Journal of Computer Science and Technology Studies
The Journal of Computer Science and Technology Studies (ISSN: 2709-104X) is a double-blind peer-reviewed, open-access journal published by Al-Kindi Center for Research and Development. It covers the latest developments in the broad areas of computer science and information technology. The journal offers readers free access to all new research issues relevant to computer science and information technology. While the journal strives to maintain high academic standards and an international reputation through the suggestions of the international advisory board, it welcomes original, theoretical and practical submissions from all over the world.
Al-Kindi Center for Research and Development
A Review of Sensor Data Acquisition Methods for Accurate and Timely Detection of Faults in Mechanical Systems
The fault detection of mechanical systems is essential to the reliability of such systems in the industrial environment and it is enabled by the efficient method of sensor data acquisition. This review research studies the importance of different sensors and acquisition techniques to detect defects of mechanical parts with reference to current developments and applications. The paper identifies some of the important sensor types including temperature, motion, proximity, and chemical sensors and discusses the application of these sensors in real-time monitoring and diagnostics. More emphasis is put on the contemporary techniques of data acquisition such as synchronization, signal preprocessing, and smart systems, which are able to improve the accuracy of decision-making. It also examines how machine learning and deep learning models can be integrated into fault detection models that enhance efficiency and minimize the need to rely on manual inspection in diagnostic procedures. The review also provides the comparative results of recent open-access research to assess the strengths, issues, and opportunities of the future. The significance of the accuracy and timeliness is described with references to the impact of the late or wrongful detection on the productivity and safety of industry. In general, the review can be considered as a reference point for researchers and engineers who are interested in creating or enhancing sensor-based fault detection systems in mechanical systems.
Sensor Data Acquisition, Fault Detection, Mechanical Systems, Real-Time Monitoring, Machine Learning, Industrial Diagnostics, Accuracy, Timeliness
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Forecasting Household Economic Hardship, Financial Fragility, and Vulnerability Through Artificial Intelligence and Predictive Analytics
Household economic hardship in the United States is increasingly shaped by interacting pressures rather than a single income shortfall. Inflation, rent burdens, revolving debt, medical costs, labor-market volatility, and fraud losses combine to produce financial fragility that conventional scorecards often miss. This paper develops a publication-ready research framework for forecasting household economic hardship, financial fragility, and vulnerability through artificial intelligence and predictive analytics using public data through 2024. The framework integrates survey microdata and contextual indicators from the Federal Reserve’s Survey of Household Economics and Decisionmaking, the Consumer Financial Protection Bureau’s Making Ends Meet surveys and Financial Well-Being Scale, the U.S. Census Bureau’s Household Pulse Survey, USDA food security reports, the Federal Reserve’s Survey of Consumer Finances, Bureau of Labor Statistics expenditure and inflation statistics, and Federal Trade Commission fraud-loss data. Building on the supplied literature and broader scholarship in household finance, explainable machine learning, and welfare measurement, the paper proposes a multimodal architecture that combines gradient boosting, temporal models, and graph-informed neighborhood context to predict three linked outcomes: immediate hardship, near-term fragility, and medium-horizon vulnerability. Data trends through 2024 show that the share of adults doing at least okay financially declined after 2021, emergency liquidity weakened, food insecurity rose, and reported fraud losses climbed sharply. The paper argues that explainable, fairness-audited models can improve targeting, consumer protection, and preventive intervention without reducing households to opaque risk labels. It concludes that AI can strengthen public-interest forecasting when embedded in transparent validation, governance, and human-centered implementation.
household economic hardship; financial fragility; vulnerability forecasting; machine learning; predictive analytics; SHED; CFPB; Household Pulse Survey; food insecurity; explainable AI
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A Survey of Blockchain-Powered Digital Payment Systems and Their Influence on Global Financial Inclusion
The blockchain technology has adhered to becoming a radical innovation in the area of digital finance, in which it has delivered secure, decentralized and transparent methods of transacting business. This paper examines the application of digital payment systems based on blockchain and how it enhances financial inclusion in the world. It describes the fundamental concepts of blockchain, such as transaction formats, block architecture, and digital signatures, as well as how cryptocurrencies can support peer-to-peer digital payments. Payments and settlements, smart contracts, asset tokenization, trade financing, and other notable applications of blockchain technology in the financial sector are included in the study. Furthermore, the paper examines the concepts of financial inclusion and the applicability of digital payments to the magnitude of supplying underserved groups with formal financial services. It is assessed that digital finance that utilizes blockchain technology might offer a viable alternative.to several prominent financial inclusion challenges, including high transaction costs, a lack of digital identity, and mistrust of financial institutions. Lastly, the study concludes blockchain technology's impact on financial inclusion globally by discussing how it improves financial system accessibility, efficiency, and transparency while also addressing new challenges in infrastructure, cybersecurity, and regulation.
Blockchain Technology, Digital Payment Systems, Financial Inclusion, Cryptocurrencies, Decentralized Finance (DeFi), and Digital Finance.
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Real-Time and Near-Real-Time Analytics in Healthcare Data Ecosystems
The fact that connected medical gadgets, wearable sensors and digital health platforms are rapidly expansive has added to pressure on the requirement of real-time and near-real-time analytics in healthcare data ecosystems. With newer changes in the healthcare systems, where the aspect of retrospective reporting is being replaced with the concept of continuous monitoring and acting upon the available data in real-time, the capacity to process, analyse and respond to streaming data in a minimal balance of latencies has gained significant importance. Nevertheless, poor system architecture, lack of interoperability and true data governance and scale are still in the way of the smooth assimilation of real-time analytics into clinical and organizational functions. This paper analyzes the architectural principles, interoperability solutions and governance needs that makes real-time and near real-time healthcare analytics a reality. Based on the latest publications in the field of stream processing, IoT-supported healthcare, edge-cloud processing, AI-based analytics, and interoperability standards like FHIR, the paper carries synthesis of both technical and ecosystem views to create a cohesive framework of real-time healthcare data ecosystem. The analysis proposed four mutually supporting layers in data generation by means of multimodal sensaging infrastructure, stream processing or event-based analytics infrastructure, adaptive Artificial Intelligence (AI)-provided intelligence to aid clinical decision-making, and governance infrastructures to achieve privacy, compliance with regulations, and system reliability. The paper also identifies trade-offs that are very crucial between latency, accuracy, scalability and security, especially in deployed distributed environment as also in edge-enabled environment. Through the alignment of technical architectures and ecosystem governance issues, the present paper presents a comprehensive model of how to operationalize real-time healthcare analytics. The findings provide insights on how to guide system developers, healthcare providers and policymakers to be able to develop resilient play low-latency and ethically controlled healthcare data infrastructures.
Real-time analytics; Near-real-time processing; Healthcare data ecosystems; Stream processing architectures; Edge–cloud computing; Internet of Things (IoT); Clinical decision support systems; FHIR interoperability; Healthcare data governance; Privacy-preserving analytics
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Explainable AI for Institutional Fraud Decisions: A Cross-Sector Empirical Study Using Public Healthcare and Financial Transaction Data
Institutional fraud detection operates at the intersection of predictive analytics, regulatory accountability, and human judgment. While machine learning models can identify fraudulent behavior, their organizational value depends on their ability to support institutional decision-making through interpretable and stable explanations. In regulated environments, analytical outputs must be justified and integrated into investigative workflows. This study empirically examines explainable machine learning as a decision support mechanism across two institutional domains: public healthcare payment systems and financial transaction systems. Using the CMS-derived Healthcare Provider Fraud Detection dataset and the UCI Credit Card Fraud dataset, logistic regression, random forest, and gradient boosting models were developed and evaluated under realistic class imbalance conditions. Explainability was assessed using SHAP-based feature attribution to examine explanation stability and institutional interpretability. Results show that healthcare fraud models produce stable and institutionally meaningful explanations aligned with billing behavior, while financial fraud models generate accurate predictions but less stable explanations. These findings indicate that explainability is shaped by institutional data structure rather than model architecture alone. The study contributes to business analytics research by demonstrating how explanation stability influences decision relevance in institutional fraud detection.
Business Analytics; Fraud Detection; Explainable Artificial Intelligence; Decision Support Systems; Healthcare Analytics; Financial Fraud; SHAP; Institutional Risk Management
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Evidence-Gated Search: Controlling Operational Search Explosion in LLM-Driven Incident Response
Large language model (LLM) assistants are increasingly used during outages and failed deployments, yet their remediation behavior can degrade into long lists of loosely justified changes such as restarts, redeploys, rollbacks, scaling changes, and configuration edits. In practice, this ap- pears as operational search explosion (OSE), where the branching factor of candidate fixes grows faster than teams can validate them, increasing change risk, time-to-recovery, and operator distrust. This paperformulates incident response as a bounded search problem over a constrained library of remediation primitives, where each primitive carries explicit preconditions, risk and cost meta- data, and evidence requirements that must be satisfied beforeexecution. The proposed mechanism, Evidence-Gated Search (EGS), blocks any state-changing action unless the required evidence is present in a normalized incident state. Missing evidence forces bounded read-only evidence gath- ering, such as logs, metrics, traces, deployment diffs, Terraform plan outputs, DNS checks, and access-control verification, before another action is considered. Across 175 incident episodes, EGS reduces executed remediation actions by 13.47% and lowers the action explosion rate (AER) by 16.65%, while maintaining a recovery success rate of 98.86%. The results show that requiring ev- idence before irreversible operational steps can significantly reduce operational search complexity without materially degrading recovery outcomes.
Incident response automation, large language models, operational search explosion, evidence- gated search, safe remediation, budgeted search control, reliability engineering, DevOps
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AI-Driven Hyper-Personalization, Consumer Sovereignty Loss, and the Transition Toward Subscription Economy: An Information Foraging Autonomy Perspective: An Information Foraging Perspective
Artificial intelligence-driven hyper-personalization has revolutionized marketing efficiency but simultaneously erodes consumer sovereignty through structural constraints on information exploration pathways. This study proposes the Information Foraging Autonomy Score (IFAS) - a novel metric operationalizing consumer decision autonomy within algorithmic recommendation ecosystems. Analysis draws from Cisco Consumer Privacy Survey 2024 (n=3,400 global consumers), Korea Consumer Commerce Association survey 2025-2026 (n=1,000 Korean consumers), EU Digital Services Act consumer impact study (n=2,300), and comparative firm cases including ZARA (SVD+RNN inventory optimization), Sephora Virtual Artist (AR personalization), and Coupang Rocket Membership (domestic subscription benchmark). A Difference-in-Differences model (n=1,247 matched pairs, 2024Q4-2026Q1) demonstrates subscription economies increase IFAS by +23.5 points (p<0.01) versus performance marketing environments. Key findings reveal hyper-personalization reduces choice autonomy by 30% through daily ad exposures exceeding 5,000 impressions (CTR decline -25%, brand avoidance +76%, privacy distrust 91%), while subscription models recover +40% autonomy at modest efficiency cost (ROAS 5.5x→4.9x). Performance marketing exhibits structural exploration suppression (IFAS=35), whereas subscription architectures occupy optimal Autonomy-Efficiency Matrix quadrant (IFAS=70, ROAS=4.9x). Korean food e-commerce applications demonstrate viability: regional specialty producers can implement "default box + 3 alternatives" structures maintaining diversity while leveraging subscription lock-in benefits. Theoretical contributions include IFAS framework extending information foraging theory to algorithmic marketing, practical Autonomy-Efficiency Matrix for strategy positioning, and Korea-specific PIPA/AI Basic Act 2026 compliance roadmap linking consumer rights (explanation/opt-out) to marketing KPIs. Findings challenge the personalization=utility paradigm, demonstrating autonomy preservation as long-term LTV prerequisite amid escalating privacy fatigue and regulatory pressures.
Hyper-personalization; Consumer sovereignty; Information foraging; Subscription economy; Algorithmic marketing; Consumer autonomy; Digital advertising; AI marketing; Choice overload
Machine Learning-Driven Early Warning Analytics for Identifying Market Manipulation, Irregular Trading Activity, and Suspicious Market Signals in U.S. Stock Markets
This paper develops a machine learning-driven early warning framework for identifying market manipulation, irregular trading activity, and suspicious market signals in U.S. stock markets, while grounding the argument in public regulatory evidence rather than purely illustrative examples. The study is motivated by three practical problems. First, conventional rule-based surveillance remains necessary but often produces large alert volumes, high false-positive burdens, and limited capacity to prioritize weak but meaningful signals. Second, suspicious trading increasingly spans multiple channels, including order flow, cross-venue behavior, public narratives, and relational patterns that are poorly captured by isolated tabular indicators. Third, the strongest public evidence available to researchers is fragmented across enforcement summaries, disciplinary statistics, and whistleblower reporting, which means rigorous early warning research must integrate official sources even when transaction-level labels remain limited. Accordingly, the manuscript combines scholarly synthesis with descriptive evidence derived from Securities and Exchange Commission and Financial Industry Regulatory Authority datasets and reports covering the 2021-2023 period.
Market manipulation; abnormal trading; suspicious market signals; U.S. stock markets; machine learning; anomaly detection; graph neural networks; surveillance analytics; explainable AI
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A Generative AI–Driven Clinical Decision Support Framework Using Large Language Models
Early disease detection aids in the correct diagnosis and treatment of illnesses. A Clinical Decision Support System (CDS) helps identify illnesses and choose the best course of therapy. This paper presents a Generative AI-powered Clinical Decision Support Architecture based on Large Language Models (LLMs) to predict diseases and support diagnoses. The suggested architecture incorporates both structured clinical information and high-level preprocessing, feature selection, and class-balancing algorithms to increase the predictive accuracy. Experiments were conducted on 400 patient records from the UCI Chronic Kidney Disease (CKD) dataset. GPT-4o was used to learn more complex clinical patterns and aid in diagnostic decision-making. The recommended framework performed well, as evidenced by the accuracy of 99.17, sensitivity of 99.98, specificity of 98.70, F1-score of 98.85, Matthews Correlation Coefficient (MCC) of 98.21, and AUROC of 0.996. These findings are far more effective than conventional ML models and currently available LLM-based clinical methods. The high sensitivity yields a low rate of false negatives, which is essential in the early detection of disease, whereas the high specificity lowers the wrong diagnosis of healthy patients. Altogether, the suggested generative AI-based solution is powerful, consistent, and effective in clinical contexts, which underscores the potential of large language models (LLM) in medical decision support systems of the next generation.
Clinical Decision Support System, Chronic Kidney Disease, Electronic Health Records, Machine Learning, Disease Prediction, Large Language Models
Securing Enterprise Data for LLM-Powered Applications: A Reference Architecture for Inference-Time Data Protection
Enterprises are rapidly adopting large language models (LLMs) by connecting them to internal data sources through approaches like retrieval-augmented generation (RAG), natural language database querying, and API based tool calling. In most enterprise deployments, the LLM itself is not hosted with in the company’s cloud tenant, but hosted by a third-party provider, which means the company data including customer records, financial information and proprietary documents must leave the organization’s, controlled environment every time a query is processed. This applies to the adoption of Saas products as well that leverage GenAI capabilities, where often times the data leaves the organization, the Saas platforms and are sent to the LLM vendors. Contractual agreements with the LLM providers help, but they cannot fully address risks like prompt injection, unintended data exposure, or regulatory non-compliance. This paper presents a practical reference architecture for securing enterprise data at inference time, organized around three main layers. The first layer addresses the data sources including unstructured content accessed through RAG, structured databases queried through SQL or graph languages, semi-structured records from SaaS APIs, and real-time event streams. The second layer covers the access mechanisms that connect these sources to LLMs, including function and tool calling, the Model Context Protocol, and code execution, along with agentic orchestration that combines multiple mechanisms autonomously. The third layer provides the governance controls such as data classification, role-based and context-aware access control, monitoring, audit trails, and regulatory compliance. The architecture draws on established industry standards including the NIST AI Risk Management Framework and the OWASP Top 10 for LLM Applications. This paper consolidates proven industry security practices into a coherent, practical, layered model that any organization can adapt when deploying LLM-powered applications against their enterprise data.
A data-driven analysis of how AI-driven misinformation and deepfakes affect public trust in US financial institutions
The rapid development of artificial intelligence has fundamentally changed the digital communication environment, allowing spreading information more quickly, as well as creating highly realistic and deceptive content. Misinformation and deepfakes with the involvement of AI has become one of the greatest threats to civil perception, institutional validity, and social stability. Financial ecosystems, in which trust, transparency, and accurate information are the most important factors, spreading deceptive or manipulated content may provoke confusion in the population, distort the perception of risks, and even the loss of legitimacy of the U.S. financial institutions. This study provides a quantitative analysis of these threats in the form of large-scale fact-checking content of the Verified Posts: Fact-Checking Online Content (Politifact) data set. The sample used consists of over 20,000 online posts which have been labeled within the various credibility categories, such as true, false, half-true, pants-fire and mostly-true, covering the years 2008 to 2022. This study through the methods of Natural Language Processing and machine-learning algorithms explores linguistic frames, topic trends, misinformation rates, and propaganda time series. Temporal analysis indicates that the wave of misinformation is related to significant socio-political developments, which indicates that there are external forces, and the amplification of the algorithm. Even though the dataset consists of no video deepfakes, the text-based patterns of misinformation patterns are pretty similar to the manipulation tactics used in the production of synthetic media. This study shows that false accounts, particularly those who concern policy choices, economic outcomes, or governmental activities, can indirectly affect the trust of the population in financial institutions as they change the views of their institutional competence, justness, and stability. The present research makes a contribution to a better comprehension of AI-facilitated misinformation ecosystem evolution and the effect they can have on financial trust. The lessons derived can provide a practical policy to regulators and financial institutions and policy makers to improve the digital governance, enhance the level of awareness and create a strong mitigation policy of misinformation. To promote credibility and sustainability of the financial system in the long term, it is critical to strengthen information integrity so that the society can develop a sense of confidence in the system.
AI-driven misinformation, Deepfakes, Public Trust, Financial institutions, Natural language processing (NLP) and Misinformation classification
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Strengthening U.S. Critical Infrastructure Resilience Through NIST-Aligned Cybersecurity Governance and AI-Driven Threat Detection
U.S. critical infrastructure operators face a persistent gap between high-level cybersecurity frameworks and day-to-day measurable execution, especially under ransomware-driven threat progression. This paper presents an applied, program-to-analytics approach that operationalizes NIST-aligned resilience into auditable actions and metrics while providing a transparent baseline for AI/ML-based threat detection. First, we map five intrusion stages—Initial Access, Privilege Escalation, Lateral Movement, Exfiltration, and Impact—to NIST CSF 2.0 functions and NIST SP 800-53 control-family domains, then define a minimal set of operational metrics (e.g., MFA coverage, patch compliance, MTTD, MTTR, backup restore success, and RTO/RPO achievement) that can be sourced from enterprise systems of record. Second, we implement a sparse-friendly preprocessing and modeling pipeline and evaluate two baseline classifiers on the UNSW-NB15 benchmark dataset (UNSW_NB15_training-set.csv; 175,341 rows; 45 columns) using an 80/20 stratified split (seed=42) and a fixed decision threshold of 0.5. XGBoost achieves ROC-AUC 0.993 and average precision 0.997, with F1 0.969 (TN=10,279; FP=921; FN=575; TP=23,294). Logistic regression (saga) achieves ROC-AUC 0.984 and average precision 0.992, with F1 0.954 (TN=9,230; FP=1,970; FN=281; TP=23,588). The results illustrate baseline tradeoffs under a fixed policy and show how model outputs can be governed through CSF-aligned resilience metrics rather than unsupported deployment claims.
Critical infrastructure resilience; ransomware; nation-state threats; NIST Cybersecurity Framework (CSF) 2.0; NIST SP 800-53 Rev. 5; intrusion detection; UNSW-NB15; XGBoost
Governance Frameworks for Large-Scale ETL Ecosystems in Complex Data Environments
Extract, Transform, and Load (ETL) pipelines have been at the heart of modern data-centric organizations, enabling the integration of large volumes of data from disparate sources into centralized data stores. With the increasing need for organizations to operate within complex data ecosystems characterized by distributed architectures, cloud computing, and diverse data governance needs, traditional ETL management practices have often been inadequate in ensuring transparency, reliability, and compliance to various data governance needs. This research presents a framework for the governance of ETL ecosystems within large-scale data environments, with the aim of addressing the complexities, scalability, and compliance needs of modern organizations. The research draws from a synthesis of existing research in the areas of data governance, workflow orchestration, and metadata-driven architectures to propose a conceptual framework applicable to enterprise data environments. The proposed framework focuses on the need for centralized metadata management, monitoring, and policy-based ETL pipeline management to ensure reliability and accountability in the operation of ETL systems. The research contributes to the emerging discourse on the need for data governance within enterprise environments through the proposed framework for the governance of ETL systems, applicable to large-scale data integration workflows within modern data analytics environments. The findings of the research show the potential for organizations to enhance the trustworthiness, reliability, and compliance of their data through the adoption of data governance principles within ETL systems.
ETL governance, data engineering, data pipelines, metadata management, data governance, enterprise data architecture
Architecting AI-Augmented Enterprise Software Systems: A Systematic Framework for Scalable, Secure, and Event-Driven Cloud-Native Applications
The increasing incorporation of artificial intelligence into software systems in various enterprises has further complicated architectural concerns related to scaling, security, governance, and adaptability. Although cloud-based and event-based architectural approaches are promising for modern enterprise systems, existing architectural approaches rarely consider artificial intelligence incorporation, cloud-based systems, and event-based systems as integral parts. These further limits the potential for artificial intelligence-based decision-making systems in various enterprises. This paper proposes a systematic architectural framework for AI-augmented enterprise software systems, which combines cloud-native design principles and event-driven coordination. By using a design science research approach, a systematic approach is followed in developing the architecture using synthesized architectural requirements from enterprise systems, AI service lifecycles, and distributed cloud environments. In addition, a layered abstraction is introduced in the proposed architecture for enterprise software systems, where AI services are treated as first-class components. The framework is validated using analytic, quality attribute-driven architectural reasoning, thus verifying its ability to tackle key concerns of an enterprise, such as scalability with varying AI-driven workload, secure interactions between services, resistance to partial failures, and long-term maintainability. By abstracting architectural patterns rather than tying them to implementation technologies, we provide a generalized blueprint for designing enterprise-grade AI systems. The proposed framework offers practical advice to enterprises looking to move from initial AI system deployment to scalable, secure, adaptable, and cloud-native systems.
AI-augmented enterprise architecture, Cloud-native systems, Event-driven architecture, AI service lifecycle governance, Scalable and resilient software systems
Zero Trust Based Critical Infrastructure Cybersecurity Framework with AI-Driven Threat Detection and Secure Network Modernization
Critical infrastructure systems increasingly rely on interconnected IoT and message-oriented communication protocols, making them highly vulnerable to sophisticated cyberattacks that can disrupt essential services. Traditional perimeter-based defenses are insufficient against dynamic and insider threats, highlighting the need for continuous verification and intelligent threat detection. This study proposes a Zero Trust based critical infrastructure cybersecurity framework that integrates secure MQTT communication, AI-driven intrusion detection, and automated mitigation within a layered architecture. The proposed framework consists of perception, network, and application layers, where trusted edge devices collect real-time data, a secure message broker ensures protected communication, and a Zero Trust enforcement mechanism continuously validates traffic. At the core of the detection engine, a hybrid GRU+LSTM deep learning model is introduced to capture both short-term and long-term temporal dependencies in network traffic, enabling accurate classification of legitimate and malicious activities. Experiments were conducted using a multi-class MQTT intrusion dataset containing legitimate, DoS, flood, malformed, brute force, and SlowITe traffic. The proposed model achieved 89.21 percent accuracy, 0.90 precision, 0.91 recall, 0.89 F1 score, and 0.99 AUC, outperforming conventional machine learning and standalone deep learning models while also reducing inference time. The framework further enables automated mitigation and real-time monitoring through secure application-layer response mechanisms. These results demonstrate that integrating Zero Trust principles with hybrid deep learning provides a robust and scalable solution for securing critical infrastructure against evolving cyber threats, supporting secure network modernization and resilient cyber defense.
Zero Trust, Critical infrastructure cybersecurity, IoT security, MQTT protocol, Intrusion detection system, Hybrid GRU+LSTM, Deep learning, Multi class attack classification
Harnessing Large Language Models and Agentic AI for Transformative Cloud Reliability and Incident Management: A Comprehensive Suggestive Review
Cloud infrastructure reliability remains a critical challenge as organizations face escalating operational complexity, with the average enterprise experiencing 14-18 hours of downtime annually at costs reaching $14,056 per minute. Traditional incident management approaches, relying heavily on manual root cause analysis, static troubleshooting guides, and reactive remediation, struggle to meet the demands of modern distributed systems. This systematic study examines the emerging application of Large Language Models (LLMs) and Agentic AI in cloud reliability engineering and incident management. Through comprehensive analysis of 100+ research papers, industry reports, and production deployments spanning 2023-2026, we establish a novel taxonomy organized across four dimensions: scope (detection, diagnosis, remediation, prevention), cloud deployment models (single-cloud, multi-cloud, hybrid), autonomy levels (advisory, human-in-the-loop, fully automated), and compliance frameworks (GDPR, ISO 27001). Our findings reveal that LLM-powered incident assistants reduce Mean Time to Resolution (MTTR) by 40-60%, while multi-agent orchestration systems demonstrate 90% performance improvements for specific workloads. We propose a comprehensive evaluation framework integrating reliability metrics (MTTD, MTTR, incident recurrence), safety indicators (change failure rate, rollback frequency), human factors (cognitive load, trust, explainability), and data privacy governance. This research bridges critical gaps between DevOps/SRE literature and contemporary LLM applications, emphasizing production deployment guardrails, access control, observability patterns, and regulatory compliance, areas traditionally underdeveloped in academic AI research. Organizations implementing these frameworks can expect 30-70% reduction in incident response time, $400B annual savings potential across Global 2000 companies, and enhanced operational resilience through intelligent automation.
Comparative Analysis of AI-Generated Video Marketing Content: Evaluating OpenAI’s Sora and Gemini’s VEO Generated Video Modalities
The rapid proliferation of generative artificial intelligence (GenAI) has fundamentally disrupted digital marketing, yet empirical research comparing the efficacy of specific high-fidelity text-to-video models remains scarce. This study addresses this gap by conducting a quasi-experimental field comparison of video advertising content generated by OpenAI’s Sora 2 and Google’s Veo 3.1. Set within the context of an international student recruitment campaign for a U.S. university targeting prospective students in Bangladesh, the research analyzes the performance of two distinct ad sets across Facebook, Instagram, and YouTube. Using a standardized prompt engineering protocol to minimize creative bias, the study measured key performance indicators across the marketing funnel, including View Rate, Engagement Rate, Click-Through Rate (CTR), and Cost Per Acquisition (CPA). The results reveal a significant performance trade-off between the two models. Video content generated by Sora 2 demonstrated superior upper-funnel performance, achieving significantly higher View Rates (p < 0.05) and Engagement Rates (p < 0.05), attributed to its cinematic quality and visual novelty. Conversely, content generated by Veo 3.1 outperformed in lower-funnel metrics, delivering a significantly higher CTR (p < 0.05) and a lower CPA (p < 0.05), driven by its capability to generate clear, synchronized audio and dialogue. These findings suggest that "aesthetic novelty" drives attention while "utilitarian clarity" drives action. The study contributes to the literature on AI in advertising by validating the application of the AIDA model to GenAI content and offers actionable managerial implications: marketers should not view these tools as interchangeable but rather select them strategically based on specific campaign objectives, using Sora 2 for brand awareness and Veo 3 for direct response and lead generation.
The Role of HRM in Managing Employee Privacy and Data Security in IoT-Enabled Healthcare Organisations
The current study explores the role of human resource management in facilitating privacy protection and data security of employees of IoT-enabled healthcare organisations, whereby constant monitoring and data production has become part of routine work processes. A qualitative research design was chosen and thematic analysis of secondary literature based on peer-reviewed journals, policy documents, and sources of reliable information on the industry. The method will allow synthesizing evidence on the governance, ethics, and workforce implications of IoT adoption in healthcare in a systematic manner. The analysis shows that there are four fundamental themes, which include: HR governance and data ethics, consent and transparency, surveillance and performance control and regulatory compliance. Results indicate that IoT has led to increased efficiency and control, but it also causes ethical threats, lack of trust, and increased vulnerability to data breaches when not regulated effectively. Implications: The research indicates that HRM is a key governance player that has to convert technical protections into ethical policies, consent, training, and compliance procedures that continue to keep employees trusting and the organisation legitimate. Future studies are needed to examine the perceptions of the IoT monitoring among employees, their longitudinal effects on trust and wellbeing, as well as the efficacy of integrated HR-IT models of governance within a variety of healthcare settings.
Human Resource Management, Employee Privacy, Data Security, Internet of Things (IoT), Healthcare Organisations, Workplace Surveillance, Ethical Governance
Ethical and Explainable AI Frameworks for Responsible Business Analytics in the U.S. Economy
The rising adoption of artificial intelligence (AI) in business analytics has changed the manner in which organizations are analyzing the massive data on consumers and making business decisions. Automated analytics in the U.S. financial services industry are becoming more important to regulatory bodies and financial institutions as a tool to detect risk, rank consumer complaints, and track institutional behavior. This application of AI, with the stakes so high, contains such a lively ethical question as the absence of transparency, a weak accountability level, and the possibility of harming consumers due to a non-transparent or biased decision-making process. This study attempts to solve these problems by suggesting an ethical and elucidable AI framework of responsible business analytics based on consumer complaint data on the Consumer Financial Protection is provided under this section.Consumer Financial Protection comes in this section. Bureau Consumer Complaint Database. The authors of the research investigate those mortgage debt consumer complaints that were posted in 2019-2022 and use natural language processing to categorize the narrative of complaints into meaningful issue categories. To manage responsible AI usage, the framework proposed will combine interpretable machine learning models with post-hoc explain ability frameworks that will justify the automated decisions by humans in a manner that is understandable. Explainable AI methods are applied to point out important textual characteristics that determine the classification of a complaint to allow transparency and auditing to business stakeholders and the regulators. Besides model performance evaluation, the study also considers ethical aspects of transparency, accountability, and fairness of automated complaint analytics. The framework is evaluated based on various criteria of evaluation, such as classification accuracy, explanation fidelity, stability and human interpretability. This study will fill the gap between theoretical ethical ideals and realistic business practices because it shows how explainable AI can be integrated into the framework of a regulatory-oriented analytics pipeline. The results provide contributions to the academic and industry practice through the provision of a reusable framework that can support the use of AI in a regulated business setting by instilling trust. This study demonstrates that explainable and ethical AI systems are capable of increasing regulatory trust, organizational accountability, and responsible decision-making in the U.S. economy without affecting analytical effectiveness.
Ethical Artificial Intelligence, Explainable AI (XAI), Responsible Business Analytics, Consumer Finance Protection, Natural Language Processing and Financial Services Regulation
An IoT-Based Remote Patient Monitoring Architecture Integrating Edge AI and Blockchain for Chronic Disease Management
The maintenance of chronic disease requires a continuous health checkup to prevent complications and minimize the number of times one goes to the hospital. The Internet of Things (IoT) is utilised to develop Remote Patient Monitoring (RPM) systems that assist doctors in gathering and reviewing health information in real-time. Although these systems have the potential to enhance care delivery, they are still affected by challenges such as delay of communication, poor security, system incompatibility and ineffective usage of the systems in the zones with weaker internet connections. The current paper introduces an IoT-based RPM system with six layers. It has Edge Artificial Intelligence (AI) to search promptly through anomalous health information, an impregnable blockchain to secure and conserve health records securely, and a digital twin design to assist in making forecasts about the future. This system was tested using a Python simulation mechanism in which sample health data were used. The performance indicated rapid identification of issues within 10.46 milliseconds and great data security based on blockchain. Figures and charts demonstrated that the system could sort out the normal and abnormal health data accurately and assist the physicians in making superior judgments. This design fixes most of the issues with the existing RPM system design and provides a secure, intelligent, and adaptable mode of chronic disease management.
Artificial Intelligence (AI), Blockchain Security, Chronic Disease Management, Digital Twin, Edge Computing, Health Data Privacy, Internet of Things (IOT), Medical GADGETS (m-IoT), Remote Patient Monitoring (RPM), Real-Time Monitoring, Interoperability, Smart Healthcare Systems, Wearable Sensors, 5G Connectivity.
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Cloud-Native Architectures in Financial Services: Enhancing Scalability and Security with AWS and Kubernetes
The financial industry's service delivery model is constrained by unique requirements; volatile traffic, high fault-tolerance engineering needs, and regulatory compliance require security controls to demonstrate their effectiveness. The purpose of this research is to explore whether cloud native architecture based on AWS primitives and Kubernetes can provide scalable and secure solutions for the financial sector, including compliance for financial sector operational resiliency and regulatory compliance standards (GDPR security obligations, PCI DSS, SOC 2 and other related security obligation). To achieve this objective, authoritative documentation from the Cloud Native Computing Foundation (cloud native computing foundation) and the official documentation for Kubernetes and AWS, along with relevant security documents published by NIST (containerization, micro-services, service mesh, zero trust architecture) and the financial sector's guidelines for managing risk were synthesized. A compliance aware reference architecture was proposed, which defines and enforces system boundaries (network, identity, workload, and data) that employs declarative automation to tie runtime telemetry to audit evidence. Scalability is viewed as a closed-loop feedback mechanism, enabling consideration of load balancing, platform autoscaling, and Kubernetes control loops that include security externalities such as an EDoS attack against autoscaling. The security aspects of identity, least privilege, encryption/key management, segmentation, vulnerability management, tamper evident logging, and incident response preparedness were also examined. The case studies cited within the document represent regulated or finance adjacent production deployment examples that were utilized to support the analysis with measurable outcomes and implementation realities.
Cloud-Native Architectures; Financial Services; Scalability and Security; AWS and Kubernetes
Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
Prescriptive Analytics on Anonymized Patient Data Using Regression and Distributed Computing
The scale of digital healthcare has resulted in an unprecedented increase in patient data generated from clinical records, monitoring devices, and expansive health information systems. Predictive analytics has become a highly effective method for converting these types of data into actionable data that can be used to foster early diagnosis, predict outcomes, and provide preventive care to patients. Nonetheless, patient information is sensitive, and this issue poses substantial privacy and security threats, especially when data are processed within distributed and multi-institutional settings. This study explored the use of predictive analytics as regression on anonymized patient data through a distributed computing architecture. Using machine learning workflow solutions based on Apache Spark, the proposed solution can provide scalable data processing and effective model training with a low risk of privacy loss. Linear and regularized regressions were used to determine the predictive performance under different privacy conditions. It also explores the trade-off between the predictive utility and privacy of data in distributed healthcare analysis. These findings show that distributed regression models can achieve predictive accuracy with easily obtainable levels of reliability and privacy-sensitive data analysis, which are suitable for large-scale healthcare decision-support systems.
Predictive analytics; anonymized data; regression; distributed computing
Copyright (c) 2022 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
Digital Service Factories: AI-Driven Lifecycle Service Orchestration Beyond Connectivity
The evolution from isolated OSS/BSS processes to fully automated digital service factories marks a significant advancement in telecom operations. While Lifecycle Service Orchestration (LSO) traditionally centers on connectivity, Digital Service Factories broaden this scope to encompass the entire service lifecycle including provisioning, scaling, policy enforcement, assurance, billing, and customer experience management. In this paper, we introduce DSF-AI, an AI-powered service factory framework designed to translate business intent into actionable workflows, enforce policies as code, and orchestrate network and cloud resources seamlessly across on-premises infrastructure, Azure, and AWS. Leveraging reinforcement learning for ongoing service optimization, pilot migration demonstrated substantial benefits where service activation times improved by 60% (from 4.8 hours to 1.9 hours), manual handoffs per incident were reduced by half, SLA compliance reached 98.5% over 30 days, and policy violations decreased by 70% compared to legacy CI/CD approaches. This paper outlines DSF-AI’s layered architecture, details its AI-driven orchestration engine, visualizes data flows and process sequences, and provides a quantitative assessment along with practical insights for transformation leaders.
Digital Service Factory, Lifecycle Service Orchestration, AI-Driven Orchestration, Telecom Automation, Multi-Cloud, Policy-as-Code
Venkata Manikanta Sai Ramakrishna Chundi
Modernizing Mission-Critical Systems: A Hybrid-Cloud Transformation Roadmap
Large enterprises running mission-critical legacy systems for decades, such as Layer-1 network fault-management platforms, seek the agility of hybrid-cloud deployments without service disruption. This paper emphasizes best practices for migrating monolithic, on-premises applications to containerized, Infrastructure-as-Code (IaC) pipelines spanning on-prem and public clouds (Azure, AWS). We generalize learnings from a telecom fault-management modernization by assessing stateful dependencies, applying the Strangler Fig pattern for incremental decomposition, codifying policies via Open Policy Agent, and orchestrating blue/green field cutovers. Key outcomes across two pilot programs include: 45–60 % reduction in deployment lead time 70 % fewer manual remediation tickets during rollout 30 % improvement in fault-detection-to-resolution time 100 % policy compliance via automated checks We present an extensible five-phase framework, diagrams illustrating architecture and workflow, quantitative findings, and a roadmap guiding transformation leaders through common pitfalls and decisions.
Hybrid Cloud, Legacy Modernization, Microservices, Infrastructure as Code, DevOps Transformation, Fault Management, Cloud Computing, Transformation
Accelerating Delivery: A Unified Framework for Enterprise CI/CD Standardization
Enterprises frequently develop disparate CI/CD pipelines across different teams, resulting in inconsistent practices, quality deficiencies, and governance vulnerabilities. To address these challenges, we present EntDevOps, a standardized DevOps pipeline framework that integrates a centralized template library, policy-as-code checkpoints, and consolidated metrics. Over a six-month period, EntDevOps was deployed within three projects in network services and analytics. Prototype assessment demonstrated the following outcomes: 48% reduction in build failures (from 25% to 13%) 35% decrease in mean commit-to-deploy time (from 42 minutes to 27 minutes) 72% improvement in automated policy compliance rates (from 58% to 100%) 22% increase in team satisfaction scores This report discusses framework architecture, implementation methodology, architectural and workflow diagrams, quantitative evaluation results, and key insights gained. The recommended best practices provide enterprises with a framework to implement consistent, secure, and efficient DevOps processes across the organization.
DevOps Standardization, CI/CD Framework, Pipeline Templates, Deployment Automation, Enterprise Governance, Quality Metrics, DevOps Transformation, Cloud Computing, Transformation
Comparative Analysis of REST, WebSocket, andFIX Protocols for Cryptocurrency Market Data: AComprehensive Benchmarking Study
Low-latency market data access is critical for algorithmic trading, market making, and high-frequency trading strategies in cryptocurrency exchanges. This paper presents a comprehensive empirical study comparing three widely-adopted market data protocols: Representational State Transfer (REST), WebSocket (WS), and Financial Information eXchange (FIX). We developed an open-source benchmarking framework to systematically measure end-to-end latency, throughput, and statistical distribution characteristics across these protocols using Coinbase exchange as our primary test venue. Our experiments span multiple time horizons (30 seconds, 2 minutes, and 5 minutes) and analyze over 50,000 messages across all protocols. Results demonstrate that FIX achieves sub-millisecond latencies (P50: 0.177-0.263 ms) on local loopback, REST exhibits median latencies of 22-25 ms with moderate tail behavior, while WebSocket shows higher variability (P50: 72-89 ms) but better scaling characteristics. We quantify tail latency behavior through percentile analysis (P90, P95, P99, P999), calculate statistical moments (skewness, kurtosis), and provide actionable insights for infrastructure architects and trading system designers.
Cryptocurrency, Market Data, Latency Benchmarking, REST API, WebSocket, FIX Protocol, High-Frequency Trading, Performance Measurement
The Recognition Gap: How Women’s Technical Abilities Remain Invisible in the AI Age
This paper examines the persistent gender gap in the tech industry, which stems from a lack of appreciation for skills, rather than technical aptitude. Using the principles of Social Cognitive Theory and the concept of the “Second Digital Divide,” a meta-analytical review of 54 journal articles published between 2021 and 2025 across 15 countries was conducted. The results show that women score as well as men on technical proficiency tests, yet appreciation of identical tasks is reduced by 12% as soon as the appreciator recognizes the gender of the author. The case analysis of Artificial Intelligence (AI) orchestration for women-led startups in Southeast Asia reveals that, although they achieve 35% higher productivity with AI-integrated workflows, they receive 60% less institutional funding than male-led startups. Here, I propose the “Glass Wall” phenomenon, in which women’s technical expertise is labeled “prompt dependency” and relegated to the domain of appreciation for architectural skills. The suggestions for change include implementing blind code review practices, measuring skill delivery progress asynchronously, and leveraging the unseen efforts of documentation and ethical auditing in promotion cycles.
Gender; technology; gatekeeping; women in STEM
Self-Regulating AI Agents: A Runtime Constitutional Framework for Autonomous Decision Systems in Cloud-Native Environments
The increasing trend of deploying autonomous AI agents in cloud-native environments has enabled the automation of real-time and large-scale decision processes in enterprise and industrial systems. Nevertheless, the existing governance and alignment mechanisms remain external to the operational cycle of the agents. The current mechanisms rely on static policies and offline validation or infrastructure-level controls. Such mechanisms are not effective for agents that dynamically plan and collaborate and change their behavior at runtime. This paper proposes a runtime constitutional framework for self-regulating autonomous decision systems deployed in cloud-native environments. The framework incorporates machine-understandable governing principles into the operational cycle of AI agents and enables the monitoring, contextualization, and correction of every decision made by the agents before their execution. The framework is designed as an architectural structure comprising a constitutional rule layer, contextual state observation, decision interception, constitutional reasoning, and self-correction and adaptation. This makes the framework an independent mechanism of governance. Unlike existing alignment and policy enforcement mechanisms, the framework focuses on the regulation of the behavior of the agents at runtime and not at the training or deployment stages. This paper proposes an architectural and methodological contribution that offers a scalable and platform-independent solution for the runtime governance of autonomous decision systems. The proposed framework supports the development of dynamic cloud infrastructures and multi-agent systems, offering a practical solution for the development of reliable and human-aligned autonomous decision systems.
Autonomous AI agents, Runtime governance, Constitutional AI framework, Cloud-native decision systems, Human–AI collaboration
Collaborative Learning and Teaching in Digital Environments: A Systematic Review of Two Decades of Research
This study aimed to conduct an SR of the author’s own studies published between 2004 and 2025 on collaborative learning in EFL (English as a Foreign Language), AFL (Arabic as a Foreign Language), translation, reading, writing and research skills among students from Saudi Arabia, Ukraine, Russia and India. The 26 studies were categorized into 3 thematic clusters: (i) Who collaborated (local collaboration within the same class or institution, national collaboration across Saudi universities, and global collaboration across countries); (ii) which skills and courses (translation, reading, writing, grammar, research, general EFL and AFL, teachers’ professional development); and (iii) which technologies (Twitter, Facebook, Skype, SMS, Chatbox, Elluminate, WebEx, Polycom based systems supported by Sony and Tandberg units, mobile e books, online discussion forums, and Learning Management Systems such as Blackboard, WebCT, Moodle, Makkah eLearning, and Nicenet). Findings revealed that most collaborative learning cases were effective in enhancing students’ linguistic skills, cultural awareness, interactions, engagement, and teachers’ professional development across a variety of contexts and skill areas. Successful collaborations typically featured clear student-centered activities, task assignment, accessible technologies, active instructor facilitation, and that supported sustained interaction. The students benefited from opportunities for exchanging ideas, practicing skills, and engaging with peers, giving and receiving feedback, which contributed to improved motivation, interaction and communicative competence. Additionally, online collaboration fostered a stronger sense of community among the students, encouraged reflective learning, and supported the development of digital communication skills across multiple platforms. However, two studies reported a complete failure in collaboration due to unfamiliarity, students’ reluctance to interact publicly with students and teachers whom they did not know, cultural constraints, and limited digital readiness. Cross cutting analysis revealed that collaboration is highly sensitive to cultural, contextual, and institutional factors, and that its success depends on alignment between students’ preparedness, technological support and instructional design. Overall, the review showed that collaborative learning has a great potential for language skills development, but its effectiveness is dependent on how it is conducted, cultural responsiveness, and sustained instructional support from collaborating teachers and the online community.
Systematic Review (SR), Collaborative Learning, interaction, communication, Teaching and learning, Digital Environments, student-student, student-instructor, local collaboration, national collaboration, global collaboration
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
Agentic AI Modernization: Transforming Institutional Infrastructure Through Orchestrated Multi-Agent LLM Framework
While managing constrained funds and strict regulatory requirements, the higher education institutions are under unprecedented pressure to modernize outdated information systems, such as mainframe-based Student Information Systems (SIS), custom registration platforms, legacy Learning Management Systems (LMS) and Enterprise Resource Planning (ERP) deployments. The complexity of institutional governance is being overlooked by the conventional single-agent based approaches to legacy modernization, which is delaying the digital transformation and creating security vulnerabilities. In order to achieve end-to-end code analysis, intelligent planning, safe migration and rigorous validation through specialized agents coordinated by institutional governance patterns, this research presents a novel agentic architecture using multi-agent Large Language Model (LLM) frameworks, created especially for higher education legacy system modernization. Crucially, this research innovates the deployment process, where we suggest an on-premises implementation strategy that natively protects sensitive student and faculty data, while maintaining GDPR, CCPA and FERPA compliance. Which projects to be challenging by cloud-based solutions, as these introduce data residency and compliance complexities. For COBOL/MUMPS/PL-I legacy codebases, our research demonstrated 87% successful modernization rate, with a 65% decrease in manual intervention and a 78% improvement in documentation accuracy. By mapping multi-agent workflows to existing institutional governance structures, academic committees, change boards and divisional responsibility models, the framework accomplishes institutional alignment, thereby increasing the credibility and organizational compatibility of agentic modernization solutions. This study offers institutions a revolutionary route to modernization, that maintains institutional data sovereignty, while significantly cutting modernization timelines and costs by bridging cutting-edge AI research with useful higher education IT strategy.
Agentic AI, Multi-Agent LLM Frameworks, Legacy System Modernization, Higher Education IT, Data Privacy Compliance, FERPA, GDPR, CCPA, On-Premises Deployment, Educational Technology Infrastructure, Artificial Intelligence, Large Language Models