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 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
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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
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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
Copyright (c) 2026 Journal of Computer Science and Technology Studies
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
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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
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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
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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
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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
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
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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
Deep Learning-Enabled Demand Intelligence: Transforming Forecast Accuracy in high velocity transaction systems
High-velocity transaction systems, including digitally integrated supply networks, omnichannel commerce platforms, and real-time manufacturing systems, produce continuous and heterogeneous demand signals that pose challenges to traditional, batch-oriented approaches to demand forecasting. Traditional analytical processes, which are based on the assumption of stationarity and stable patterns of demand, are no longer able to produce timely, reliable, and operationally actionable insights under the conditions of dynamic market conditions and execution processes. This paper aims to conceptually elaborate the notion of deep learning-enabled demand intelligence as a new paradigm of shifting from periodic forecasting to continuous, contextualized, and decision-embedded interpretation of demand signals. The paper proposes a unified conceptual and architectural framework for integrating the concepts of streaming data ingestion, automated representation learning, multi-source signal fusion, and low-latency inference within the context of an event-driven operational environment. Additionally, this paper proposes learning strategies for the application of deep learning techniques within the context of high-velocity systems, including incremental learning, concept drift handling, and transfer learning for handling sparse or cold-start demand signals. Besides the application of predictive learning, the paper also addresses the aspects of robustness, awareness of uncertainty, explainability, and human-AI collaboration within the context of the proposed framework. By integrating the concepts of system design principles, learning mechanisms, and their implications for organizations, this paper proposes a unified framework for the application of deep learning-enabled demand intelligence within the context of digitally intensive organizations and outlines future research directions for developing human-centric, continuously adaptive systems of demand management.
Deep learning, High-velocity transaction systems, Real-time demand forecasting, Streaming analytics and online learning, Human-centric AI for operational decision-making
Strategic Framework for Enterprise Cybersecurity Management: Integrating Intelligent Anomaly Detection for Proactive Threat Mitigation
In the contemporary digital business landscape, cybersecurity has emerged as a critical strategic imperative for enterprise sustainability and competitive advantage. This paper presents a comprehensive theoretical framework for enterprise cybersecurity management that integrates intelligent anomaly detection mechanisms with proactive threat mitigation strategies. Drawing upon established theories including the Technology-Organization-Environment (TOE) framework, Dynamic Capabilities Theory, and Risk Management Theory, this study develops a multi-dimensional model for understanding and implementing effective cybersecurity governance in business organizations. The proposed Strategic Cybersecurity Management Framework (SCMF) encompasses five interconnected dimensions: organizational readiness, technological infrastructure, human capital development, governance mechanisms, and continuous improvement processes. Through systematic analysis of existing literature and industry best practices, this paper identifies critical success factors, key performance indicators, and implementation guidelines for organizations seeking to enhance their cybersecurity posture. The framework provides actionable insights for business leaders, IT managers, and policymakers navigating the complex cybersecurity landscape while maintaining operational efficiency and business continuity.
A Self-Systematic Review of Mobile Apps for Developing Multiple Language Skills in EFL
This study fills a gap in the Mobile Assisted Language Learning (MALL) literature by conducting a self systematic review of the author’s research program published between 2009 and 2023. The review synthesizes empirical studies on the use of mobile apps to support the development of listening and speaking, reading, vocabulary, test preparation, dictionary use, and grammar skills among freshman students at the College of Languages and Translation, King Saud University, Riyadh. It also includes studies in which mobile learning was partially integrated through blogs, podcasts, and text to speech applications. The corpus was organized into seven clusters: general mobile app use in EFL, listening and speaking apps, reading apps, vocabulary learning, test preparation apps, dictionary apps, and grammar podcasts. Across all studies, a consistent pattern emerges: mobile based tools—whether reading apps, fiction apps, e books, audiobooks, vocabulary trainers, flashcards, news and grammar podcasts, or specialized dictionaries—significantly enhanced students’ language skills when used as structured, supervised supplements to classroom instruction. Despite variation in skill focus and app type, the studies collectively show that mobile learning environments increase accessibility, flexibility, personalization, and learner autonomy, enabling students to practice language skills anytime and anywhere, at their own pace and proficiency level. A meta level synthesis reveals that the effectiveness of mobile apps lies not in the technology itself but in the pedagogical framework guiding their use. Successful studies consistently employed a three phase instructional model: a pre task phase involving app selection, orientation, and pre questions; a task phase involving guided practice, individual or collaborative work, and online interaction; and a post task phase involving feedback, discussion, evaluation, and follow up. This structured approach led to measurable improvements in reading comprehension, listening comprehension, vocabulary development, literary appreciation, test taking skills, and ESP terminology acquisition. Students also reported increased motivation, confidence, engagement, and enjoyment, particularly when apps provided multimodal input aligned with their proficiency levels. Overall, the review demonstrates that mobile learning is most effective when purposeful, scaffolded, interactive, and aligned with clear learning outcomes. Mobile apps do not replace instructors; rather, they extend instructional time, diversify input, and create new opportunities for practice and feedback.
Mobile-Assisted Language Leaning (MALL), mobile learning (M-learning), Mobile apps, mobile devices, systematic review (SR), longitudinal research program, podcasts for learning, audiobooks, EFL college learners, Saudi context
Autism Spectrum Disorder (ASD) Detection Using Multiple Deep Learning Models and Comparison of Model Performances
Autism Spectrum Disorder (ASD) impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning techniques such as 1D CNN, DNN, LSTM etc. have been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). The conventional method for diagnosing ASD involves clinical evaluations which are often time-consuming and subjective. With the rising prevalence of ASD, the demand for automated, scalable, and accurate detection systems has increased. Deep Learning (DL) models, including 1-Dimensional Convolutional Neural Network (1D CNN), Deep Neural Network (DNN), LSTM, BiLSTM, CNN-LSTM have shown remarkable results in this domain. This paper compares multiple deep learning techniques using publicly available ASD datasets for Children, Adolescents, and Adults. Experimental results demonstrate that the DNN achieves the highest accuracy of 93.65% for adolescents, 99.90% for adults and 99% for children, while 1D CNN, LSTM, BiLSTM and CNN-LSTM also offer competitive results.
CRM Extensions for Retail Fuel Industry in USA Accounting: Efficiency Gains and ROI
The retail fuel segment of the downstream fuel industry relies heavily on timely and accurate financial reporting, yet traditional workflows remain dependent on manual data collection, spreadsheet-based consolidation, and week-long bookkeeping cycles. This paper presents a custom-built retail in-house accounting system, developed as the companion framework to the previously published research “CRM Extensions for Wholesale Jobbers in USA Operations: Efficiency Gains and ROI”. While the wholesale CRM system automated dispatch, pricing, supplier reconciliation, and invoicing, the retail accounting system addresses the equally complex challenge of integrating C-store sales data, fuel inventory, credit card settlements, cash summaries, paid-outs, and merchandising transactions into a unified platform capable of directly posting journal entries to QuickBooks Online (QBO).Built on the same web application and SQL database architecture as the wholesale CRM, the system utilizes API integrations, FTP ingestion, CSV automation scripts, and audit-tracked workflows to eliminate multi-team data collection and manual spreadsheet preparation. Core modules include monthly sales summaries, credit card settlement reconciliation, cash management, expense tracking, station-level inventory, EFT and batch summaries, and a suite of profitability dashboards. Together, these modules reduce manual workload, accelerate month-end close, improve cash visibility, and ensure complete accuracy by removing manual entry errors.The retail system further leverages data already generated in the wholesale CRM such as fuel delivery records, EFTs, and inventory data creating a seamless wholesale-to-retail integration layer where wholesale sales become automated retail purchase entries. Emerging AI applications, including sales forecasting using decision tree and random forest models, computer vision for receipt scanning, and future NLP-based incident log automation, position the system for next-generation operational intelligence. This research demonstrates how combining wholesale CRM automation with integrated retail accounting capabilities forms a unified financial backbone for fuel enterprises, enabling faster reporting, higher accuracy, and stronger data-driven decision-making across the entire rack-to-retail value chain.
Operational Monitoring for Enterprise Chatbots: Webex Teams–Based Alerting for NLU Drift, Fallbacks, and Service Health
Conversational AI systems are rapidly becoming mission-critical components of enterprise support and service delivery. As organizations delegate increasingly complex operational workflows to chatbots, the reliability and semantic correctness of conversational pipelines directly affect business continuity, security posture, and user trust. However, the production behavior of conversational AI systems is inherently non-stationary: natural language usage evolves, business processes change, and machine learning models degrade under concept drift. Moreover, conversational platforms depend on complex distributed infrastructures, introducing additional operational risks. This paper presents a comprehensive analysis of operational monitoring for enterprise chatbots, with a focus on production observability and incident response using Webex Teams–based alerting. We examine core conversational health signals including fallback rate spikes, intent confidence distribution shifts, latency anomalies, error patterns, and knowledge base miss ratios. Drawing on established research in distributed systems monitoring, site reliability engineering, and machine learning operations, we propose an end-to-end monitoring and alert pipeline specifically tailored for conversational AI systems. We further describe the integration of Kibana dashboards to provide engineers with immediate contextual insight during incidents. While no new empirical performance results are claimed, the paper synthesizes validated engineering practices into a unified operational framework for managing enterprise conversational AI deployments.
Conversational AI Monitoring; Enterprise Chatbots; NLU Drift Detection; Production Observability; Incident Response; MLOps; Webex Teams Alerting; Kibana Dashboards
Copyright (c) 2022 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0
Assessing The Development of an Automated Fan Control System
In today's world, automation is necessary for efficient and responsive systems. Automated systems provide significant benefits such as increased productivity and reduced human error, making them increasingly important across various industries. The development of an automated fan control system addressed the limitations of the existing manual fan. Manual fan control often leads to energy waste, insufficient comfort, and higher operating costs. This study aimed to address this gap by developing and implementing an automated fan control system that correctly maintained and optimized temperature, convenience, and reduced energy usage. The automated system was designed for real-time monitoring, utilizing advanced algorithms to control fan operations based on detected human movement and real-time conditions. The results showed that respondents assessed the acceptability of the automated fan control system (AFCS) using the Technology Acceptance Model (TAM). The results from the study provided strong suggestions to adopt and implement automated fan control systems to enhance environmental sustainability and user satisfaction.
Automated Fan Control System, Smart Classroom, Environmental Control, Thermal Comfort, Technology Acceptance Model (TAM)
AI-Powered Sprint Planning & Backlog Management for Project Lifecycle Management
The synergy between Artificial Intelligence (AI) and Agile Scrum methodologies is fundamentally transforming project lifecycle management. This journal explores the profound impact of AI on sprint planning and backlog management, moving beyond traditional, human-centric approaches to embrace data-driven, predictive, and adaptive strategies. We delve into how AI algorithms, machine learning models, and natural language processing can optimize sprint forecasting, automate backlog grooming, simulate capacity scenarios, and enhance risk identification. This paper proposes a comprehensive framework for integrating AI into the Scrum Master's toolkit, repositioning them as "intelligence orchestrators" who leverage AI to foster greater efficiency, predictability, and team satisfaction. Through theoretical exposition, practical applications, and future outlooks, this journal aims to provide a unique perspective on the evolution of Agile project management in the age of AI.
Artificial Intelligence, Agile, Scrum, Sprint Planning, Backlog Management, Project Lifecycle Management, Machine Learning, Natural Language Processing, Predictive Analytics, Capacity Planning.
Comparing Vision Transformers and Convolutional Neural Networks: A Systematic Analysis
Vision Transformers have emerged as powerful alternatives to Convolutional Neural Networks for image classification tasks. Systematic comparisons under controlled settings remain limited despite growing adoption of transformer-based vision models. The present article conducts comprehensive evaluation of ViTs and CNNs across identical datasets, training conditions, and computational budgets. Multiple architectures including ResNet, EfficientNet, ViT-Base, and DeiT undergo training on benchmark datasets such as CIFAR-10, CIFAR-100, and customized real-world datasets. Performance evaluation encompasses accuracy, F1-score, training stability, adversarial robustness, and inference latency metrics. Results demonstrate that ViTs outperform CNNs on larger datasets while exhibiting superior robustness to noise and perturbations. CNNs maintain advantages for small datasets due to strong inductive biases embedded within convolutional architectures. The effective receptive field in deep convolutional networks exhibits Gaussian distribution patterns centered on each spatial location. Vision transformers learn spatial relationships entirely from data through global self-attention mechanisms. Dataset scale fundamentally determines relative performance characteristics between architectural families. Transformer architectures require substantial training data to discover optimal attention patterns. Convolutional networks converge efficiently on smaller datasets through built-in spatial priors. The article identifies specific conditions under which each architecture demonstrates clear advantages. Findings contribute to understanding of transformer-based vision models while offering practical guidance for architecture selection in applied machine learning systems.
Vision Transformers, Convolutional Neural Networks, Image Classification, Deep Learning Architectures, Adversarial Robustness, Transfer Learning
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0
Streamlining DevOps Pipelines with AI-Augmented Feature Flagging for Microservices Architectures
The complexity of microservices-based SaaS applications, coupled with the demands of rapid feature development, poses significant challenges for DevOps pipelines, particularly in managing feature rollouts for large teams. This article proposes an AI-augmented feature flagging system that enhances deployment efficiency by predicting feature stability, optimizing rollout strategies, and automating testing configurations. The system relies on machine learning models, the AWS Bedrock API, and the OpenAI API to identify the changes in the code and runtime metrics and use these to guide flag management, which is combined with Kubernetes and CI/CD, such as Jenkins. A React-based dashboard gives the product managers, QA engineers, and developers real-time insight into the rollout progress. This is based on real-world simulations based upon large-scale deployments of microservices and shows a significant decrease in deployment errors and quicker release cycles. The architectural framework integrates predictive intelligence and automated optimization in order to overcome important issues of development teams that deal with complex distributed systems. Machine learning potential will be integrated into the existing DevOps practices to optimize human decision-making during the deployment lifecycle. The numerous benefits of an organization adopting AI-enhanced feature flagging are an increase in deployment reliability, a shortened release schedule, lowered infrastructure expenses, and the development of strong teamwork. The benefits to the environment could also be seen regarding optimal use of resources and reduced energy use in cloud computing infrastructure. The economic returns are in the form of cost savings, productivity, as well as reduced time-to-market in revenue-generating features. The system makes feature flag management more of a proactive risk management process rather than a problem-solving approach. Gradual implementation plans allow building organizational capability stepwise and prove its value at every step. These governance regimes guarantee the right human control and have real control of important issues of deployment. The modular architectural design will be compatible with the existing DevOps toolchains and allow gradual improvement as the organizational capabilities advance.
artificial intelligence in software deployment, microservices architecture management, predictive deployment analytics, automated feature rollout optimization, DevOps pipeline automation