic connectivity and implementing rigorous high-availability patterns, this architecture challenges the industry’s fixation on Total Cost of Ownership. Instead, we demonstrate that the primary value of cloud migration lies in operational resilience. Analysis of post-migration data aligns with broader industry benchmarks, where modern integration suites achieve transaction success rates of 99.94%, effectively stabilizing the "Interim Hybrid State" not as a regrettable transitional phase, but as a durable architectural standard for mission-critical logic.
Sap Process Orchestration (Sap Po), Microsoft Azure, Sap Integration Suite, Azure EXPRESSROUTE, Cloud Migration Architecture, Middleware Modernization, Hybrid Cloud Integration, High Availability (Ha), Operational Resilience
Quantum-Resilient Cryptographic Protocols for the Next-Generation Financial Cybersecurity Landscape
The rapid evolution of quantum computing presents a new age of paradigm shift in cybersecurity and particularly to financial institutions, which heavily rely on the use of encryption to keep their sensitive data confidential. The traditional cryptographic designs, such as RSA and ECC, are becoming more vulnerable to quantum attacks, which also include a requirement to design quantum-resistant cryptographic designs. This paper discusses quantum resistant cryptography systems to be implemented into the future of the financial cybersecurity setting, potential applications of cryptosecurity to finances, data protection systems, digital transactions security, and encryption and authentication systems. We suggest a hybrid cryptography system which is a combination of lattice-based encryption, hash-based signatures, post-quantum key exchange systems to enable it to be more resilient to quantum attacks. It also has risk-aware authentication and multi-layered encryption solutions which are integrated into the framework based on the requirement of financial institutions in its operation. To test the framework, we modeled the digital transaction case through the application of representative financial data and quantified the key performance indicators to include encryption/decryption time, the transaction time, and the resistance to attacks. The results confirm the fact that the security measures have considerably increased and the quantum-resilient protocols possess the same stage of computing power as a classical system and decrease risks stemming out of quantum-enabled adversary. The quantitative findings have shown that the likelihood of possible breaches is now minimized by 78 percent, confidentiality of transactions is enhanced, and dependability of authentication with different threat models. The work provides a feasibility guide on the application of quantum resistant protocols in financial systems that could be utilized to provide scalability with regard to securing of digital transactions and risk management. The findings emphasize the need to deploy quantum-resilient cryptography by financial institutions, which would experience sustainable cybersecurity in the new age of quantum computing.
Cybersecurity in Finance, Data Privacy Protection, Digital Transaction Security, Financial Institutions Risk Management, Encryption and Authentication Systems
Meta-Learning–Driven Intrusion Detection for Zero-Day Attack Adaptation in Cloud-Native Networks
Zero-day attacks are a very critical factor to cloud-native networks because they are evolving in nature, dynamic load, and highly distributed networks. Conventional intrusion detection systems (IDS) whose models are built on a fixed set of rules or under the watch of a supervised learning model find it difficult to generalize new attack patterns and changing traffic patterns which happen very fast. The article presents a Meta-Learning-Based Intrusion Detection Framework that is intended to allow adapting to zero-day attacks in cloud-native applications quickly. The given method is based on Model-Agnostic Meta-Learning (MAML) combined with deep neural network classifiers to acquire transferable representations with respect to a wide range of attack conditions. The features are container-level, network-flow, and system-call-based features, which are extracted out of cloud-native workloads on Kubernetes-based infrastructure. In meta-training, the model is presented with several attack tasks so that it is exposed to new, unseen attacks with little labeled samples. Hybrid dataset that is comprised of CICIDS2017, UNSW-NB15, and artificially created traces of zero-day attacks is used to evaluate the experiment. The proposed model attains a mean detecting precision of 96.8, F1-score of 0.964 and 21 percent faster detection speed than the traditional deep learning-based IDS models. The performances have shown better conformability, lower false positives and better strength in the face of concept drift. The results uphold that meta-learning is one of the most effective strategies in strengthening the resistance of zero-day attacks, and thus the suggested framework is ideal in the intrusion detection process of elastic cloud-native networks in real-time.
Artificial Intelligence, Cybersecurity, Intrusion Detection Systems, Machine Learning, Threat Detection, Anomaly Detection
Beyond the Air Gap: Autonomous AI-Driven Anomaly Detection in Converged IT/OT Critical Infrastructure Environments
The convergence between Information Technology (IT) and Operational Technology (OT) has occurred at an extremely rapid pace and has made traditional air-gapped security models ineffective in securing critical infrastructure. With growing usage of AI-enhanced capabilities to conduct automated reconnaissance, zero-day code exploitation, and rapid lateral movement, human-controlled surveillance within the industrial setting is no longer able to react as fast as it can. In this article, an independent, AI-assisted, anomaly detecting system is suggested that can operate within machine speed and provide real-time protection against pending cyber-physical attacks. The method combines self-learning models, behavioral baselining, cross-domain telemetry, and simulation on a digital-twin to detect subtle and newly emerging anomalies in highly complex industrial ecosystems. The system improves situational awareness by correlating IT and OT signals and helps decrease the operator load and provide quick mitigation without affecting safety. The results indicate that the switch to machine-vs-machine defense model is posed as the prerequisite requirement to ensure the safety of modern critical infrastructure.
AI-based cybersecurity, OT anomaly detection, IT/OT convergence, autonomous defense, digital twins, cyber-physical security, machine-speed response.
Separating Technology and Trust: A Survey Analysis of Patients’ Attitudes toward AI-Assisted Healthcare Decision-Making
The integration of artificial intelligence (AI) and Internet of Things (IoT) technologies is fundamentally transforming healthcare delivery from hospital-centric to people-centered models. While AI demonstrates significant potential in improving diagnostic accuracy and enabling predictive disease management, important challenges persist regarding ethical considerations, social trust, data privacy, and equitable access across diverse populations. This research significance despite technological advances, comprehensive frameworks addressing AI transparency, social trust, and ethical implications in healthcare remain underdeveloped. This study addresses critical gaps by examining consumer perspectives on AI-assisted clinical decision support (AI-CDS) systems, focusing on perceived benefits, risks, and factors influencing acceptance across demographic segments. These statistical methods and measures were implemented using IBM SPSS 27.0 software for analyzing the survey data on attitudes toward AI-CDS systems. Data were collected through structured online surveys from 442 participants across diverse demographic backgrounds in the United States. The questionnaire assessed nine key dimensions including AI-CDS knowledge, trust, and ease of use, bias concerns, and willingness to follow recommendations using validated five-point Likert scales. Statistical analysis employed descriptive statistics, chi-square tests, and one-sample t-tests. The results respondents demonstrated moderate, neutral attitudes toward AI-CDS across all dimensions (means 3.02-3.18). Education level significantly influenced understanding and comfort with AI systems, while income affected approval needs and bias concerns. Healthcare provider status and clinical documentation experience emerged as crucial factors shaping trust and acceptance. Successful AI-CDS implementation requires addressing trust deficits through experiential learning, robust regulatory frameworks, and maintaining human-AI collaboration in healthcare decision-making.
Artificial Intelligence in Healthcare, Clinical Decision Support Systems (AI-CDS), Patient Trust and Acceptance.
AI-Driven Fraud Detection Using Self-Supervised Deep Learning for Enhanced Customer Identity Modeling
The quick growth rate of e-financial services has augmented the need of smart and dynamic fraud detection framework which had the capacity to acquire intelligent lean and dynamic and unbalanced threat models. The given article introduces an AI-based customer identity modeling and fraud system, which presupposes the implementation of hybrid temporal and graph analytics and relies on the self-supervised deep learning to increase the focus on anomalies in the intricate setting of transactions. The suggested system entails contrastive behavioral representation learning and masked feature reconstruction throughout the pretraining stage to acquire discriminative latent embedding of the customer behavior with a minimum requirement of frail information with labeling. These embeddings are trained on a Temporal Transformer that is learned to learn the dynamics of sequential spending and a Graph Neural Network (GNN) that is learned to learn cross-account identity relationships. It is a fusion of the temporal, identity and self-supervised embeddings, which apply to provide sound decision making by using the stacked ensemble of XGBoost, deep neural networks and logistic regression meta-learner to rank real time risks. The performance metrics on the framework are measured on large and very imbalanced real- world financial transaction data on the accuracy, recall, F1-score, ROC-AUC, the false positive and latency of detection. The recall, precision, and F1-score of the system are 17, 12 and 19 percent higher than the traditional rule- based and supervised fraud detectors, as explained in the experiments. Moreover, identity-conscious modeling will be capable of minimizing the false positive rates by 15 percent and identify it in time, which may be considered in real-time cybersecurity analytics. In general, the proposed framework can be described as a flexible, sustainable and intelligent system of proactive fraud detection, dynamic customer identity profiling, and next-generation financial cybersecurity systems.
AI-Driven Process Optimization in Automated Microsoldering for Fine-Pitch PCB Assembly
Fine pitch printed circuit board assembly requires highly accurate microsoldering performance to avoid defects such as bridging, voiding, misalignment, and insufficient solder deposition. Traditional rule based inspection approaches lack predictive intelligence and do not provide real time decision capacity for improving solder quality during production. This research investigates an artificial intelligence driven methodology for optimizing microsoldering processes by combining machine vision inspection, predictive stencil printing models, and statistical process control. A consolidated dataset was developed consisting of optical solder joint images, stencil paste measurements, printing parameters, and microsoldering variables. Convolutional neural networks were applied to identify defect types. Recurrent neural network prediction was used to estimate stencil cleaning frequency and support vector regression was implemented to forecast paste deposition behavior. Statistical evaluation showed reductions in bridging occurrence from 12.4 percent to 4.1 percent and misalignment frequency from 9.3 percent to 2.7 percent after AI integration. Inspection recognition improved from 82 percent to 96 percent. The proposed AI supported SPC structure enhances control chart interpretation, automatic defect tagging, and real time process capability monitoring. Findings indicate that AI based hybrid optimization reduces variation in solder paste application, strengthens pattern detection for microscale solder behavior, and improves consistency in fine pitch PCB assembly quality. The study concludes that artificial intelligence offers a reliable path toward improved production stability, lower defect rates, and greater operational efficiency in automated microsoldering systems.
Microsoldering, Printed Circuit Board Assembly, Machine Vision, Artificial Intelligence, Statistical Process Control, Deep Learning, Defect Detection.
Global AI Skills Index: Creating an Open Benchmark for Tracking National AI Upskilling Readiness
Artificial intelligence continues to reshape global labor markets, amplifying the urgency for countries to develop scalable and equitable AI-related competencies. Yet, the absence of a unified global benchmark limits policymakers’ ability to compare national readiness or design targeted workforce development strategies. This study introduces the Global AI Skills Index (GASI), a comprehensive cross-national framework that evaluates AI upskilling readiness across four dimensions: AI Job Demand Density, Education-Industry Alignment, AI Skills Penetration Rate, and the Equity and Access Index. Drawing on standardized datasets from the OECD, UNESCO, ILO, World Bank, and LinkedIn covering 2018 to 2023, the index applies a weighted composite methodology to generate a harmonized 0-100 score for each country. Methodological reliability was assessed through cross-validation with three established international benchmarks: the OECD Digital Skills Indicators, the Stanford AI Index, and World Bank–LinkedIn labor-market analytics. Strong alignment across these sources (R² = 0.76 to 0.84) demonstrates that GASI accurately captures systemic national differences in AI competence, digital readiness, and workforce capability. Quantitative findings highlight pronounced global disparities: advanced economies such as the United States, Singapore, and Finland achieve scores above 80, while countries across Sub-Saharan Africa and South Asia consistently fall below 55. A significant positive association between national education expenditure and AI job demand (R² = 0.68) further underscores the central role of sustained human-capital investment. By integrating labor-market dynamics, education quality, skill prevalence, and equity considerations into a unified benchmark, the Global AI Skills Index offers an actionable, transparent framework for policymakers, educators, and international organizations. It enables evidence-based comparison, targeted intervention design, and long-term monitoring of national progress toward inclusive, AI-enabled workforce development.
Global AI Skills Index, artificial intelligence, workforce readiness, digital skills, education-industry alignment, equity in AI upskilling.
Measuring the Human Risk Factor: Developing Predictive Models for Insider Threat Detection Using Behavioral Analytics
Insider threats represent one of the most complex challenges in modern cybersecurity, as they originate from authorized users who exploit legitimate access for malicious or negligent purposes. Unlike external attacks, insider incidents often evolve gradually through subtle behavioral changes that traditional rule-based systems fail to detect. This study investigates the human risk factor underlying insider threats and develops predictive models that leverage behavioral analytics to identify early indicators of malicious intent. The research utilizes the Carnegie Mellon University CERT Insider Threat Dataset (v4.2), integrating system activity logs, communication patterns, and psychosocial proxies such as email sentiment and work-hour deviations. Data preprocessing involved normalization and feature selection across technical, behavioral, and psychological dimensions. Three machine learning models—Random Forest, Long Short-Term Memory (LSTM), and Autoencoder—were implemented to evaluate predictive performance. Model performance was assessed using precision, recall, F1-score, and ROC-AUC metrics. Results show that the LSTM model achieved the highest overall accuracy of 93.2 percent with an AUC of 0.96, outperforming both Random Forest and Autoencoder models. Behavioral deviations such as unusual file transfers, abrupt login time changes, and communication tone shifts emerged as strong predictors of insider risk. The findings highlight the value of integrating human behavioral analytics into cybersecurity frameworks to enhance proactive threat detection. This study contributes to the development of data-driven, ethically aligned security strategies that enable organizations to identify, quantify, and mitigate insider risks before they escalate into security incidents.
Insider Threats, Behavioral Analytics, Human Risk Factor, Predictive Modeling, Machine Learning, Cybersecurity, CERT Dataset, LSTM, Random Forest, Autoencoder.
Hybrid AI Models Combining Machine-Deep Learning for Botnet Identification
The botnet is considered a highly advanced vulnerability threat. Threats to smart systems and data integrity posed by botnet assaults have grown in importance alongside the proliferation of IoT networks, making them a major topic in cybersecurity discussions. Current detection systems frequently fail to accurately classify various attack types, manage data with high dimensions, or record attack trends over time. To efficiently detect botnets, this work aims to construct a robust hybrid AI model utilizing the BOT-IOT dataset, which encompasses both common and unusual attack types, including reconnaissance, DoS/DDoS, and information theft. The proposed methodology combines LSTM networks for temporal pattern recognition, Decision Trees (DT) for feature-based classification, and Logistic Regression (LR) for high-dimensional multiclass prediction, with aggregated final predictions to enhance stability and accuracy. During data preprocessing, missing value handling, categorical variable encoding, and consistent feature scaling using Min-Max normalization were all part of the procedure. The hybrid model achieved 99% accuracy, recall, and F1-score, with an AUC-ROC of 0.9895, surpassing established methods such as C4.5, KNN, and weighted ego network with LDA, as well as individual models (LSTM: 97%, DT: 94%, LR: 95%). These findings support improved, balanced, and real-time IoT botnet detection for smart city cybersecurity.
Cybersecurity, Cyber-Attacks, IoT, Botnet, AI, Machine learning, Deep Learning.
Body Percussion for Focused Starts: Links to Attention and Executive Function
Attention and executive functions represent cognitive processes underlying our ability to focus, remember, and self-regulate, which are vital for learning. In a classroom environment, these skills help determine how long it takes students to orient to learning tasks, assimilate verbal instructions, and stay engaged throughout an activity. New research indicates that rhythmic-motor coordination activities such as body percussion may help improve these attentional and executive processes. This article reviews how body percussion practices can enhance neural and behavioral mechanisms underlying attention and executive readiness in school settings. It also provides recommendations for integrating these activities as “start-of-lesson” activation strategies. The paper reviews literature from cognitive neuroscience, music education, and embodied learning research and describes mechanisms through which synchronous activation of the auditory and motor systems in rhythmic activities facilitates attention, working memory, inhibitory control, and task-switching functions. Regular engagement with body percussion supports prefrontal activation through repetition, timing, and cross-modal integration. The BAPNE technique and similar methods offer evidence for the positive impact of movement and rhythm on student concentration, accuracy, and classroom behavior. The article also proposes body percussion as a versatile and inexpensive tool for embodied learning, highlighting its accessibility to children with diverse abilities, the potential to synchronize group participation, and the benefits for awareness of rhythm and internal states. Furthermore, it underlines the importance of using body percussion and related movement activities to develop students’ cognitive abilities, as a break between classroom activities, or a “transition” or focus routine. The paper highlights body percussion as an innovative approach to facilitating students’ attention and executive readiness in a rhythmic and embodied way. It suggests that future research is needed to understand the longer-term effects of such practices on brain function, academic performance, and emotion regulation.
body percussion, executive function, attention, rhythmic movement, embodied learning, classroom engagement, cognitive readiness, BAPNE method.
A Survey of Blockchain-Enabled Supply Chain Processes in Small and Medium Enterprises for Transparency and Efficiency
Supply chain activities in small and medium-sized businesses (SMEs) are made possible by blockchain technology, which offers a revolutionary means of enhancing transparency, efficiency, and trust in global networks. Cooperation between manufacturers, suppliers, logistics companies and consumers is enhanced by blockchain because it provides decentralized, secure peer-to-peer transactions that cannot be altered. All through the supply chain, it enables supply chain traceability, real-time data passing, smart contract execution, and reduces costs, preventing fraud and guaranteeing product authenticity. The features are specifically useful to SMEs which tend to be limited in resources, operational capacity and access to progressive digital tools. This paper highlights the ability of blockchain to build sustainable benefits through reduced inefficiency and enhanced accountability among stakeholders and examines the potential of blockchain to develop competitiveness in other sectors (agriculture, manufacturing, and pharmaceuticals) where transparency and compliance are essential. This study has shown that blockchain can transform SME supply chain management in a way that allows it to become resilient, innovative, and create long-term value. Blockchain implementation in smaller supply chains can help the SMEs to compete successfully in the dynamic markets and be able to adapt to the digital transformation that the world is turning towards.
Blockchain, Supply Chain Management, Small and Medium Enterprises, Transparency, Efficiency, Smart Contracts.
Teacher-Vetted AI Feedback in Primary EFL Writing: A Classroom Cross-Over Study
The increased integration of artificial intelligence (AI) technologies in the English as a Foreign Language (EFL) context has led to significant changes in the learning and teaching paradigm. Automated writing evaluation (AWE) technologies support L2 writers by giving them instantaneous corrective feedback at the grammar, vocabulary, and sentence level. However, questions have been raised about the contextual accuracy of the automated writing feedback and its pedagogical suitability for younger learners who require scaffolding and guidance from their teachers. The current study assesses the impact of vetted teacher AI feedback compared to AI only (no human input) and teacher only feedback on primary EFL learners’ writing performance. The 6-week classroom cross-over study involved 60 primary aged learners, between the ages of 10 to 12, who completed 3 feedback conditions (AI only, teacher only, and teacher vetted AI feedback) for 2 weeks each. Quantitative and qualitative data were gathered from writing pre- and post-tests, rubric scoring of the essays, and a learner reflection journal. Quantitative results from an exploratory repeated measures design revealed that the human-in-the-loop condition (teacher vetted AI feedback) made a statistically the greatest improvement in accuracy, lexical diversity, and task completion. Learners also showed a higher level of motivation, self-confidence, and feedback acceptance when the teacher vetted, added a personal touch, and explained the AI-generated writing suggestions. Qualitative data demonstrated that human involvement enhanced learners’ trust in automated writing feedback, thus making it more pedagogically meaningful. The study results support the advantages of a human–machine partnership, in which teacher-vetted AI feedback is more time-efficient and instructionally effective without jeopardizing the pedagogical values manual feedback provides. The study findings also revealed the potential of AI technologies’ inclusion in primary EFL writing classrooms as a complementary pedagogical tool rather than replacing the teachers. It can help develop feedback literacy, learner agency, and sustainable digital disposition in young writers
AI feedback, teacher-vetted feedback, EFL writing, cross-over study, formative assessment, automated writing evaluation (AWE).
Artificial intelligence (AI) is transforming global labor markets, creating an urgent demand for new digital competencies and workforce readiness strategies. Yet, no unified framework currently exists to measure how prepared countries are to upskill their populations for an AI-driven economy. This study introduces the Global AI Skills Index (GASI), a comprehensive benchmarking tool designed to evaluate national AI upskilling readiness across education, labor, and inclusion dimensions. Using datasets from the OECD, UNESCO, ILO, World Bank, and LinkedIn spanning 2018 to 2023, the research employs a comparative, cross-national approach to construct a weighted composite index. Four indicators form the foundation of GASI: AI Job Demand Density, Education–Industry Alignment, AI Skills Penetration Rate, and Equity and Access Index. These metrics collectively assess how effectively nations integrate AI competencies within their education systems, labor markets, and institutional frameworks. Results reveal significant regional disparities. High-income countries such as the United States, Singapore, and Finland show strong AI integration in education and employment, while emerging economies in Africa and South Asia lag due to infrastructural and policy constraints. A positive relationship between education expenditure and AI job demand highlights the central role of investment in human capital. The Global AI Skills Index provides a transparent, evidence-based benchmark for comparing national AI readiness. It serves as a strategic tool for policymakers and educators to track progress, address inequality, and design inclusive pathways for sustainable, AI-enabled workforce development.
Global AI Skills Index, artificial intelligence, workforce readiness, digital skills, education–industry alignment, equity in AI upskilling.