Journal of Computer Science and Technology Studies
type: channel
description: 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.
publisher: Al-Kindi Center for Research and Development
language: en
publication name: Journal of Computer Science and Technology Studies
issn: 2709-104X
items: Ethical and Explainable AI Frameworks for Responsible Business Analytics in the U.S. Economy
items: An IoT-Based Remote Patient Monitoring Architecture Integrating Edge AI and Blockchain for Chronic Disease Management
items: Cloud-Native Architectures in Financial Services: Enhancing Scalability and Security with AWS and Kubernetes
items: Prescriptive Analytics on Anonymized Patient Data Using Regression and Distributed Computing
items: Digital Service Factories: AI-Driven Lifecycle Service Orchestration Beyond Connectivity
items: Modernizing Mission-Critical Systems: A Hybrid-Cloud Transformation Roadmap
items: Accelerating Delivery: A Unified Framework for Enterprise CI/CD Standardization
items: The Recognition Gap: How Women’s Technical Abilities Remain Invisible in the AI Age
items: Self-Regulating AI Agents: A Runtime Constitutional Framework for Autonomous Decision Systems in Cloud-Native Environments
items: Collaborative Learning and Teaching in Digital Environments: A Systematic Review of Two Decades of Research
items: Agentic AI Modernization: Transforming Institutional Infrastructure Through Orchestrated Multi-Agent LLM Framework
items: Deep Learning-Enabled Demand Intelligence: Transforming Forecast Accuracy in high velocity transaction systems
items: Strategic Framework for Enterprise Cybersecurity Management: Integrating Intelligent Anomaly Detection for Proactive Threat Mitigation
items: A Self-Systematic Review of Mobile Apps for Developing Multiple Language Skills in EFL
items: Autism Spectrum Disorder (ASD) Detection Using Multiple Deep Learning Models and Comparison of Model Performances
items: CRM Extensions for Retail Fuel Industry in USA Accounting: Efficiency Gains and ROI
items: Operational Monitoring for Enterprise Chatbots: Webex Teams–Based Alerting for NLU Drift, Fallbacks, and Service Health
items: Assessing The Development of an Automated Fan Control System
items: AI-Powered Sprint Planning & Backlog Management for Project Lifecycle Management
items: Comparing Vision Transformers and Convolutional Neural Networks: A Systematic Analysis
items: Streamlining DevOps Pipelines with AI-Augmented Feature Flagging for Microservices Architectures
items: CRM Extensions for Wholesale Jobbers in USA Operations: Efficiency Gains and ROI
items: Optimizing Extended Warehouse Management for Make-to-Order Plants: Slotting, Wave Picking, and Yard Orchestration at Scale
items: Engineering for Millions of Requests Per Second: Building Ultra-Low Latency, High-Availability Services at Scale
items: Cloud Identity Risk Management Framework for Regulated Enterprises: A Comprehensive Approach to Identity-Centric Security
items: Autonomous Remediation Pipelines with Human-in-the-Loop Oversight
items: An Attention-Enhanced Transformer Framework for Intelligent Energy Management and Load Forecasting in U.S. Power Grids
items: A Multi-Functional AI-Enabled Robotic Doctor for Intelligent Patient Monitoring and Autonomous Healthcare Support with Adaptive Human-Centered Interaction
items: Retrieval-Augmented Generation (RAG) Systems: Architectures, Strategies, and Evaluation
items: The Rise of DataOps Observability: AI-Driven Reliability for Modern Data Platforms

Ethical and Explainable AI Frameworks for Responsible Business Analytics in the U.S. Economy
type: item
description: 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 th…
subject: Research Article
  topic: section
subject: Ethical Artificial Intelligence, Explainable AI (XAI), Responsible Business Analytics, Consumer Finance Protection, Natural Language Processing and Financial Services Regulation
  topic: keywords
creator: Md Shahdat Hossain
creator: MD SHAHADAT HOSSAIN
creator: Mohammad Ali
creator: Md Whahidur Rahman
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2026-01-15
publication date: 2026-01-15
starting page: 74
ending page: 96
doi: 10.32996/jcsts.2025.8.1.6

An IoT-Based Remote Patient Monitoring Architecture Integrating Edge AI and Blockchain for Chronic Disease Management
type: item
description: 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.
subject: Research Article
  topic: section
subject:
  topic: keywords
  value: 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.
creator: Md Shakil Islam
creator: Md. Maniruzzaman
creator: Md Abul Kalam Azad
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0
date: 2025-11-30
publication date: 2025-11-30
starting page: 442
ending page: 451
doi: 10.32996/jcsts.2025.7.11.41

Cloud-Native Architectures in Financial Services: Enhancing Scalability and Security with AWS and Kubernetes
type: item
description: 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.
subject: Research Article
  topic: section
subject: Cloud-Native Architectures; Financial Services; Scalability and Security; AWS and Kubernetes
  topic: keywords
creator: Ashmitha Nagraj
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2023-11-20
publication date: 2023-11-20
starting page: 296
ending page: 308
doi: 10.32996/jcsts.2023.5.4.30

Prescriptive Analytics on Anonymized Patient Data Using Regression and Distributed Computing
type: item
description: 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.
subject: Research Article
  topic: section
subject: Predictive analytics; anonymized data; regression; distributed computing
  topic: keywords
creator: JAGADEESWAR ALAMPALLY
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2022-03-25
publication date: 2022-03-25
starting page: 107
ending page: 111
doi: 10.32996/jcsts.2022.4.1.13

Digital Service Factories: AI-Driven Lifecycle Service Orchestration Beyond Connectivity
type: item
description: 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.
subject: Research Article
  topic: section
subject: Digital Service Factory, Lifecycle Service Orchestration, AI-Driven Orchestration, Telecom Automation, Multi-Cloud, Policy-as-Code
  topic: keywords
creator: Amar Gurajapu
creator: Swapna Anumolu
creator: Vardhan Garimella
creator: Venkata Manikanta Sai Ramakrishna Chundi
creator: Venkata Sita Anand Prakash Gubbala
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2025-06-11
publication date: 2025-06-11
starting page: 1115
ending page: 1119
doi: 10.32996/jcsts.2025.7.6.133

Modernizing Mission-Critical Systems: A Hybrid-Cloud Transformation Roadmap
type: item
description: 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.
subject: Research Article
  topic: section
subject: Hybrid Cloud, Legacy Modernization, Microservices, Infrastructure as Code, DevOps Transformation, Fault Management, Cloud Computing, Transformation
  topic: keywords
creator: Amar Gurajapu
creator: Swapna Anumolu
creator: Vardhan Garimella
creator: Venkata Manikanta Sai Ramakrishna Chundi
creator: Venkata Sita Anand Prakash Gubbala
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2025-01-15
publication date: 2025-01-15
starting page: 425
ending page: 430
doi: 10.32996/jcsts.2025.7.1.32

Accelerating Delivery: A Unified Framework for Enterprise CI/CD Standardization
type: item
description: 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.
subject: Research Article
  topic: section
subject: DevOps Standardization, CI/CD Framework, Pipeline Templates, Deployment Automation, Enterprise Governance, Quality Metrics, DevOps Transformation, Cloud Computing, Transformation
  topic: keywords
creator: Amar Gurajapu
creator: Swapna Anumolu
creator: Vardhan Garimella
creator: Venkata Manikanta Sai Ramakrishna Chundi
creator: Venkata Sita Anand Prakash Gubbala
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2025-01-03
publication date: 2025-01-03
starting page: 420
ending page: 424
doi: 10.32996/jcsts.2025.7.1.31

The Recognition Gap: How Women’s Technical Abilities Remain Invisible in the AI Age
type: item
description: 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.
subject: Research Article
  topic: section
subject: Gender; technology; gatekeeping; women in STEM
  topic: keywords
creator: Kristy Rae Stewart
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2026-02-15
publication date: 2026-02-15
starting page: 71
ending page: 81
doi: 10.32996/jcsts.2026.8.4.6

Self-Regulating AI Agents: A Runtime Constitutional Framework for Autonomous Decision Systems in Cloud-Native Environments
type: item
description: 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.
subject: Research Article
  topic: section
subject: Autonomous AI agents, Runtime governance, Constitutional AI framework, Cloud-native decision systems, Human–AI collaboration
  topic: keywords
creator: Harvendra Singh
creator: Subba Rao Katragadda
rights: Copyright (c) 2026 Journal of Computer Science and Technology Studies
date: 2026-02-11
publication date: 2026-02-11
starting page: 41
ending page: 46
doi: 10.32996/jcsts.2026.8.4.3

Collaborative Learning and Teaching in Digital Environments: A Systematic Review of Two Decades of Research
type: item
description: 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 teach…
subject: Research Article
  topic: section
subject:
  topic: keywords
  value: Systematic Review (SR), Collaborative Learning, interaction, communication, Teaching and learning, Digital Environments, student-student, student-instructor, local collaboration, national collaboration, global collaboration
creator: Reima Al-Jarf
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-10
publication date: 2026-02-10
starting page: 25
ending page: 40
doi: 10.32996/jcsts.2026.8.4.2

Agentic AI Modernization: Transforming Institutional Infrastructure Through Orchestrated Multi-Agent LLM Framework
type: item
description: 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…
subject: Research Article
  topic: section
subject:
  topic: keywords
  value: 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
creator: Mahesh Kumar Damarched
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2026-02-09
publication date: 2026-02-09
starting page: 01
ending page: 24
doi: 10.32996/jcsts.2026.8.4.1

Deep Learning-Enabled Demand Intelligence: Transforming Forecast Accuracy in high velocity transaction systems
type: item
description: 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.
subject: Research Article
  topic: section
subject: Deep learning, High-velocity transaction systems, Real-time demand forecasting, Streaming analytics and online learning, Human-centric AI for operational decision-making
  topic: keywords
creator: Subba Rao Katragadda
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2026-02-09
publication date: 2026-02-09
starting page: 39
ending page: 45
doi: 10.32996/jcsts.2026.8.3.4

Strategic Framework for Enterprise Cybersecurity Management: Integrating Intelligent Anomaly Detection for Proactive Threat Mitigation
type: item
description: 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.
subject: Research Article
  topic: section
subject: Enterprise Cybersecurity
  topic: keywords
subject: Strategic Management
  topic: keywords
subject: Anomaly Detection
  topic: keywords
subject: Threat Mitigation
  topic: keywords
subject: Business Continuity
  topic: keywords
subject: Risk Management
  topic: keywords
creator: Akib Rahman
creator: Sharmin Sultana
creator: Rishalatun Jannat Lima
rights: Copyright (c) 2026 Journal of Computer Science and Technology Studies
date: 2026-02-15
publication date: 2026-02-15
starting page: 58
ending page: 70
doi: 10.32996/jcsts.2026.8.4.5

A Self-Systematic Review of Mobile Apps for Developing Multiple Language Skills in EFL
type: item
description: 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…
subject: Research Article
  topic: section
subject:
  topic: keywords
  value: 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
creator: Reima Al-Jarf
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-07
publication date: 2026-02-07
starting page: 14
ending page: 29
doi: 10.32996/jcsts.2026.8.3.2

Autism Spectrum Disorder (ASD) Detection Using Multiple Deep Learning Models and Comparison of Model Performances
type: item
description: 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.
subject: Research Article
  topic: section
subject: Autism Spectrum Disorder
  topic: keywords
subject: Classification
  topic: keywords
subject: Deep Learning
  topic: keywords
creator: Jannatul Mawah Tasmi
creator: Chitra Roy
rights: Copyright (c) 2026 Journal of Computer Science and Technology Studies
date: 2026-02-15
publication date: 2026-02-15
starting page: 47
ending page: 57
doi: 10.32996/jcsts.2026.8.4.4

CRM Extensions for Retail Fuel Industry in USA Accounting: Efficiency Gains and ROI
type: item
description: 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 …
subject: Research Article
  topic: section
subject: CRM automation
  topic: keywords
subject: wholesales-to-retail integration
  topic: keywords
subject: ROI
  topic: keywords
subject: month-end close
  topic: keywords
subject: accounting system
  topic: keywords
subject: financial backbone
  topic: keywords
creator: Shahebazkhan Pathan
rights: Copyright (c) 2026 Journal of Computer Science and Technology Studies
date: 2026-02-07
publication date: 2026-02-07
starting page: 30
ending page: 38
doi: 10.32996/jcsts.2026.8.3.3

Operational Monitoring for Enterprise Chatbots: Webex Teams–Based Alerting for NLU Drift, Fallbacks, and Service Health
type: item
description: 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.
subject: Research Article
  topic: section
subject: Conversational AI Monitoring; Enterprise Chatbots; NLU Drift Detection; Production Observability; Incident Response; MLOps; Webex Teams Alerting; Kibana Dashboards
  topic: keywords
creator: SUNIL KARTHIK KOTA
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0
date: 2022-02-25
publication date: 2022-02-25
starting page: 99
ending page: 106
doi: 10.32996/jcsts.2022.4.1.12

Assessing The Development of an Automated Fan Control System
type: item
description: 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.
subject: Research Article
  topic: section
subject: Automated Fan Control System, Smart Classroom, Environmental Control, Thermal Comfort, Technology Acceptance Model (TAM)
  topic: keywords
creator: Julie Ann Lopez
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2026-01-30
publication date: 2026-01-30
starting page: 27
ending page: 35
doi: 10.32996/jcsts.2026.8.2.4

AI-Powered Sprint Planning & Backlog Management for Project Lifecycle Management
type: item
description: 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.
subject: Research Article
  topic: section
subject: Artificial Intelligence, Agile, Scrum, Sprint Planning, Backlog Management, Project Lifecycle Management, Machine Learning, Natural Language Processing, Predictive Analytics, Capacity Planning.
  topic: keywords
creator: RaviKumar Bhuvanagiri
rights: Copyright (c) 2026 Journal of Computer Science and Technology Studies
date: 2026-02-18
publication date: 2026-02-18
starting page: 82
ending page: 89
doi: 10.32996/jcsts.2026.8.4.8

Comparing Vision Transformers and Convolutional Neural Networks: A Systematic Analysis
type: item
description: 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.
subject: Research Article
  topic: section
subject: Vision Transformers, Convolutional Neural Networks, Image Classification, Deep Learning Architectures, Adversarial Robustness, Transfer Learning
  topic: keywords
creator: Chandrasekar Adhithya Harsha Pasumarthi
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0
date: 2026-01-28
publication date: 2026-01-28
starting page: 19
ending page: 26
doi: 10.32996/jcsts.2026.8.2.3

Streamlining DevOps Pipelines with AI-Augmented Feature Flagging for Microservices Architectures
type: item
description: 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 capab…
subject: Research Article
  topic: section
subject: artificial intelligence in software deployment, microservices architecture management, predictive deployment analytics, automated feature rollout optimization, DevOps pipeline automation
  topic: keywords
creator: Ravi Babu Dasari
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0
date: 2026-01-28
publication date: 2026-01-28
starting page: 10
ending page: 18
doi: 10.32996/jcsts.2026.8.2.2

CRM Extensions for Wholesale Jobbers in USA Operations: Efficiency Gains and ROI
type: item
description: In the competitive downstream fuel industry, the adoption of Customer Relationship Management (CRM) systems tailored for wholesale fuel operations is still nascent. The wholesale fuel industry operates in a highly volatile market, where pricing dynamics resemble the stock market, and larger volume commitments secure more favorable rates. Operationally, multiple teams are involved in moving fuel from terminal racks to retail sites, including suppliers, wholesale jobbers, dispatch, pricing, carriers, accounting, and data analysis teams. Effective communication among these diverse stakeholders is critical, yet industry is often constrained by fragmented exchanges through emails, texts, and phone calls, leading to inefficiencies and miscommunication. After fuel delivery, accounting teams manually process invoices and reconcile supplier costs, while data analysis teams transform transactional data into reports and visual insights for strategic decision-making. However, the lack of a unified platform creates delays, redundant work, and data inconsistencies across departments. This paper proposes a role-based Customer Relationship Management (CRM) system designed for wholesale fuel operations, enabling cross-functional teams to collaborate seamlessly within a database. By integrating modules tailored to each team’s operational role, the system minimizes back-and-forth communication, reduces data loss, enhances overall efficiency and lead to a favorable Return of Investment (ROI). The study underscores how a centralized CRM framework can streamline communication, strengthen data integrity, and empower stakeholders with timely insights to drive informed business decisions.
subject: Research Article
  topic: section
subject: CRM System
  topic: keywords
subject: wholesale fuel
  topic: keywords
subject: ROI
  topic: keywords
subject: operational efficiency
  topic: keywords
subject: communication efficiency
  topic: keywords
subject: role-based system
  topic: keywords
subject: database
  topic: keywords
creator: Shahebazkhan Pathan
rights: Copyright (c) 2026 Journal of Computer Science and Technology Studies
date: 2026-02-01
publication date: 2026-02-01
starting page: 01
ending page: 13
doi: 10.32996/jcsts.2026.8.3.1

Optimizing Extended Warehouse Management for Make-to-Order Plants: Slotting, Wave Picking, and Yard Orchestration at Scale
type: item
description: The make-to-order (MTO) production setting introduces high levels of operational complexity to the warehouse execution because of the high variability of products, variable demand trends, and high levels of synchronization with production and outbound logistics. The conventional management techniques in the warehouse, based on unchangeable slotting policies, fixed wave formations, and separated yard activities, cannot normally be scaleable to these circumstances. The given paper offers a combined optimization model of Extended Warehouse Management (EWM) in large-scale MTO plants, and the central issues to be considered are correlated ameliorations of slotting approach, wave picking, and yard coordination. The offered solution is a hybrid of demand-based SKU profiling, dynamically-based slotting heuristics, workload-based wave generation, and event-based yard and dock coordination, which allows real-time flexibility in warehouse and yard activities. The concept of an integrated architectural framework is presented to bring EWM and upstream production planning and downstream transportation system at par with one another so that there is consistency in the end-to-end execution. The validation of the framework is made by the industrial scale case study that proves the quantifiable reduction of order cycle time, picking productivity, dock turnaround time, and the overall warehouse throughput compared to the baseline settings. Findings suggest that co-optimisation of storage, picking and yard operations is fundamental to attain scalability, resilience and performance efficiency of MTO manufacturing settings, where advanced EWM has the potential to be deployed as a strategic enabler to responsive and high-performance execution of supply chain operations.
subject: Research Article
  topic: section
subject: Extended Warehouse Management (EWM), Make-to-Order Manufacturing, Slotting Optimization, Wave Picking, Yard Management, Warehouse Automation, ERP Systems, Make-To-Order (MTO)
  topic: keywords
creator: Ramesh Babu Potla
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2024-08-25
publication date: 2024-08-25
starting page: 181
ending page: 192
doi: 10.32996/jcsts.2024.6.3.15

Engineering for Millions of Requests Per Second: Building Ultra-Low Latency, High-Availability Services at Scale
type: item
description: The growth of digital services has intensified the need for distributed systems that can sustain millions of requests per second while maintaining ultra low latency and continuous availability. Engineering such workloads requires coordinated decisions about programming language runtimes, serialization formats, network topology, caching, and fault tolerance. This paper proposes a design framework for ultra low latency, high availability microservice-based services, grounded in published empirical studies and documented industrial systems operating at high throughput. The framework is based on the evidence on the latency impact of binary serialization and language runtimes such as Rust and Java, network hop minimization and cellular architectures for failure isolation, multi-tier caching and precomputation, and adaptive resilience mechanisms including token bucket based retry budgets, circuit breakers, and additive increase multiplicative decrease control mechanisms. Rather than reporting new experiments, the paper synthesizes findings from existing empirical evidence and organizes these findings into a layered set of design dimensions and best practice guidelines intended to support predictable tail latency, high availability, and cost-aware operation in large-scale cloud environments.
subject: Research Article
  topic: section
subject: Ultra low latency, tail latency, microservices, distributed systems, high availability, cloud computing, caching, cellular architecture, adaptive resilience, Rust, Java
  topic: keywords
creator: Naveen Kumar Jayakumar
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2026-01-13
publication date: 2026-01-13
starting page: 60
ending page: 73
doi: 10.32996/jcsts.2025.8.1.5

Cloud Identity Risk Management Framework for Regulated Enterprises: A Comprehensive Approach to Identity-Centric Security
type: item
description: The digital transformation of regulated enterprises has fundamentally altered cybersecurity landscapes, positioning identity management as the cornerstone of modern security architectures. Traditional network perimeter-based security models prove inadequate for addressing hybrid cloud complexities where organizational boundaries become increasingly fluid and dynamic. The Cloud Identity Risk Management Framework addresses these challenges through a sophisticated five-layer architecture encompassing discovery, classification, control, detection, and response capabilities. The framework integrates comprehensive privileged access management, segregation of duties enforcement, and graph-based analytics to provide unified approaches to identity governance across multiple regulatory environments, including HIPAA, PCI DSS, GDPR, and SOX requirements. Implementation methodology employs phased deployment strategies incorporating change management protocols, continuous monitoring capabilities, and identity-driven incident response procedures. Performance optimization demonstrates substantial improvements in access violation reduction, audit readiness enhancement, and security incident containment effectiveness. The framework transforms identity management from reactive compliance activities into proactive strategic enablers supporting business objectives while maintaining rigorous security standards. Advanced behavioral analytics and machine learning capabilities enable predictive risk assessment that anticipates potential security issues before manifestation as actual incidents. Integration with existing security operations centers ensures seamless coordination between identity governance and broader organizational security programs.
subject: Research Article
  topic: section
subject: Cloud Identity Management, Risk Assessment Framework, Regulatory Compliance, Privileged Access Control, Identity Governance
  topic: keywords
creator: Naga Yeswanth Reddy Guntaka
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2026-01-13
publication date: 2026-01-13
starting page: 49
ending page: 59
doi: 10.32996/jcsts.2025.8.1.4

Autonomous Remediation Pipelines with Human-in-the-Loop Oversight
type: item
description: Enterprise data platforms are growing in incident volume at exponential rates as infrastructure complexity grows, while operational team resources are limited by finite human resources. The rise of microservices-based cloud-native architectures has multiplied this challenge with the generation of alert rates that, coming from distributed systems, can overwhelm traditional mechanisms for manual responses. By themselves, fully autonomous remediation systems provide rapid response capabilities, but when not contextually aware, they present a massive risk of creating more failures than they resolve. Human-in-the-loop remediation architectures are the best solution, as they combine both machine learning abilities and human judgment to establish systems that are machine-level fast yet human-level sensitive. These hybrid frameworks include smart observability layers that leverage ensemble anomaly detection algorithms and root cause investigation engines (inclusive of telemetry, log, and trace assessment that strives to isolate failures and create prioritized remediation recommendations). Rather than the execution of actions autonomously, the systems offer recommendations via embedded integrated approval workflows integrated within existing operational tools to allow the operator to validate proposals with full contextual enrichment, including business impact assessment, deployment status, and historical precedents. Implementation in the container orchestration platforms is this: declarations, configuration, and API for program management to take quick, traceable actions, and average remediation. Operator feedback and post-action affirmation mechanisms are continuous learning processes that enhance the accuracy of the recommendations as they progress over time. Robust governance frameworks offer risk-based approval levels, complete audit traces, and role-based access controls for responsible automation, striking a balance between operational efficiency and safety requiremen…
subject: Research Article
  topic: section
subject: Human-In-The-Loop Systems, Autonomous Remediation, Anomaly Detection, Container Orchestration, Governance Frameworks
  topic: keywords
creator: Anshul Verma
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2026-01-13
publication date: 2026-01-13
starting page: 40
ending page: 48
doi: 10.32996/jcsts.2025.8.1.3

An Attention-Enhanced Transformer Framework for Intelligent Energy Management and Load Forecasting in U.S. Power Grids
type: item
description: Accurate short-term and multi-horizon electricity load forecasting is a fundamental requirement for intelligent energy management in modern power grid systems, particularly under increasing demand variability and weather-driven consumption patterns. Conventional statistical, machine learning, and recurrent neural network models often exhibit limited capability in modelling complex non-linear relationships and long-range temporal dependencies inherent in large-scale power system data. To address these challenges, this paper proposes an Attention-Enhanced Transformer-based Multi-Horizon Weather-aware Network (ATMH-WNet) for efficient and accurate load forecasting in U.S. power grids. The proposed framework employs linear feature embedding and sinusoidal positional encoding to construct temporally informed latent representations, which are processed through a multi-layer Transformer encoder with multi-head self-attention. This design enables the model to jointly capture short-term dynamics and long-range dependencies while producing direct multi-step forecasts in a single forward pass, thereby avoiding recursive error accumulation. The proposed model is evaluated on the PJM Interconnection hourly electricity consumption dataset spanning 2002-2018 and is compared against persistence, SARIMA, Prophet, XGBoost, and LSTM benchmarks. Experimental results demonstrate that ATMH-WNet consistently outperforms all baseline models, achieving a mean absolute error of 1325 MW, a root mean squared error of 1873 MW, a mean absolute percentage error of 2.9%, and an ????2 score of 0.97 on the held-out test set. Compared to the strongest deep learning baseline, the proposed framework reduces forecasting errors by more than 20% across major accuracy metrics. Additional qualitative analyses, including load profile alignment, residual diagnostics, and error distribution assessment, further confirm the robustness, stability, and generalization capability of the proposed approach. These resu…
subject: Research Article
  topic: section
subject: Persistence model, SARIMA, XGBoost, LSTM, Prophet, Time series forecasting, Baseline models, Forecasting benchmarks, Model evaluation, Predictive analytics
  topic: keywords
creator: Rayhanul Islam Sony
creator: K M Shihab Hossain
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0
date: 2026-01-09
publication date: 2026-01-09
starting page: 01
ending page: 30
doi: 10.32996/jcsts.2025.8.1.1

A Multi-Functional AI-Enabled Robotic Doctor for Intelligent Patient Monitoring and Autonomous Healthcare Support with Adaptive Human-Centered Interaction
type: item
description: The increasing demand for contactless and uninterrupted healthcare has accelerated the need for intelligent robotic systems capable of supporting clinical tasks with minimal human exposure. This research presents a complete design and architectural framework for an AI-Enabled Robotic Doctor developed using Tinkercad, integrating vital-sign monitoring, autonomous navigation, room detection, voice interaction, AI-driven priority assessment, medication assistance, and remote doctor communication into a unified operational model. The methodology outlines structured healthcare requirement analysis, functional decomposition, workflow modeling, and conceptual system validation, demonstrating how the robot can interpret vital data, navigate safely using AI-based visual recognition and obstacle avoidance, communicate naturally with patients, and support medication management. The system architecture defines six coordinated layers responsible for monitoring, decision-making, interaction, assistance execution, and documentation. Conceptual evaluation shows that the proposed model can enhance patient monitoring efficiency, reduce clinical workload, and improve safety in high-risk medical environments. The 3D structural design validated ergonomic feasibility and modular organization. Future work includes software simulation of navigation and interaction behaviours. Followed by hardware realization with biomedical sensors and embedded controllers to transition this design into a fully operational autonomous healthcare robot. The study establishes a comprehensive foundation for advancing robotic healthcare support systems in modern clinical settings.
subject: Research Article
  topic: section
subject:
  topic: keywords
  value: AI-enabled healthcare robot, robotic doctor, contactless healthcare delivery, patient interaction system, healthcare workflow automation, AI-based priority assessment, human–robot interaction, medical service robot architecture, autonomous healthcare support, intelligent patient monitoring
creator: Ekramul Hasan
creator: Dipta Paul
creator: Ahmed Tamim Zabir Prince
creator: Ibrahim Ibne Moksud
creator: Lam Ya Islam
creator: Md Asif Mustafa Pial
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2022-09-25
publication date: 2022-09-25
starting page: 200
ending page: 214
doi: 10.32996/jcsts.2022.4.2.24

Retrieval-Augmented Generation (RAG) Systems: Architectures, Strategies, and Evaluation
type: item
description: Retrieval-Augmented Generation (RAG) systems have revolutionized the way large language models (LLMs) synthesize responses by coupling generative capabilities with dynamic retrieval from external knowledge bases. This integration not only enhances the factual accuracy and contextual relevance of responses but also reduces the potential for hallucination in generated content. In this paper, we present an extensive survey of RAG systems, covering theoretical underpinnings, various retrieval strategies, agentic architectures, and the technical developer stack necessary for system integration. Additionally, we detail advanced techniques for fine-tuning embedding and reranker models and establish comprehensive evaluation metrics applicable to both retrieval and generation components. This document synthesizes methods and best practices described in recent research articles [1]–[10], offering a roadmap for researchers and practitioners to design robust, efficient, and scalable RAG systems.
subject: Research Article
  topic: section
subject: Retrieval-Augmented Generation, RAG, Embedding Models, Reranker Fine-Tuning, Semantic Retrieval, Agentic Architectures, Evaluation Metrics, Vector Databases, Query Routing, Dynamic Context
  topic: keywords
creator: Sunil Karthik Kota
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0
date: 2024-12-25
publication date: 2024-12-25
starting page: 350
ending page: 358
doi: 10.32996/jcsts.2024.6.5.30

The Rise of DataOps Observability: AI-Driven Reliability for Modern Data Platforms
type: item
description: Modern data settings have grown past fixed pipeline designs into complicated, spread-out structures covering hybrid and multi-cloud computing setups. Standard monitoring tools cannot keep up with the speed, variety, and amount marking today's data flow patterns today. DataOps observability, powered by Generative AI and Machine Learning methods, shows a basic shift from inactive watching toward active reliability handling. AI-powered observability systems now go past simple dashboard work, examining measurement information, tracking origins throughout changing data flows, and spotting oddities before they spread into production breakdowns. Generative structures automatically draw connections between datasets, figure out transformation reasoning, and propose fixing steps with situational knowledge. For data reliability specialists, this change builds an intelligence level that constantly learns system actions, lowers wrong warnings, and speeds up finding problem causes. Using forecasting tools, AI expects data changes, format mismatches, and delay rises, turning incident answers into incident stopping. AI-boosted DataOps observability gives a foundation for self-fixing pipelines and independent control systems. This growth moves from reactive fixing toward active reliability ways, where each step of the data life process gets better through flexible smarts. Companies using these setups reach working stability while cutting manual work needs throughout data infrastructure tasks.
subject: Research Article
  topic: section
subject: DataOps Observability, AI-Driven Reliability, Generative AI, Machine Learning, Data Pipelines, Anomaly Detection, Predictive Analytics, Self-Healing Systems, Data Governance
  topic: keywords
creator: Dillepkumar Pentyala
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-29
publication date: 2025-12-29
starting page: 541
ending page: 546
doi: 10.32996/jcsts.2025.7.12.60