Journal of Computer Science and Technology Studies
The Journal of Computer Science and Technology Studies (ISSN: 2709-104X) is a double-blind peer-reviewed, open-access journal published by Al-Kindi Center for Research and Development. It covers the latest developments in the broad areas of computer science and information technology. The journal offers readers free access to all new research issues relevant to computer science and information technology. While the journal strives to maintain high academic standards and an international reputation through the suggestions of the international advisory board, it welcomes original, theoretical and practical submissions from all over the world.
Al-Kindi Center for Research and Development
Operational Monitoring for Enterprise Chatbots: Webex Teams–Based Alerting for NLU Drift, Fallbacks, and Service Health
Conversational AI systems are rapidly becoming mission-critical components of enterprise support and service delivery. As organizations delegate increasingly complex operational workflows to chatbots, the reliability and semantic correctness of conversational pipelines directly affect business continuity, security posture, and user trust. However, the production behavior of conversational AI systems is inherently non-stationary: natural language usage evolves, business processes change, and machine learning models degrade under concept drift. Moreover, conversational platforms depend on complex distributed infrastructures, introducing additional operational risks. This paper presents a comprehensive analysis of operational monitoring for enterprise chatbots, with a focus on production observability and incident response using Webex Teams–based alerting. We examine core conversational health signals including fallback rate spikes, intent confidence distribution shifts, latency anomalies, error patterns, and knowledge base miss ratios. Drawing on established research in distributed systems monitoring, site reliability engineering, and machine learning operations, we propose an end-to-end monitoring and alert pipeline specifically tailored for conversational AI systems. We further describe the integration of Kibana dashboards to provide engineers with immediate contextual insight during incidents. While no new empirical performance results are claimed, the paper synthesizes validated engineering practices into a unified operational framework for managing enterprise conversational AI deployments.
Conversational AI Monitoring; Enterprise Chatbots; NLU Drift Detection; Production Observability; Incident Response; MLOps; Webex Teams Alerting; Kibana Dashboards
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Assessing The Development of an Automated Fan Control System
In today's world, automation is necessary for efficient and responsive systems. Automated systems provide significant benefits such as increased productivity and reduced human error, making them increasingly important across various industries. The development of an automated fan control system addressed the limitations of the existing manual fan. Manual fan control often leads to energy waste, insufficient comfort, and higher operating costs. This study aimed to address this gap by developing and implementing an automated fan control system that correctly maintained and optimized temperature, convenience, and reduced energy usage. The automated system was designed for real-time monitoring, utilizing advanced algorithms to control fan operations based on detected human movement and real-time conditions. The results showed that respondents assessed the acceptability of the automated fan control system (AFCS) using the Technology Acceptance Model (TAM). The results from the study provided strong suggestions to adopt and implement automated fan control systems to enhance environmental sustainability and user satisfaction.
Automated Fan Control System, Smart Classroom, Environmental Control, Thermal Comfort, Technology Acceptance Model (TAM)
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Comparing Vision Transformers and Convolutional Neural Networks: A Systematic Analysis
Vision Transformers have emerged as powerful alternatives to Convolutional Neural Networks for image classification tasks. Systematic comparisons under controlled settings remain limited despite growing adoption of transformer-based vision models. The present article conducts comprehensive evaluation of ViTs and CNNs across identical datasets, training conditions, and computational budgets. Multiple architectures including ResNet, EfficientNet, ViT-Base, and DeiT undergo training on benchmark datasets such as CIFAR-10, CIFAR-100, and customized real-world datasets. Performance evaluation encompasses accuracy, F1-score, training stability, adversarial robustness, and inference latency metrics. Results demonstrate that ViTs outperform CNNs on larger datasets while exhibiting superior robustness to noise and perturbations. CNNs maintain advantages for small datasets due to strong inductive biases embedded within convolutional architectures. The effective receptive field in deep convolutional networks exhibits Gaussian distribution patterns centered on each spatial location. Vision transformers learn spatial relationships entirely from data through global self-attention mechanisms. Dataset scale fundamentally determines relative performance characteristics between architectural families. Transformer architectures require substantial training data to discover optimal attention patterns. Convolutional networks converge efficiently on smaller datasets through built-in spatial priors. The article identifies specific conditions under which each architecture demonstrates clear advantages. Findings contribute to understanding of transformer-based vision models while offering practical guidance for architecture selection in applied machine learning systems.
Vision Transformers, Convolutional Neural Networks, Image Classification, Deep Learning Architectures, Adversarial Robustness, Transfer Learning
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Streamlining DevOps Pipelines with AI-Augmented Feature Flagging for Microservices Architectures
The complexity of microservices-based SaaS applications, coupled with the demands of rapid feature development, poses significant challenges for DevOps pipelines, particularly in managing feature rollouts for large teams. This article proposes an AI-augmented feature flagging system that enhances deployment efficiency by predicting feature stability, optimizing rollout strategies, and automating testing configurations. The system relies on machine learning models, the AWS Bedrock API, and the OpenAI API to identify the changes in the code and runtime metrics and use these to guide flag management, which is combined with Kubernetes and CI/CD, such as Jenkins. A React-based dashboard gives the product managers, QA engineers, and developers real-time insight into the rollout progress. This is based on real-world simulations based upon large-scale deployments of microservices and shows a significant decrease in deployment errors and quicker release cycles. The architectural framework integrates predictive intelligence and automated optimization in order to overcome important issues of development teams that deal with complex distributed systems. Machine learning potential will be integrated into the existing DevOps practices to optimize human decision-making during the deployment lifecycle. The numerous benefits of an organization adopting AI-enhanced feature flagging are an increase in deployment reliability, a shortened release schedule, lowered infrastructure expenses, and the development of strong teamwork. The benefits to the environment could also be seen regarding optimal use of resources and reduced energy use in cloud computing infrastructure. The economic returns are in the form of cost savings, productivity, as well as reduced time-to-market in revenue-generating features. The system makes feature flag management more of a proactive risk management process rather than a problem-solving approach. Gradual implementation plans allow building organizational capability stepwise and prove its value at every step. These governance regimes guarantee the right human control and have real control of important issues of deployment. The modular architectural design will be compatible with the existing DevOps toolchains and allow gradual improvement as the organizational capabilities advance.
artificial intelligence in software deployment, microservices architecture management, predictive deployment analytics, automated feature rollout optimization, DevOps pipeline automation
CRM Extensions for Wholesale Jobbers in USA Operations: Efficiency Gains and ROI
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.
Copyright (c) 2026 Journal of Computer Science and Technology Studies
Optimizing Extended Warehouse Management for Make-to-Order Plants: Slotting, Wave Picking, and Yard Orchestration at Scale
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.
Extended Warehouse Management (EWM), Make-to-Order Manufacturing, Slotting Optimization, Wave Picking, Yard Management, Warehouse Automation, ERP Systems, Make-To-Order (MTO)
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Engineering for Millions of Requests Per Second: Building Ultra-Low Latency, High-Availability Services at Scale
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.
Ultra low latency, tail latency, microservices, distributed systems, high availability, cloud computing, caching, cellular architecture, adaptive resilience, Rust, Java
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Cloud Identity Risk Management Framework for Regulated Enterprises: A Comprehensive Approach to Identity-Centric Security
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.
Cloud Identity Management, Risk Assessment Framework, Regulatory Compliance, Privileged Access Control, Identity Governance
Autonomous Remediation Pipelines with Human-in-the-Loop Oversight
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 requirements.
Human-In-The-Loop Systems, Autonomous Remediation, Anomaly Detection, Container Orchestration, Governance Frameworks
An Attention-Enhanced Transformer Framework for Intelligent Energy Management and Load Forecasting in U.S. Power Grids
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 results establish ATMH-WNet as an effective and scalable solution for real-world intelligent energy management and multi-horizon load forecasting applications.
Persistence model, SARIMA, XGBoost, LSTM, Prophet, Time series forecasting, Baseline models, Forecasting benchmarks, Model evaluation, Predictive analytics
A Multi-Functional AI-Enabled Robotic Doctor for Intelligent Patient Monitoring and Autonomous Healthcare Support with Adaptive Human-Centered Interaction
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.
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
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Retrieval-Augmented Generation (RAG) Systems: Architectures, Strategies, and Evaluation
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.
Retrieval-Augmented Generation, RAG, Embedding Models, Reranker Fine-Tuning, Semantic Retrieval, Agentic Architectures, Evaluation Metrics, Vector Databases, Query Routing, Dynamic Context
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The Rise of DataOps Observability: AI-Driven Reliability for Modern Data Platforms
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.
DataOps Observability, AI-Driven Reliability, Generative AI, Machine Learning, Data Pipelines, Anomaly Detection, Predictive Analytics, Self-Healing Systems, Data Governance
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MuleSoft Architectural Paradigms and Sustainability: A Comprehensive Technical Analysis
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.
API-Led Connectivity, Event-Driven Architecture, Sustainable Integration, Microservices Orchestration, Green Computing Practices
Autonomously Transacting Agents: A New Paradigm for AI in Finance
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.
Autonomous Financial Agents, Blockchain Technology, Decentralized Finance, Reputation Systems, Agent Governance
Software Engineering Practices: In the era of AI / LLMs
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].
Generative AI; Software Development; AI Augmented SDLC; Best Practices; Developer Productivity; AI Ethics
A Comparative Performance & Metadata Study of Open Table Formats: Iceberg vs Delta vs Hudi at Scale
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.
Lakehouse Architecture, Table Format Comparison, Metadata Management, Transaction Isolation, Distributed Data Systems
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Transitioning to Open-Source Observability: A Cost-Benefit Analysis and Migration Framework
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.
Observability, Open-source, Prometheus, Loki, Distributed Tracing, Cloud-native, Kubernetes, Cost Optimization
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AI-Driven Big Data Analytics for Precision Medicine: A Unified Framework Integrating Molecular Data Intelligence, Wearable Health Systems, and Predictive Modeling
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.
Precision medicine; artificial intelligence; big data analytics; multi-omics integration; wearable health systems; predictive modeling; federated learning
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Towards a Futuristic Security Roadmap: Advanced Strategies
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.
Cloud Computing, Kubernetes, Artificial-Intellignce, Machine Learning, Scanning, Patching, Continuous Integration, Continuous Deployment, Data Protection, Identity and Access Management.
Calligraphic Text Recognition by Gemini, Ernie ViLG and Google Translate: A Comparative Study of Arabic, Japanese and Chinese
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 calligraphic scripts challenge these AI systems because they intentionally distort or stylize characters, requiring both visual recognition and the ability to match ambiguous forms with known verses, idioms, or poetic structures. Overall, the current results highlight how multimodal AI models diverge when confronted with stylization, cultural priors, and incomplete visual information.
Calligraphic texts, Arabic calligraphy, Japanese calligraphy, Chinese calligraphy, Gemini, ERNIE-ViLG, Google Translate (GT), OCR (optical character recognition), multimodal AI models, AI models
Integrating Blockchain and Data Analytics to Strengthen Financial Traceability and Anti-Fraud Controls
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. financial system.
Blockchain Analytics, Financial Traceability, Ransomware Detection, Anti-Money Laundering, Machine Learning Risk Scoring, U.S. Financial Compliance
Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
Securing Industrial Water Networks: Event-Level Anomaly Detection in SCADA Telemetry
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.
Industrial water networks; SCADA telemetry; Anomaly detection; Hybrid deep learning; Multi-Resolution Temporal Convolutional Network (MR-TCN); Attention mechanisms; Cyber–physical security.
Multi resolution Temporal Convolutional Network
Copyright (c) 2025 Journal of Computer Science and Technology Studies
The AI- Powered Treasury: A Data- Driven Approach to managing America’s Fiscal Future
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 technologies is highlighted. This paper concludes that AI-driven fiscal management does not replace human judgment but is an auxiliary tool that makes decision-making easier, more accountable, and fiscally resilient in the long-term. Through blending technological novelty and a sustainable policy formulation, the U.S can be better placed to have a more stable fiscal future in a more data-driven global economy.
U.S. National Debt Artificial Intelligence Data Analytics Revenue Forecasting Treasury bond Optimization Fiscal Sustainability
AI-Driven Big Data Analytics for Personalized Cancer Treatment: Integrating Multi-Omics, Medical Imaging, and Predictive Intelligence
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.
Artificial intelligence, Big-data analytics, Personalized cancer treatment, Precision oncology, Multi-omics integration, Medical imaging, Predictive modeling
Google Translate Then and Now: Translations From Five Languages into English and Arabic (2012–2025)
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 structural similarities with English, but it struggled with languages that are typologically distant, such as Turkish, Japanese, and Arabic. In 2025, GT has been trained on much larger and more diverse datasets than SMT. This breadth of training data enables better handling of words, specialized terminology, and cross domain variation. The study concludes with recommendations for students, instructors, researchers, and developers on the effective use and improvement of GT.
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
Security Architecture Program Governance at Scale: A Technical Framework
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.
Enterprise Security Architecture, Federated Governance, Security Patterns, Competency Framework, Architectural Automation
Ethical and Privacy Implications of Machine Learning in Android Development
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.
Machine Learning Ethics, Android Privacy, Algorithmic Bias, Federated Learning, Mobile Data Governance
Architecting Resilient Data Pipelines: Best Practices for Scalable ETL in Multi-Cloud Environments
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 cloud-agnostic, resilient, and intelligent pipeline architectures that can adapt dynamically to evolving business requirements, workload fluctuations, and infrastructure conditions while maintaining high levels of data quality, security, and regulatory compliance.
Multi-Cloud Architecture, ETL Pipelines, Resilient Systems, AI-Driven Optimization, Microservices, Containerization, Data Engineering, Scalability, Predictive Maintenance, Cloud Computing
Agentic AI Frameworks: Building Autonomous, Self-Healing Systems for Financial Infrastructure
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 sharing of knowledge, sophisticated causal modeling to predict intervention cascades, and digital twin incorporation, offering safe test beds.
Agentic AI Architectures, Self-Healing Systems, Autonomous Infrastructure Management, Multi-Agent Coordination, Explainable Artificial Intelligence, Operational Resilience