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: Comparing Vision Transformers and Convolutional Neural Networks: A Systematic Analysis
items: Streamlining DevOps Pipelines with AI-Augmented Feature Flagging for Microservices Architectures
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
items: MuleSoft Architectural Paradigms and Sustainability: A Comprehensive Technical Analysis
items: Autonomously Transacting Agents: A New Paradigm for AI in Finance
items: Software Engineering Practices: In the era of AI / LLMs
items: A Comparative Performance & Metadata Study of Open Table Formats: Iceberg vs Delta vs Hudi at Scale
items: Transitioning to Open-Source Observability: A Cost-Benefit Analysis and Migration Framework
items: AI-Driven Big Data Analytics for Precision Medicine: A Unified Framework Integrating Molecular Data Intelligence, Wearable Health Systems, and Predictive Modeling
items: Towards a Futuristic Security Roadmap: Advanced Strategies
items: Calligraphic Text Recognition by Gemini, Ernie ViLG and Google Translate: A Comparative Study of Arabic, Japanese and Chinese
items: Integrating Blockchain and Data Analytics to Strengthen Financial Traceability and Anti-Fraud Controls
items: Securing Industrial Water Networks: Event-Level Anomaly Detection in SCADA Telemetry
items: The AI- Powered Treasury: A Data- Driven Approach to managing America’s Fiscal Future
items: AI-Driven Big Data Analytics for Personalized Cancer Treatment: Integrating Multi-Omics, Medical Imaging, and Predictive Intelligence
items: Google Translate Then and Now: Translations From Five Languages into English and Arabic (2012–2025)
items: Security Architecture Program Governance at Scale: A Technical Framework
items: Ethical and Privacy Implications of Machine Learning in Android Development
items: Architecting Resilient Data Pipelines: Best Practices for Scalable ETL in Multi-Cloud Environments
items: Agentic AI Frameworks: Building Autonomous, Self-Healing Systems for Financial Infrastructure
items: Airlines Flight Baggage Handling using Predictive Analytics
items: Settlement, Fees, and Interchange: Data Models for Accurate Reconciliation and Exception Handling
items: Advanced SHA256-AES Hybrid Algorithm for Enhanced Data Confidentiality and Integrity in Cloud Data Security

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

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

MuleSoft Architectural Paradigms and Sustainability: A Comprehensive Technical Analysis
type: item
description: Modern enterprise computing environments require integration architectures to meet both business and sustainability goals. MuleSoft's vision for API-led connectivity achieves these goals through a three-level integration architecture: System APIs, Process APIs, and Experience APIs aligned with the API-led connectivity architecture process model. The layering supports the principles of re-use, loose coupling, and modularization, as well as preventing duplicate processing in the enterprise integration portfolio. Event-driven architectural and connector-based microservices integration support asynchronous communication and independent scalability, and improve the resilience of the solution, compared to synchronous integration. In more complex scenarios, streaming data transformations, connection pooling, batched processing, and response caching can save CPU, memory, and network bandwidth, and coupling these with an event-based model (as opposed to periodic polling) avoids performing business logic for new events when there are none, thus saving computational resources. Cloud-native deployment models with container-based orchestration can achieve economies of scale through higher resource utilization, and, eventually, access to more renewable resources, as seen with hyperscale cloud providers. Through API reusability and consolidation of infrastructure, entire areas of duplicate functionality across enterprise portfolios can be avoided. Specification-first, performance testing and capacity management, monitoring, lifecycle management, and good practices ensure that APIs and architectures can perform well and with low levels of technical debt while supporting the sustainability goals of the organization.
subject: Research Article
  topic: section
subject: API-Led Connectivity, Event-Driven Architecture, Sustainable Integration, Microservices Orchestration, Green Computing Practices
  topic: keywords
creator: Venkata Pavan Kumar Gummadi
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: 534
ending page: 540
doi: 10.32996/jcsts.2025.7.12.59

Autonomously Transacting Agents: A New Paradigm for AI in Finance
type: item
description: Autonomous financial agents, powered by the convergence of artificial intelligence and blockchain technology, represent a paradigm shift in decentralized finance. These self-operating entities now possess capabilities to hold cryptocurrency wallets, execute complex transactions, and even launch tokens without human oversight. The architectural framework supporting these agents integrates specialized language models, secure wallet management systems, and persistent on-chain identities. From market-making to yield optimization, these agents demonstrate remarkable efficacy across various financial operations, creating novel market dynamics when interacting with both human participants and other autonomous systems. Essential to mainstream adoption are sophisticated reputation frameworks combining algorithmic assessment with social consensus mechanisms. However, significant challenges exist, including market manipulation vulnerabilities, spam production, and regulatory complexity. As these autonomous agents continue evolving, appropriate governance models tailored to agent characteristics become critical for balancing innovation with market integrity in this emerging financial landscape.
subject: Research Article
  topic: section
subject: Autonomous Financial Agents, Blockchain Technology, Decentralized Finance, Reputation Systems, Agent Governance
  topic: keywords
creator: Utkarsh Sinha
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: 526
ending page: 533
doi: 10.32996/jcsts.2025.7.12.58

Software Engineering Practices: In the era of AI / LLMs
type: item
description: The software development landscape is going through a major shift as generative artificial intelligence (AI) tools, including code assistants and autonomous agents, become increasingly widespread. Recent industry surveys indicate that more than 75% of developers currently use or plan to adopt AI-based solutions, with approximately half of professional developers utilizing these tools daily. Furthermore, organizational adoption is nearly universal, with reports suggesting that over 97% of companies incorporate AI into their development workflows for tasks such as code generation, documentation, code review, and automated testing. The anticipated benefits of these technologies are considerable. Empirical studies report productivity gains of up to 50% in coding speed and 33% in code refactoring efficiency. However, these advantages are accompanied by notable risks. AI-generated code can propagate security vulnerabilities and exacerbate technical debt. Moreover, evidence from a 2025 randomized controlled trial (RCT) shows that experienced developers using AI assistants were, on average, 19% slower when completing real-world programming tasks, challenging the assumption of universal efficiency gains. This paper discusses the current state of generative AI adoption in software engineering and synthesizes best practices for employing its potential while controlling associated risks. Following the structure recommended by the Journal of Computer Science and Technology Studies, the discussion is organized into the following sections: Introduction, Literature Review, Methodology, Results and Findings, Conclusion, Statements and Declarations, and References [11].
subject: Research Article
  topic: section
subject: Generative AI; Software Development; AI Augmented SDLC; Best Practices; Developer Productivity; AI Ethics
  topic: keywords
creator: Ashif Anwar
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: 521
ending page: 525
doi: 10.32996/jcsts.2025.7.12.57

A Comparative Performance & Metadata Study of Open Table Formats: Iceberg vs Delta vs Hudi at Scale
type: item
description: The rapid adoption of open table formats has fundamentally transformed modern data engineering by enabling ACID transactions, schema evolution, and time travel capabilities on cloud object storage systems. Apache Iceberg, Delta Lake, and Apache Hudi represent the three dominant solutions that have emerged to address traditional data lake limitations, including a lack of transactional guarantees, concurrent write challenges, and metadata management inefficiencies. This evaluation conducts empirical benchmarking across terabyte-scale datasets to compare these formats across critical dimensions, including metadata scalability, transaction isolation guarantees, concurrent write handling, compaction strategies, streaming consistency semantics, and cross-engine interoperability. Testing scenarios encompass bulk ingestion throughput, incremental write latency, selective query performance, time travel operations, schema evolution capabilities, and maintenance overhead under varying concurrency levels. Results reveal that Iceberg excels in read-heavy analytics workloads with superior query planning efficiency and cross-engine portability, Delta Lake demonstrates operational simplicity with strong Spark integration and the highest bulk write throughput, while Hudi offers flexible write-read tradeoffs through dual table types optimized for streaming upserts. Format convergence trends indicate rapid feature adoption across competing implementations, reducing vendor lock-in risks and enabling organizations to select formats based on specific workload characteristics rather than seeking universally optimal solutions. The article establishes quantitative foundations for practitioners navigating table format selection as lakehouse architectures become the dominant paradigm for enterprise data platforms, with direct implications for infrastructure costs, operational complexity, and analytical performance at scale.
subject: Research Article
  topic: section
subject: Lakehouse Architecture, Table Format Comparison, Metadata Management, Transaction Isolation, Distributed Data Systems
  topic: keywords
creator: Siddhartha Parimi
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2025-12-29
publication date: 2025-12-29
starting page: 513
ending page: 520
doi: 10.32996/jcsts.2025.7.12.56

Transitioning to Open-Source Observability: A Cost-Benefit Analysis and Migration Framework
type: item
description: Commercial observability platforms cost enterprises $1.5–2.5 million annually at 1TB daily telemetry, motivating evaluation of open-source alternatives. This paper assesses migrating to an open-source stack—Prometheus (metrics), Loki (logs), and Jaeger (traces)—through a case study of an 800GB/day cloud-native deployment. The migration achieved 68% cost reduction ($1.26M first-year savings) with 14–24% query latency increase. Break-even analysis places the cost-advantage threshold at 50–100GB daily ingestion. We present a nine-month phased migration framework covering infrastructure sizing, query translation, and parallel validation. Critical success factors include structured logging practices, dedicated platform engineering resources, and comprehensive training (45 hours per engineer). The framework suits Kubernetes-native organizations with mature DevOps capabilities. Limitations: Findings derive from a single organization with favorable preconditions; cost data is self-reported; alternative technology stacks are not evaluated. Organizations should assess applicability to their specific contexts.
subject: Research Article
  topic: section
subject: Observability, Open-source, Prometheus, Loki, Distributed Tracing, Cloud-native, Kubernetes, Cost Optimization
  topic: keywords
creator: Satyanarayana Gudimetla
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0
date: 2025-12-29
publication date: 2025-12-29
starting page: 495
ending page: 512
doi: 10.32996/jcsts.2025.7.12.55

AI-Driven Big Data Analytics for Precision Medicine: A Unified Framework Integrating Molecular Data Intelligence, Wearable Health Systems, and Predictive Modeling
type: item
description: The rapid digitization of healthcare and the massive growth of high-dimensional biomedical data have shed light on fundamental limitations regarding conventional medical decision-making, which is population-based. Precision medicine aims to overcome such limitations by incorporating biological, clinical, and behavioral data on individual levels with the purpose of achieving personalized diagnosis, treatment, and prevention. Artificial intelligence (AI) and big data analytics are the key enablers in the operationalization of precision medicine, including enabling a scalable interrogation of heterogeneous data sources - including multi-omics profiles and electronic health records, medical imaging and wearable sensor data. This investigation provides the overarching, data-centric synthesis of AI-based big data analytics in precision medicine, bringing into the spotlight integrated analytical architectures on the basis of predictive, preventive, and personalized delivery of healthcare. Drawing on a structured analytical evaluation methodology, the investigation draws together evidence from higher levels of healthcare AI scholarship to assess the efficacy of multiple data modalities integration, predictive modelling, and governance-aware systems design. The results have shown that the integrated AI frameworks greatly improve disease risk stratification, real-time health monitoring and clinical decision management compared to siloed analytic methodology. The research adds a coherent conceptual framework addressing the topics of scalability, interpretability, privacy, and ethical governance in order to provide reachable information to practice and implement trustworthy AI systems concordant with global objectives for healthcare and sustainability.
subject: Research Article
  topic: section
subject: Precision medicine; artificial intelligence; big data analytics; multi-omics integration; wearable health systems; predictive modeling; federated learning
  topic: keywords
creator: Nur Vanu
creator: Md Rezaul Hasan
creator: Tawfiqur Rahman Sikder
creator: Zakia Sultana Tamanna4
rights: Copyright (c) 2021 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2021-12-25
publication date: 2021-12-25
starting page: 124
ending page: 141
doi: 10.32996/jcsts.2021.3.2.11

Towards a Futuristic Security Roadmap: Advanced Strategies
type: item
description: As cloud-native technologies proliferate, I believe that the attack surface spanning Kubernetes clusters, virtual machines (VMs), and diverse cloud workloads continues to expand. Traditional security approaches are often insufficient to address the dynamic, distributed, and automated nature of modern infrastructures. This paper presents a forward-looking security roadmap, integrating emerging technologies and proactive frameworks to secure cloud environments—including applications, databases, devices, and operating systems—against both current and anticipated threats. The roadmap emphasizes automation, zero trust, AI-driven threat detection, and continuous compliance as pillars for the next generation of cloud security.
subject: Research Article
  topic: section
subject: Cloud Computing, Kubernetes, Artificial-Intellignce, Machine Learning, Scanning, Patching, Continuous Integration, Continuous Deployment, Data Protection, Identity and Access Management.
  topic: keywords
creator: Amar Gurajapu
rights: Copyright (c) 2026 Journal of Computer Science and Technology Studies
date: 2026-01-09
publication date: 2026-01-09
starting page: 31
ending page: 39
doi: 10.32996/jcsts.2025.8.1.2

Calligraphic Text Recognition by Gemini, Ernie ViLG and Google Translate: A Comparative Study of Arabic, Japanese and Chinese
type: item
description: This study investigates the ability of Gemini, Ernie ViLG, and Google Translate (GT) to recognize Arabic, Japanese, and Chinese calligraphic text images. Analysis of 15 Arabic, 7 Japanese, and 10 Chinese calligraphic samples shows that Gemini successfully matched 12/15 Arabic calligraphic texts with their correct Qur’anic verses, produced accurate translations for all Japanese samples, and correctly interpreted 9/10 Chinese texts. Ernie ViLG generated incorrect or random Qur’anic matches for Arabic, correctly translated 4/7 Japanese and 3/10 Chinese samples, and frequently defaulted to culturally common themes when unable to decode strokes. GT failed to produce any translations for Arabic calligraphy and rendered partial, fragmented, or incoherent translations for Japanese and Chinese images. Across Arabic, Japanese or Chinese calligraphic texts, the three AI models exhibited distinct strengths and weaknesses due to their differing approaches to calligraphic text recognition. Gemini proved to be the most reliable, leveraging multilingual training, pattern matching, and semantic retrieval to associate stylized characters with canonical works, enabling coherent and culturally grounded interpretations. On the contrary, Ernie ViLG struggled with literal recognition, especially in Arabic, and relied heavily on cultural priors when visual cues were ambiguous. GT was the weakest, as its OCR pipeline is optimized for printed or clean handwritten text and breaks down when confronted with stylized or artistically distorted calligraphy. Modern AI models process calligraphic text through a combination of visual feature extraction and linguistic prediction. Contemporary neural architectures employ convolutional and transformer-based encoders to interpret strokes, curves, and spatial patterns holistically, allowing them to infer characters even when calligraphy departs from standard rules of spacing, baseline alignment, or shape consistency. Arabic, Chinese, and Japanese calligra…
subject: Research Article
  topic: section
subject: Calligraphic texts, Arabic calligraphy, Japanese calligraphy, Chinese calligraphy, Gemini, ERNIE-ViLG, Google Translate (GT), OCR (optical character recognition), multimodal AI models, AI models
  topic: keywords
creator: Reima Al-Jarf
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2025-12-29
publication date: 2025-12-29
starting page: 474
ending page: 494
doi: 10.32996/jcsts.2025.7.12.54

Integrating Blockchain and Data Analytics to Strengthen Financial Traceability and Anti-Fraud Controls
type: item
description: This study presents and assesses an Integrated Blockchain Analytics Framework that uses blockchain data to identify illegal activity related to ransomware attacks by combining on-chain transactional data with off-chain regulatory and institutional knowledge. Blockchain-based ledgers provide a transparent view of each individual transaction; however, due to the pseudonymous nature of most users of cryptocurrency, along with the fact that much of this information may be fragmented between the blockchain and other institutions' databases, makes it difficult to attribute and enforce Anti-Money Laundering (AML) requirements. In this research, we propose an integrated framework for analyzing blockchain data using a combination of graph-based modeling of transactions, data enrichment and explainable machine learning techniques to enhance the traceability and compliance analysis of financial activity. The proposed framework includes a structured pipeline for preprocessing and feature engineering of the data, as well as, an interpretable risk score for the purpose of supporting both the regulatory review process and the workflow of investigators. The results also demonstrate the need to combine explainable analytics with blockchain forensic techniques to increase transparency, reproducibility and usability by regulators. Utilizing a publicly available labeled dataset of Bitcoin transactions from ransomware attacks, the approach shows significant increases in the completeness of the traceability, reductions in the time required to detect suspicious activity and efficiencies in the analysis of large volumes of data when comparing our approach to traditional rule-based approaches used for monitoring. Overall, the results indicate that a hybrid, explainable, blockchain analytic technique could significantly improve the effectiveness of AML and help meet U.S. government policy objectives concerning the risks associated with the use of digital assets and the integrity of the U.S. …
subject: Research Article
  topic: section
subject: Blockchain Analytics, Financial Traceability, Ransomware Detection, Anti-Money Laundering, Machine Learning Risk Scoring, U.S. Financial Compliance
  topic: keywords
creator: Md Shoriful Islam Chowdhury
creator: Kaniz Sultana Chy
creator: Md Ashiqul Islam
creator: Md Nurul Islam Chowdhury
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2023-08-09
publication date: 2023-08-09
starting page: 189
ending page: 202
doi: 10.32996/jcsts.2023.5.3.14

Securing Industrial Water Networks: Event-Level Anomaly Detection in SCADA Telemetry
type: item
description: The increased interdependence between industrial control systems and digital infrastructures has made water treatment facilities more suscepti-ble to cyber-physical attack, which has resulted in a need to have robust and smart anomaly detection systems. In this study, MRTAF-Net (Multi-Resolution Temporal-Attention Fusion Network) is a modern hybrid deep-learning framework aimed at facilitating the safe and de-pendable functionality of industrial water networks due to the detection of anomalies at the event levels of SCADA telemetry. The proposed model uses a Multi-Resolution Temporal Convolutional Network (MR-TCN) to extract hierarchical temporal features, a Channel Squeeze-Excitation (SE) to induce changes in channel weights in adaptive mode, and a Multi-Head Self-Attention (MHSA) to identify long-range tem-poral features and contextual sensor interactions. The proposed model is based on the SWaT data of the iTrust laboratory. In order to further em-power the model to jointly leverage the dynamic temporal information and the static statistical information, some statistical descriptors are in-tegrated with these sequential features by means of a learnable gated fu-sion module. Numerous experimental assessments show that MRTAF-Net has a very high level of performance with 99.83 accuracy, 99.84 F1-score and an AUC of 99.99, which is significantly better than popu-lar baseline models (Bi-LSTM, XGBoost, CatBoost and TabNet). MRTAF-Net has created an effective and scalable base to protect indus-trial water infrastructure against changing cyber-physical attacks through a combination of interpretability, robustness and multi-scale time rea-soning.
subject: Research Article
  topic: section
subject: Industrial water networks; SCADA telemetry; Anomaly detection; Hybrid deep learning; Multi-Resolution Temporal Convolutional Network (MR-TCN); Attention mechanisms; Cyber–physical security.
  topic: keywords
subject: Industrial Control Systems (ICS)
  topic: keywords
subject: Industrial Network
  topic: keywords
subject: SCADA
  topic: keywords
subject: Anomaly detection
  topic: keywords
subject: Hybrid Deep Learning
  topic: keywords
subject: Multi resolution Temporal Convolutional Network
  topic: keywords
subject: Attention Mechanism
  topic: keywords
subject: Cyber-Phycial Security
  topic: keywords
creator: Rayhanul Islam Sony
rights: Copyright (c) 2025 Journal of Computer Science and Technology Studies
date: 2025-12-22
publication date: 2025-12-22
starting page: 428
ending page: 452
doi: 10.32996/jcsts.2025.7.12.51

The AI- Powered Treasury: A Data- Driven Approach to managing America’s Fiscal Future
type: item
description: The growth in the United States national debt has become a major issue leading to concern regarding the long-term fiscal sustainability and economic stability of the economy that will reach over 33 trillion in 2023. The traditional methods of managing debt, which are mostly based on the use of fixed econometric models and manual forecasts, may not be up to the dynamic nature of the modern financial markets, changes in the behavior of the taxpayers, and the real-time changes in the government revenue streams. This study examines how fiscal management can be transformed by the use of artificial intelligence (AI) and data analytics to improve forecasting and Treasury bond auction strategies and minimize the cost of borrowing. In the study, the U.S. Government Revenue Collections dataset (20042023) is employed that offers the data on federal revenues at the daily level, by tax type, payment type, electronic type, and the value of collection. The data contains both numerical and non-numerical features, which allow generalizing the trends and artificial intelligence-based forecasting approaches. Results find a strong trend to digitalization of revenue collection as Internet and wholly electronic transactions are gaining dominance over the old ones, including bank, mail, and over-the-counter transactions. Time-series modeling demonstrates the capability of AI to provide more precise revenues forecasts and simulate the results of policies in changing the debt to GDP ratio to help policymakers predict risks and opportunities in the fiscal environment. Besides, effective debt management with the help of AI is able to cut down the interest expenses, leaving some essential funds to reinvest them in infrastructure, healthcare, defense and other national concerns. The ethical issues, such as transparency in the AI decision-making, and cyber security threats, data privacy, and fair access to the digital systems are also touched upon, and the necessity of responsible use of new tec…
subject: Research Article
  topic: section
subject: U.S. National Debt Artificial Intelligence Data Analytics Revenue Forecasting Treasury bond Optimization Fiscal Sustainability
  topic: keywords
creator: Mohammad Kowshik Alam
creator: Md Asief Mahmud
creator: Md Saiful Islam
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0
date: 2024-06-25
publication date: 2024-06-25
starting page: 236
ending page: 256
doi: 10.32996/jcsts.2024.6.2.25

AI-Driven Big Data Analytics for Personalized Cancer Treatment: Integrating Multi-Omics, Medical Imaging, and Predictive Intelligence
type: item
description: Cancer treatment still faces some major challenges - tumors are highly variable, diagnosis is slow, and treatments are not well personalized. Traditional cancer care is based on generalized treatment strategies and does not take into consideration the unique genetic, clinical, and environmental variations of each individual patient. Recent advances in big data analysis, artificial intelligence (AI), and machine learning (ML) have begun to bring personalized cancer care. In this shift, decisions are made using a large amount of medical data of varying nature. This paper provides an overview of big data frameworks based on Artificial Intelligence (AI) for the personalized treatment of cancer. It combines clinical data, multi-omics data, and advanced imaging. Using proven techniques and emerging research, the paper demonstrates how deep learning and predictive modeling, along with explainable AI, are involved in improving the early detection of cancer, predicting how patients will react to treatment, and planning individualized therapies. The results show that AI systems enhance diagnostic accuracy, improve risk identification, optimize treatments, and reduce side effects and wastage. However, there are still problems. Data differences, algorithm bias, the challenge of interpreting AI outputs, and rules for using AI in medicine are all issues that are still being debated. This research provides a single framework for AI-based precision oncology that can be scaled and provides practical advice for clinicians, policy makers, and future research.
subject: Research Article
  topic: section
subject: Artificial intelligence, Big-data analytics, Personalized cancer treatment, Precision oncology, Multi-omics integration, Medical imaging, Predictive modeling
  topic: keywords
creator: Md. Ishtiaque Alam
creator: Mohammad Abdus Sami
creator: Abdullah Al Masud
creator: Hossain Ahmed
creator: Forhad Hossain
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: 428
ending page: 441
doi: 10.32996/jcsts.2025.7.11.40

Google Translate Then and Now: Translations From Five Languages into English and Arabic (2012–2025)
type: item
description: Google Translate (GT) is a free online service that instantly translates words, phrases, text and web pages across 249 languages including English, Arabic, German, French, Hungarian, Turkish and others. In 2006, GT was launched as a statistical machine translation system (SMT) and in 2016, it transitioned to a neural machine translation system (NMT) with differences in translation quality in the two eras. This study compares the translation of six texts from Hungarian, German and Spanish, Turkish and Japanese to English and Arabic by GT in 2012 (SMT era) and in 2025 (NMT era) in terms of intelligibility, fluency, and semantic, lexical and syntactic accuracy. For the SMT era (2012), holistic evaluations showed that Hungarian-English, German-English, Spanish-English translations were somewhat intelligible, with literal meanings, but were a bit awkward and clumsy. GT failed to capture idiomatic nuance, produced sentences with broken syntax and robotic phrasing. Turkish-English and Japanese-English translations were far more problematic, riddled with broken syntax, incoherence, and nonsensical phrases. Arabic translations by GT across the five languages were largely unintelligible. Lexical equivalents were inaccurate and sentence structure was nonsensical with jumbled, incoherent, fragmented, distorted, and clumsy word order. Arabic translations were unusable. In 2025 (NMT era), GT translation from European and non-European languages to both English and Arabic drastically changed. It became intelligible, fluent, coherent, stylistically natural, contextually accurate, and appropriately contextualized. Syntax and word order were preserved. The differences in translation quality for both English and Arabic in the two eras stem from differences in architecture, training, and linguistic modelling, In 2012, GT matched segments of source text against stored bilingual units and recombined them according to probability. It worked reasonably well for European languages that share…
subject: Research Article
  topic: section
subject:
  topic: keywords
  value: Google Translate (GT), Statistical Machine Translation (SMT), Neural Machine Translation (NMT), SMT vs NMT, Arabic translation, English translation, GT as SMT, GT as NMT, European language translations, non-European language translation
creator: Reima Al-Jarf
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0
date: 2025-12-17
publication date: 2025-12-17
starting page: 413
ending page: 427
doi: 10.32996/jcsts.2025.7.12.50

Security Architecture Program Governance at Scale: A Technical Framework
type: item
description: Enterprise Security Architecture has historically struggled to keep pace with the velocity of digital transformation. Traditional governance models rooted in manual reviews and static documentation cannot scale across cloud-native environments, composable systems, and federated delivery teams. This paper introduces a federated, pattern-driven operating model that treats architecture as an executable system rather than a static process. The model integrates guiding principles, strategy, policies, and technical standards with automation, reusable design patterns, and configuration baselines to translate intent into verifiable outcomes. Decision authority is distributed across an enterprise architecture core, an architecture review function, and embedded security champions, aligning autonomy with consistency. Governance adapts dynamically to organizational archetypes and product delivery models, ensuring that control strength aligns with business criticality and development velocity. Effectiveness is demonstrated through measurable indicators pattern adoption, review cycle time, automated control coverage, and defect escape showing that security can scale through evidence, enablement, and automation rather than inspection. This approach repositions Security Architecture as an operational enabler of innovation a system that learns, adapts, and assures in real time while preserving trust and resilience across the enterprise.
subject: Research Article
  topic: section
subject: Enterprise Security Architecture, Federated Governance, Security Patterns, Competency Framework, Architectural Automation
  topic: keywords
creator: Rakesh Reddy Panati
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-17
publication date: 2025-12-17
starting page: 403
ending page: 412
doi: 10.32996/jcsts.2025.7.12.49

Ethical and Privacy Implications of Machine Learning in Android Development
type: item
description: This article examines the ethical and privacy implications of machine learning integration in Android application development. It explores how ML-powered applications transform data collection and processing, creating unprecedented privacy vulnerabilities while enabling advanced functionalities. Through detailed case studies across healthcare, social media, and e-commerce applications, the article illustrates varied implementation practices and their impacts on user privacy. It identifies sources of algorithmic bias within Android ML systems and evaluates their disproportionate effects on marginalized communities, while assessing technical solutions, including federated learning and differential privacy frameworks. The article evaluates multi-stakeholder governance models, industry standards, and educational initiatives necessary for responsible ML deployment. By connecting technical implementation details with broader social implications, the article provides a comprehensive ethical framework for Android developers, policymakers, and users navigating the complex landscape of machine learning in mobile ecosystems.
subject: Research Article
  topic: section
subject: Machine Learning Ethics, Android Privacy, Algorithmic Bias, Federated Learning, Mobile Data Governance
  topic: keywords
creator: Kamal Gupta
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-17
publication date: 2025-12-17
starting page: 393
ending page: 402
doi: 10.32996/jcsts.2025.7.12.48

Architecting Resilient Data Pipelines: Best Practices for Scalable ETL in Multi-Cloud Environments
type: item
description: The exponential growth of digital data and the widespread adoption of multi-cloud strategies have necessitated a fundamental transformation in how organizations design and implement data pipelines for enterprise-scale operations. This article presents a comprehensive framework for architecting resilient, scalable, and intelligent Extract, Transform, Load systems capable of operating seamlessly across heterogeneous cloud environments, including Amazon Web Services, Microsoft Azure, and Google Cloud Platform. The article addresses critical challenges inherent in multi-cloud data engineering, including fault tolerance, performance optimization, cross-platform interoperability, data governance, and cost efficiency. A layered architectural approach is proposed, incorporating five distinct layers that encompass data ingestion, staging and transformation, storage and persistence, orchestration and workflow management, and monitoring and governance. The framework integrates advanced capabilities such as event-driven orchestration, containerized microservices execution, metadata-driven processing, and artificial intelligence-assisted optimization to enable autonomous, self-healing pipeline operations. Particular emphasis is placed on leveraging machine learning algorithms for predictive failure detection, adaptive scheduling, workload optimization, and continuous data quality assurance. The architectural principles draw upon established best practices from microservices design, containerization technologies, continuous integration and continuous delivery methodologies, and industrial predictive maintenance strategies. Experimental validation demonstrates that the proposed architecture delivers substantial improvements in system reliability, operational efficiency, and economic performance compared to traditional single-cloud and monolithic approaches. The article establishes a comprehensive blueprint for organizations seeking to modernize their data infrastructure through cl…
subject: Research Article
  topic: section
subject: Multi-Cloud Architecture, ETL Pipelines, Resilient Systems, AI-Driven Optimization, Microservices, Containerization, Data Engineering, Scalability, Predictive Maintenance, Cloud Computing
  topic: keywords
creator: Sushil Kumar Tiwari
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2025-12-17
publication date: 2025-12-17
starting page: 384
ending page: 392
doi: 10.32996/jcsts.2025.7.12.47

Agentic AI Frameworks: Building Autonomous, Self-Healing Systems for Financial Infrastructure
type: item
description: Financial infrastructure management is facing unprecedented challenges as distributed architectures with hundreds of interdependent microservices impose complexity beyond the human cognitive limit for real-time monitoring and intervention. Conventional reactive monitoring systems bring intolerable latency between anomaly discovery and human perception, and the manual diagnostic processes take considerable time during which the services are degraded. Security alert proliferation overloads operations teams, with false positives above 90% in expert domains like anti-money laundering surveillance. Agentic AI frameworks overcome these inherent limitations with self-contained systems that combine perception, reasoning, and action capabilities within operational infrastructure cores. Multi-agent architectures provide specialist domain expertise in areas of network performance, database optimization, security threat response, and capacity management while retaining collaborative problem-solving abilities for sophisticated failure scenarios. Self-restoration mechanisms utilize predictive analysis to determine precursors to failure minutes to hours in advance of full service loss, allowing for preventive actions that do not impact customers at all. Automated threat identification and response condense incident containment windows from hours to seconds, significantly shrinking vulnerability windows that advanced attackers target. Immutable audit trails using blockchain technologies meet regulatory demands for operational visibility while smart contract execution ensures policy compliance. Explainability issues call for the creation of understandable decision models that can explain reasoning logic in a human-readable form. Trust calibration needs graduated autonomy models that move from advisory recommendations to supervised execution toward complete autonomy for routine situations. Directions for the future include federated learning that facilitates cross-institutional shari…
subject: Research Article
  topic: section
subject: Agentic AI Architectures, Self-Healing Systems, Autonomous Infrastructure Management, Multi-Agent Coordination, Explainable Artificial Intelligence, Operational Resilience
  topic: keywords
creator: Sudhindra Desai
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0
date: 2025-12-17
publication date: 2025-12-17
starting page: 364
ending page: 383
doi: 10.32996/jcsts.2025.7.12.46

Airlines Flight Baggage Handling using Predictive Analytics
type: item
description: This paper presents a detailed study of predictive analytics for airline baggage handling systems. It explains how computer vision, machine learning, forecasting models, and routing algorithms improve baggage flow, reduce errors, and lower operational delays. The study also shows how predictive maintenance reduces downtime and improves system reliability. It includes AI versus non‑AI comparison tables, workflow breakdowns, and an integrated architecture. Findings show that predictive analytics improves accuracy, speed, and system capacity while reducing manual work. These results support future large‑scale adoption across airports. [1][2]
subject: Research Article
  topic: section
subject:
  topic: keywords
  value: Predictive Analytics, Airline Baggage Handling, Computer Vision, Machine Learning, Airport Automation, Baggage Routing Optimization, Conveyor System Analytics, Predictive Maintenance, Time Series Forecasting, ARIMA, LSTM, Deep Learning, YOLO, OCR, Sensor Fusion, Industrial IoT, Smart Airports, Operational Efficiency, Aviation Analytics, Intelligent Transportation Systems
creator: Pallab Haldar
rights: Copyright (c) 2025 Journal of Computer Science and Technology Studies
date: 2025-12-22
publication date: 2025-12-22
starting page: 467
ending page: 473
doi: 10.32996/jcsts.2025.7.12.53

Settlement, Fees, and Interchange: Data Models for Accurate Reconciliation and Exception Handling
type: item
description: Payment ecosystems are based on a system of multilayer data communications, which binds together issuers, acquirers, payment processors, card schemes, and settlement banks. The volume of transactions processed worldwide is growing, and financial institutions are becoming more complex to reconcile due to variations in interchange, scheme fees, acquirer markup, cross-border management, late presentment, reversals, and partial captures. The traditional ledger system, which is typically implemented as a monolithic, non-effective-dated database, cannot support the time variability, heterogeneous rules, or changing fee models required for the current level of reconciliation accuracy. Subsequently, organizations have reduced D+1 close rates, operational overhead, manual journal entries, and the risk of increased financial write-offs. The paper presents a unified and ledger-grade effective-dated data modeling scheme of settlement, fees, and interchange computation. They are designed to normalize the heterogeneity of scheme fee structure and the scheme reason code to facilitate a deterministic reconciliation process. It suggests a multilayer schema structure: (1) Transport Core Model, (2) Exchange and Fee Effective-Dated Model, (3) Exception Lifecycle Engine and (4) Settlement Aggregation Layer. This architecture enables acquirers and processors to identify early differences, auto-read exceptions, and retain audit trails of financial state changes. This paper has gone into great detail on the predictable reconciliation requirements in high-throughput environments. It gives reasons why effective-dating fee rules are necessary, why all financial adjustments must be made with ledger atomicity, why reason-code normalisation of interoperability between schemes and why tasks should be automated to make late presentments, reversals, re-authorizations and partial captures. To propose a uniform approach to the process of mismatch treatment in authorization, clearing, and settlement d…
subject: Research Article
  topic: section
subject: Interchange; Effective Dating; Reconciliation; Settlement Models; Exception Handling; Ledger Systems; Payment Schemes; Late Presentment; Reversal Workflow; Cross-Border Fees
  topic: keywords
creator: Ravi Kumar Vallemoni
rights: Copyright (c) 2021 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0
date: 2021-02-28
publication date: 2021-02-28
starting page: 27
ending page: 36
doi: 10.32996/jcsts.2021.3.1.5

Advanced SHA256-AES Hybrid Algorithm for Enhanced Data Confidentiality and Integrity in Cloud Data Security
type: item
description: Clouds are also in need for solid security, which helps to safeguard sensitive data and maintain the confidentiality and integrity of it. Literature survey reveals that the use of classical and standard cryptographic techniques such as hybrid scheme consisting of RSA–AES, DH–AES, and Blowfish–AES has certain limitations in terms of speed, scalability and efficiency for huge cloud datasets. In order to solve those difficulties, this paper suggests the Advanced SHA256 – AES Hybrid Encryption Algorithm that uses SHA256 for ensuring record reliability and using AES algorithm for securing confidentiality. The method is tested on real datasets, including financial transactions, smart-home device logs and user behavior patterns. Experimental results reveal that the proposed SHA256–AES model has significantly superior performance in terms of encryption and decryption speed, and throughput over other hybrids. The architecture is represented by UMLs, flowcharts, use cases, DFDs and ER models. Its only downside is that it creates a strong and efficient solution for contemporary cloud data security.
subject: Research Article
  topic: section
subject:
  topic: keywords
  value: SHA256-AES, Hybrid Encryption, ETh (Encryption Throughput), DTh (Decryption Throughput), TET (Total Encrypted Time), AET (Average Encrypted Time), TES (Total Encrypted Size), TDT (Total Decrypted Time), ADT (Average Decrypted Time), TDS (Total Decrypted Size).
creator: Ahammad Sazid Talukder
creator: Sazid Hasan Sarker
creator: Muhtasim
rights: Copyright (c) 2025 Journal of Computer Science and Technology Studies
date: 2025-12-22
publication date: 2025-12-22
starting page: 453
ending page: 466
doi: 10.32996/jcsts.2025.7.12.52