Journal of Computer Science and Technology Studies
The Journal of Computer Science and Technology Studies (JCSTS) 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
The Role of Predictive Analytics in Cloud HR Platforms: A Transformation in Airline Workforce Management
The integration of predictive analytics into cloud-based HR systems is transforming how airlines manage their workforce, shifting from reactive staffing to proactive planning. This article explores how embedded intelligence through machine learning revolutionizes critical airline workforce functions including attrition forecasting, demand planning, and compliance management. Particular attention is given to implementations within SAP SuccessFactors that connect workforce analytics with operational systems to create comprehensive predictive capabilities. The article examines the technical frameworks supporting these implementations, evaluates their impact on operational and financial outcomes, and identifies emerging trends shaping future development. Ethical considerations regarding algorithm-based workforce decisions are addressed alongside practical recommendations for airlines at varying digital maturity levels. The evolution toward human-AI partnership in workforce planning emerges as a central theme, emphasizing the complementary roles of technology and human expertise in optimizing airline workforce management.
Predictive analytics, airline workforce management, cloud HR platforms, machine learning, digital transformation
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
Technical Implementation Guide: Modern Payment Solutions for Captive Finance
The captive finance industry confronts significant challenges from rising payment delinquencies as traditional collection methods lose effectiveness in the digital economy. This technical implementation guide presents a comprehensive framework for deploying modern payment solutions that leverage mobile payment technologies, artificial intelligence-driven predictive analytics, and cloud-native architectures to transform collection operations. The proposed system architecture integrates text-to-pay capabilities, behavioral analytics, and microservices-based payment orchestration to create seamless, secure payment experiences that align with contemporary consumer expectations. Through sophisticated customer segmentation, personalized messaging strategies, and real-time fraud detection mechanisms, these solutions enable proactive intervention before delinquency occurs while maintaining positive customer relationships. The implementation encompasses critical components, including tokenization services for enhanced security, a multi-channel communication infrastructure for optimal customer engagement, and comprehensive data management platforms that provide actionable insights. By adopting phased deployment strategies, organizations can minimize implementation risks while delivering incremental value through improved payment success rates, reduced operational costs, and enhanced portfolio performance. The convergence of emerging technologies such as blockchain integration, voice AI assistants, and advanced machine learning models presents future opportunities for continued innovation in payment collection strategies.
Artificial Intelligence, Captive Finance, Microservices Architecture, Mobile Payment, Text-To-Pay
Taming Asynchrony in Distributed Payment Systems: Guarantees, Idempotency, and End-to-End Reconciliation
Today's distributed payment systems must function correctly despite the inherent presence of asynchrony, partial failures, and third-party integrations. Unlike typical RPC-based workflows used in software development, payment flows are heavily influenced by external delays, retries, timeouts, and nondeterministic state changes across multiple systems of record. A fault-tolerant ledger abstraction that decouples payment intent from execution enables safe retries and supports service events that may arrive out of order. Correctness and safety depend on distributed transaction constructs such as outbox/inbox patterns, compensation workflows, and time-bounded state machines to contain the effects of race conditions, double submissions, and ambiguous or indeterminate outcomes. A declarative reconciliation framework continuously verifies consistency between internal and external systems, enabling real-time anomaly detection and facilitating orchestration and recovery. These pragmatic engineering approaches, validated through simulations and production-level benchmarks, offer guidance for building resilient payment infrastructures in naturally asynchronous and failure-prone environments.
distributed payments, idempotency, eventual consistency, reconciliation, fault tolerance
Geographic Consensus Layer: A Hybrid Consistency Approach for Distributed NoSQL Systems
The Geographic Consensus Layer (GCL) presents a novel hybrid consistency architecture for distributed NoSQL database systems, specifically addressing the challenges of achieving strong consistency in geo-distributed Apache Cassandra deployments. By introducing a decoupled control plane implementing Multi-Paxos across regions, GCL provides linearizable consistency guarantees exclusively for critical operations while preserving high performance for non-critical workloads. This article effectively isolates the unavoidable latency penalties of cross-region consensus to only those operations that genuinely require strong consistency. Experimental evaluation demonstrates that GCL maintains near-baseline throughput while ensuring linearizability where needed. Architecture includes metadata-cavalry unanimous, operation batching, and pipeline processing, such as adaptation to reduce overheads. The GCL represents a practical solution for the fundamental trade-bands imposed by the CAP theorem, enabling the organizations to deploy globally distributed globally without renouncing continuity for wider operations or comprehensive systems.
Distributed databases, NoSQL, hybrid consistency, Paxos consensus, geo-distribution, linearizability
Executive Coaching as a Strategic Lever in Enterprise Agile Transformation: Accelerating Scaled Execution and Cultural Change
Executive coaching drives successful enterprise-wide agile transformations forward. Companies battling turbulent markets and sudden tech shifts increasingly value the power of aligned leadership and cultural evolution. Case studies highlight how coaching sparks essential mindset changes, reshapes behaviors, and builds strategic harmony. Deliberately woven throughout transformation efforts, executive coaching accelerates cultural progress, develops flexible leadership, and sustains consistent execution across intricate organizational frameworks. Far more than just a support mechanism, coaching serves as a strategic cornerstone addressing fundamental human elements of change. The impact appears through enhanced leadership capabilities, stronger team dynamics, and persistent cultural shifts - achievements that technical adjustments alone consistently miss. Firms placing coaching at their transformation center experience smoother implementation and lasting market advantages amid constantly evolving business landscapes.
Executive coaching, agile transformation, leadership development, vertical development, psychological safety
Enterprise Application Modernization in the Cloud Era: A Case Study of Ticket Management System Migration
The migration and modernization of enterprise applications from on-premises infrastructure to cloud environments presents significant technical and organizational challenges. This article documents the transformation journey of a large-scale ticket management system from a monolithic architecture on VMware to a cloud-native implementation using containerization and microservices. Through a strategic two-phase process, the project first established baseline operations via lift-and-shift migration to Google Compute Engine before evolving to a fully containerized architecture on Google Kubernetes Engine. The transformation included database migration from Oracle to PostgreSQL, implementation of advanced network security policies, comprehensive disaster recovery mechanisms, and modern observability solutions. The resultant architecture demonstrated marked improvements in system reliability, performance, security posture, and operational efficiency. The documented strategies, challenges, and outcomes provide valuable insights for organizations undertaking similar modernization initiatives in enterprise environments.
Cloud migration, containerization, microservices, enterprise modernization, disaster recovery
Contactless Innovation in Retail Finance: Comparing Closed-Loop Gift Cards and Open-Loop Credit Cards
This article examines the evolutionary trajectory of contactless payment systems across closed-loop and open-loop architectures, tracing their development from magnetic stripe foundations through EMV chip technology to contemporary NFC implementations with cryptogram-based security. The comparative analysis highlights how closed-loop systems deliver enhanced customer loyalty and data ownership, while open-loop networks provide global accessibility and financial inclusion. The security architecture of contactless payments is explored through a detailed examination of cryptogram-based authentication, tokenization mechanisms, vulnerability mitigation strategies, and biometric integration. Future directions reveal emerging trends, including digital-only credential issuance, integration with adjacent technologies such as IoT and distributed ledgers, evolving consumer trust dynamics, and the developing regulatory landscape. The article illuminates how contactless innovation continues to reshape retail finance while balancing convenience, security, and commercial objectives across payment environments by analyzing these technological and ecosystem factors.
Contactless Payments, NFC Cryptograms, Tokenization Security, Closed-Loop Ecosystems, Biometric Authentication
Clinical Data Systems for Global Trials: Advancing Regulatory Success and Improving Outcomes for Patients Worldwide toward Better Treatments for Society
The success of global clinical trials is ultimately reliant upon robust Clinical Data Systems and infrastructure for data integrity, regulatory compliance, and rapid patient access to new treatments. To efficiently manage clinical data advanced data management systems are required to integrate complex, multinational data sets, while providing the highest levels of quality and regulatory acceptability. The Clinical Data Systems have the main technology that allows pharmaceutical companies to present the SDTM/ CDISC compliant clinical study data to regulatory authorities to review and render judgments about new medical products with confidence, allowing for timely approvals and drug development, and enhancing patient access to therapies around the world. Clinical Data Systems are a suite of emerging technologies that have Electronic Data Capture (EDC) platforms, automated validation products, and risk-based monitoring capabilities that are beginning to redefine the clinical trial paradigm. Both worldwide standardization, such as Clinical Data Interchange Standards Consortium (CDISC), provide a degree of assurance in the representation of data in decision-making from different regulatory jurisdictions, while allowing patient-reported outcomes and real-world evidence to be integrated easily. This type of technological capability also results in clinical trial designs that can be more representative of the global population and improve the generalizability of clinical results across diverse populations in various locations.The compliance features of contemporary Clinical Data Systems, which deal with intricate requirements such as electronic management of records, extensive audit trail management, and multi-faceted security measures to protect patient confidentiality while guaranteeing data validity, have helped bring about worldwide regulatory harmonization efforts, greatly increased submission acceptance rates, and shortened review timescales to the ultimate benefit of patients through quicker access to life-preserving medications. The social impact reaches beyond individual patient results to include drug development economic efficiencies, increased public confidence in clinical trials, and more efficient delivery of healthcare in developed and emerging countries.
Clinical Data Systems, Global Clinical Trials, Regulatory Compliance, Patient Outcomes, Data Management
Architecting HIPAA-Compliant Real-Time Messaging Platforms: Balancing Security, Performance, and Usability in Healthcare Communications
This article examines the architectural considerations, security implementations, and performance optimizations necessary for building real-time messaging platforms that meet healthcare compliance requirements while delivering satisfactory user experiences. The discussion encompasses essential security architecture components, including encryption methodologies, key management strategies, and audit logging systems designed specifically for protected health information. Protocol selection frameworks are presented with particular attention to performance characteristics in bandwidth-constrained environments and on resource-limited devices. The article explores offline-first design principles that enable continuous clinical communication regardless of connectivity status, while maintaining appropriate security controls across synchronization boundaries. Performance engineering strategies address the computational overhead of encryption, battery and bandwidth optimization for mobile healthcare scenarios, and database designs that balance query performance against security requirements. User experience concerns are kept at the forefront of the investigation, acknowledging that security measures must blend in perfectly with clinical procedures to avoid workarounds that eventually jeopardize patient care quality and compliance.
HIPAA-compliant messaging, Healthcare cybersecurity, End-to-end encryption, Offline-first architecture, Secure synchronization
AI-Enhanced Customer Loyalty Systems: A Collaborative Framework
Modern customer loyalty platforms are subject to great limitations in offering customized experiences based on dependence on static rule-based systems that cannot handle heterogeneous behavioral patterns or forecast future engagement paths. The shift from deterministic reward systems to adaptive systems is a core challenge that involves the integration of artificial intelligence capabilities without compromising on strategic human control. The current paper advocates a collaborative framework where machine learning algorithms perform large-scale behavioral analysis, pattern discovery, and predictive modeling, and human managers are left with strategic direction, ethical oversight, and contextual discretion. The architecture decouples computational tasks amenable to algorithmic execution from decisions needing domain experience and stakeholder input. Implementation calls for stop-to-stop organizational exchange involving a group of workers potentially constructing technical infrastructure for iterative improvement, and governance techniques to balance automation effectiveness with human control. Privacy-keeping statistics designs, mechanisms for mitigating bias, and requirements for transparency bridge moral issues inherent in algorithmic decision-making. Performance tracking frameworks determine each technical precision and enterprise consequences to ensure strategic goals are aligned with both technical optimization and algorithmic optimization. The collaborative technique enables ongoing development through systematic comment loops wherein human validation complements predictive accuracy at the same time, while making sure organizational priorities dictate automatic movement. The version illustrates how corporations can recognize personalization at scale through balanced AI-human collaboration, which utilizes complementary strengths while compensating for the risks of over-reliance on automation.
Artificial Intelligence Integration, Customer Loyalty Platforms, Human-AI Collaboration, Predictive Personalization, Business Model Innovation, Algorithmic Governance
AI-Augmented Data Pipelines: Integrating Machine Learning for Intelligent Data Processing
Contemporary data engineering methodologies evolve with the addition of artificial intelligence, creating smart processing systems that move beyond conventional ETL barriers. Large language models and machine learning programs facilitate autonomous content tagging, outlier identification, and forecasted quality review within enterprise data streams. Transformer-based architectures illustrate superior performance in financial document handling, attaining accuracy levels above ninety-six percent while processing millions of communications daily. Microservices designs enable stand-alone deployment and scaling of AI components, allowing for containerized model-serving platforms to achieve sub-second response times in distributed computing environments. Schema matching algorithms learn relationships between heterogeneous data sources automatically, allowing for dynamic structural changes to be adapted without human intervention. Data engineering and MLOps teams collaborate cross-functionally to speed up deployment cycles while ensuring system reliability using common monitoring frameworks. Real-world deployments show significant operational expense savings and processing time gains, with machine-based classification systems substituting for hands-on processes formerly involving significant human resources. Predictive quality evaluation capabilities allow proactive management of pipelines to prevent degradation incidents before they have an effect on downstream analytics systems. The intersection of artificial intelligence with data processing infrastructure provides self-upgrading pipelines that monitor changing business needs while enforcing rigorous quality standards over enterprise-level deployments.
AI-extended pipelines, machine learning embedding, auto-classification, anomaly detection, schema change, MLOps collaboration
AI, Cloud, and Cybersecurity Convergence in Banking, Financial Services, and Insurance (BFSI): A Strategic Framework
The Banking, Financial Services, and Insurance (BFSI) sector is undergoing a fundamental transformation driven by the convergence of Artificial Intelligence, Cloud computing, and Cybersecurity, moving from traditional siloed IT infrastructures to integrated intelligent ecosystems that respond dynamically to changing market circumstances and evolving threat environments. This paradigm shift enables financial institutions to process unprecedented transaction volumes, deliver personalized customer experiences through generative AI, and implement sophisticated risk management capabilities while navigating complex regulatory landscapes including GDPR and PSD2 compliance requirements. The strategic implementation encompasses AI applications—including Natural Language Processing for regulatory compliance automation, federated learning for collaborative fraud detection, and predictive analytics for risk assessment—alongside the evolution of cloud strategies toward hybrid and multi-cloud architectures utilizing Kubernetes orchestration, serverless computing, and Zero Trust Network Access frameworks. The analysis examines how cybersecurity approaches have transformed from traditional compliance-oriented models to AI-augmented threat intelligence systems incorporating behavioral biometrics, homomorphic encryption, and confidential computing technologies to address sophisticated attack vectors targeting critical financial infrastructure. Navigating this convergence requires not just technological adoption but a strategic commitment to cross-industry collaboration and a fundamental organizational mindset shift to address emerging challenges including post-quantum cryptography transitions, AI adversarial attacks, and supply chain security threats. The innovation imperative extends beyond organizing technological implementation to encompass regulatory adaptation, ecosystem-wide collaboration, and the development of resilient digital financial ecosystems capable of maintaining operational continuity while ensuring customer trust and regulatory compliance in an increasingly interconnected digital landscape.
Artificial Intelligence, Cloud Computing, Cybersecurity, Digital Transformation, Financial Technology
A Strategic Framework for Network Infrastructure Transformation and Organizational Resilience Implementation
This article examines the strategic transformation of network infrastructure management from traditional reactive maintenance approaches to comprehensive proactive frameworks that emphasize prevention, automation, and systematic optimization. The article examines how organizations can achieve substantial improvements in system reliability, cost efficiency, and operational resilience by implementing architectural redesign initiatives and network policy automation systems. Through detailed case study analysis, the article demonstrates that proactive infrastructure strategies enable organizations to minimize unplanned service disruptions while simultaneously reducing operational expenses and enhancing business continuity capabilities for distributed workforce environments. The article reveals that successful transformation requires systematic change management processes, comprehensive stakeholder engagement, and sustained organizational commitment to long-term strategic objectives. The article indicates that proactive network management approaches provide superior value propositions compared to reactive maintenance models, particularly in supporting modern enterprise requirements for high availability, scalability, and cost optimization. The article provides practical frameworks for network strategy development, best practices for implementation, and actionable recommendations for organizations seeking to modernize their infrastructure operations. The article addresses critical gaps in existing literature by providing empirical evidence of transformation outcomes and detailed methodological guidance for implementation planning. These articles have significant implications for organizational strategic planning, technology investment decisions, and operational excellence initiatives within contemporary enterprise environments that increasingly depend on reliable, efficient, and adaptable network infrastructure systems.
Network infrastructure transformation, Proactive maintenance strategies, Policy automation systems, Business continuity planning, Cost optimization frameworks
Generative AI in Business Analytics: Opportunities and Risks for National Economic Growth
Generative Artificial Intelligence (AI) is a pioneering development in Business Analytics, redefining the role of generating insights by organizations and optimizing their decision-making and maintaining growth in the digital transformation age.This study explores how Generative Artificial Intelligence (AI) can offer a transformative change in Business Analytics and the implications such changes have on the economic development of a country and what opportunities and threats are presented by the introduction of AI into the economic processes. This study compares the impact of generative AI-based analytics on macroeconomic outcomes, financial inclusion, and sustainable development in five leading economies in the world, namely, the United States, China, India, Japan, and Germany, using the data provided by the World Bank, World Development Indicators (WDI), the Economic Policy and Debt and Financial Sector datasets. As a quantitative research method, data was processed and analyzed in Python, Tableau, and Microsoft Excel, which made it possible to approach the data statistically, comparatively, and visually. The GDP growth, adjusted net national income, education expenditure, and ownership of financial accounts constituted key indicators which were analyzed to investigate the influence of digital transformation, human capital and technological readiness on the national productivity and innovation. The findings indicate that the AI readiness correlates positively with financial inclusion and economic performance at a strong level. The more democratic economies with a larger amount of digital infrastructure, investment in education, and utilization of AI, like China and India, demonstrate better economic stability and growth inclusiveness. On the other hand, mature AI-based economic trends of developed countries are stable but oriented towards efficiency and sustainability and not on high growth. This study also lists the challenges such as data inequality, job displacement due to automation and lack of ethical governance. These results underline the importance of the idea that as much as generative AI can become the driver of innovation and development, it also requires a set of strategic policies that would reduce the risk of socio-economic and ethical consequences. Finally, the paper finds that Generative AI in Business Analytics is both a governance issue and a revolutionary opportunity to the global economies. Effective regulation of how countries strike the right balance between AI innovation, human capital development, and digital transformation to include everyone in growth and transformation will determine sustainable economic progress.
Generative Artificial Intelligence, Business Analytics, Economic Growth, Financial Inclusion, Digital Transformation and Sustainable Development
Zero-Trust Security Architecture for AI-Integrated Private 5G Networks
Private 5G networks represent a transformative evolution in enterprise connectivity, blending reliability, ultra-low latency, and customizability for diverse applications. As organizations deploy these networks in manufacturing facilities and university campuses, integrating artificial intelligence creates both opportunities and security challenges. Traditional perimeter-based security models prove inadequate in these dynamic environments where devices move across network slices and workloads shift between edge and cloud. This article proposes an innovative Zero-Trust Security Architecture for AI-integrated private 5G networks, operating on the principle of "never trust, always verify" while leveraging AI for continuous authentication, behavioral analysis, and automated policy enforcement. The architecture's four-layer framework—Access, Transport & Segmentation, Policy & Intelligence, and Control & Orchestration—addresses the unique challenges of securing these environments. Key components include AI-powered identity management, micro-segmentation through network slicing, predictive threat detection, and a centralized governance layer. This comprehensive article enables organizations to maintain security integrity while fully leveraging the transformative potential of AI-integrated private 5G networks in critical infrastructure settings.
Zero-Trust Architecture, Private 5G Security, AI-Enhanced Cybersecurity, Network Slicing, Behavioral Authentication
Investment Analysis Report: Intel Corporation vs. Advanced Micro Devices
This Investment Analysis Report provides a detailed comparative evaluation of Intel Corporation and Advanced Micro Devices (AMD), two dominant forces shaping the global semiconductor market. The study investigates their financial health, competitive positioning, and innovation strategies within the rapidly evolving landscape of digital transformation and AI chips development. Through a combination of ratio analysis, revenue trend assessment, and growth projections, the report explores each company’s performance in profitability, liquidity, and long-term sustainability. It also highlights how both firms are adapting to emerging technologies such as machine learning, cloud computing, and integrated circuit design to maintain market leadership. The analysis extends to external factors such as supply chain resilience, geopolitical challenges, and consumer demand shifts that influence valuation and investor sentiment. By integrating financial and strategic perspectives, the report offers critical insights for investors seeking exposure to high-growth technology equities. The findings emphasize the balance between innovation-driven expansion and prudent financial management in assessing investment attractiveness across both firms.
Semiconductor Market, AI Chips, Financial Performance, Tech Stock Valuation, Innovation Strategy, Sustainable Investing
AI-Powered Business Analytics in Marketing: Unlock Consumer Insights for Competitive Growth in the U.S. Market
The fast evolution of artificial intelligence (AI) has revolutionized the business analytics industry, especially in the field of marketing, where companies are willing to learn more about consumer behavior in order to facilitate competitive development. This paper explores how AI-based analytics can support identifying customer insights through online reviews of products through a massive e-commerce market in the U.S. This study examines how AI-driven business analytics can be used in marketing, specifically how it can unlock consumer insights that can be used to grow competitively in the U.S. market. The research uses the Amazon Product Reviews Dataset which contains structured and unstructured consumer reviews that include ratings, text reviews, helpful votes, and timestamps. Through the use of AI methods including sentiment analysis, predictive modeling, and recommendation engines, the study shows how companies can extract deep behavioral patterns of large-scale review data. Through the analysis, it is noted that although overall product ratings show high prevalence of customer satisfaction, there are differences in helpful votes which bring to attention the validity and strength of balanced review, and temporal review patterns which show cycles of consumer engagement due to promotions and seasonal shopping patterns. These results highlight the advantage of AI-driven solutions over the traditional marketing analytics that tend to use averages, simple surveys, and static segmentation that do not account for the subtleties and do not predict future behaviors. The paper also emphasizes the role of AI in improving personalization, segmentation, and decision-making backed by data that may lead to customer loyalty, increasing brand trust, and innovation-related development. In addition to benefits to the organization, it has spread out to the U.S. economy through enhancing competitiveness in the international markets and ensuring resilience due to consumer-focused approaches. Although the study also notes some weaknesses associated with the scope of the dataset, geographical focus, and computational power, it notes that AI has the potential to become a strategic imperative in business. This study will have its contribution to both scholarship and practice in demonstrating how AI-enhanced consumer insights may transform marketing practices to make sure that U.S. firms will be competitive in a more digital and globalized world.
Artificial Intelligence (AI), Business Intelligence, Consumer Insights, Predictive Modeling Online, Product Reviews, Marketing Personalization and Market Competitiveness in U.S. market.
Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
Advanced Multi-Source Uninterrupted Power System for Mission-Critical Application
This article presents an innovative multi-source uninterrupted power system designed to address critical reliability requirements in mission-essential facilities where power interruptions can result in catastrophic consequences. The proposed architecture integrates diverse energy sources, including grid power, renewable inputs, backup generators, and battery storage under unified intelligent control, eliminating single-point failure vulnerabilities inherent in conventional UPS systems. Through advanced hybrid alternator technology incorporating power electronics and DC link voltage regulation, the article achieves frequency-independent operation adaptable to global electrical standards without hardware modifications. The intelligent control framework employs hierarchical decision-making with predictive algorithms enabling zero-delay source transitions while optimizing energy utilization based on real-time conditions. Experimental validation demonstrates superior efficiency across all loading conditions, particularly at partial loads where traditional systems exhibit significant losses. Practical deployments in data centers, healthcare facilities, and defense installations confirm operational reliability exceeding industry standards while reducing environmental impact through maximized renewable energy utilization. This article establishes a comprehensive framework for next-generation power continuity solutions capable of meeting the evolving demands of increasingly digitalized and sustainability-focused critical infrastructure.
Multi-source UPS, Intelligent power management, Zero-transfer switching, Hybrid alternator, Mission-critical infrastructure
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
Windows Are Strategy: Generative Artificial Intelligence for Time-Window Design in Big-and-Bulky Delivery
The design of delivery windows is a strategic gateway in the field of large-scale logistics, with poorly planned time slots creating systemic inefficiencies. The architecture is a generative-plus-optimization architecture that retasks windows, based on post hoc operational considerations, to strategic considerations. The interface joins an AI-based generative element, which produces the window menus and communications to the customers, with a solid operations-research interior, which finds the optimal solution to the issue of vehicle routing at scale. A pyramidal assessment system including replay harnesses, shadow arms, and controlled split validations ensures a long-term performance in terms of promise accuracy, route density, and environmental measures. The variants of operational deployment are geographic differentiation of urban and rural settings, seasonally versatile modifications, and tiers of service that incorporate window accuracy and installation offers. The governance approach, based on modern AI risk frameworks, provides fair results and compliance with the regulations. The effect of strategic window design in terms of the improvement of operational efficiency, environmental sustainability, and customer experience is empirically proven in the context of the specific domain of large-volume delivery.
Time-Window Design, Generative Artificial Intelligence, Vehicle Routing, Big-And-Bulky Delivery, Last-Mile Optimization
A Maturity Model for Observability in Big Data Pipelines: From Reactive Monitoring to Predictive Resilience
This article introduces a comprehensive maturity model for observability in big data pipelines, addressing the critical gap between traditional monitoring approaches and the complex requirements of modern distributed systems. The proposed framework delineates three distinct maturity stages—Basic, Advanced, and Predictive—providing organizations with a structured roadmap to systematically enhance their observability capabilities. Drawing from empirical research, industry case studies, and theoretical foundations in resilience engineering and autonomic computing, the model encompasses both technical and organizational dimensions essential for successful observability transformation. The Basic stage is characterized by siloed telemetry sources and reactive incident response, while the Advanced stage introduces unified telemetry streams, SLO-driven alerting, and cross-functional ownership models. The Predictive stage represents the pinnacle of observability maturity, featuring AI/ML-driven anomaly detection, automated remediation, and self-healing capabilities that enable proactive system management. Implementation strategies emphasize the importance of design patterns such as Correlation ID and Circuit Breaker patterns, alongside validation practices including chaos engineering and meta-observability. The article demonstrates that successful observability implementations require equal attention to technical sophistication and cultural transformation, with organizations achieving significant improvements in mean time to detection and recovery metrics as they progress through the maturity stages. Evidence from hyperscale operators and systematic literature reviews validates the model's efficacy, highlighting the convergence of academic research and industry practice in addressing the observability challenges of cloud-native architectures, microservices deployments, and dynamic containerized environments.
Observability Maturity Model, Big Data Pipelines, Predictive Resilience, Cloud-Native Architectures, AIOps
Policing with AI: Navigating the Balance Between Enhanced Security and Civil Liberties
This article examines the complex intersection of artificial intelligence and law enforcement in the United States, highlighting both the transformative potential and significant concerns associated with these technologies. It explores how police departments are increasingly deploying AI-driven systems, including predictive algorithms, facial recognition, automated license plate readers, and gunshot detection sensors, fundamentally altering traditional policing practices. Through a comprehensive review of empirical research, the article investigates how these technologies promise enhanced efficiency through resource optimization, rapid response capabilities, and potentially more objective analysis, while simultaneously raising profound concerns about perpetuating systemic bias, identification errors with serious consequences, privacy erosion, and diminished public trust. The article extends to emerging governance frameworks, emphasizing the importance of regulatory oversight, community involvement, and technical safeguards. By drawing on diverse scholarly perspectives, this article provides a balanced assessment of AI's role in modern policing while acknowledging the complex interplay between technological capabilities, institutional practices, and societal values that will ultimately determine these systems' impact on justice and public safety in American communities.
Algorithmic Policing, Predictive Technologies, Facial Recognition, Surveillance Governance, Civil Liberties
Mastering Search System Reliability and Availability: Architectures for 24/7 Search Operations
The exponential growth of digital services has transformed search systems from simple information retrieval tools into critical infrastructure components that underpin modern business operations. This article presents a comprehensive evaluation of architectural principles, strategies, and best practices essential for building and maintaining search systems capable of delivering consistent round-the-clock operations. Through an in-depth analysis of distributed system architectures, the study explores how defense-in-depth approaches, component isolation, and redundancy mechanisms work together to prevent single points of failure from cascading into system-wide outages. The research investigates various failover strategies ranging from primary-replica configurations to sophisticated quorum-based and multi-master architectures, highlighting the trade-offs between consistency, availability, and partition tolerance. Furthermore, the article examines the evolution of monitoring and incident response systems from simple threshold-based approaches to intelligent, machine learning-driven platforms that can predict and prevent failures before they impact users. The analysis of database consistency models reveals how different approaches from strong consistency to eventual consistency impact system reliability and availability in distributed search environments. By synthesizing insights from reliability-driven architecture design, high availability strategies, observability frameworks, and consistency model implementations, this work provides organizations with a comprehensive roadmap for achieving enterprise-grade search system reliability while balancing technical complexity with operational requirements.
Distributed search systems, high availability architecture, failover strategies, consistency models, incident response automation
Incident Management in B2B Payments: Challenges, Frameworks, and Emerging Best Practices
Business-to-business payment systems form the critical infrastructure supporting global commerce, yet their increasing digitization and complexity have created new vulnerabilities that demand sophisticated incident management approaches. This extended research examines the unique challenges facing B2B payment organizations in managing system disruptions through comprehensive empirical analysis, stakeholder interviews, and quantitative performance assessments across 127 organizations spanning 23 countries. This study analyzes how traditional incident management frameworks must be adapted to address the real-time processing requirements, multi-party dependencies, and stringent regulatory obligations characteristic of financial transaction environments. Through mixed-methods research incorporating survey data from 847 payment professionals, longitudinal case studies of 34 critical incidents, and comparative analysis of existing frameworks including ITIL, NIST, and ISO 20000, this research reveals significant gaps between conventional incident management approaches and the specialized needs of payment processing operations. The research identifies critical challenges through factor analysis and statistical modeling, including legacy system integration complexities (correlation coefficient r=0.78 with incident frequency), multi-vendor coordination difficulties (contributing to 64% of resolution delays), regulatory compliance burdens (increasing response time by average 127%), and organizational silos that impede effective incident response (present in 82% of surveyed organizations). Emerging solutions encompass advanced monitoring platforms, artificial intelligence-driven anomaly detection systems, automated response mechanisms, and resilience engineering practices that enable proactive prevention rather than reactive resolution. This study contributes a comprehensive maturity model specifically designed for B2B payment environments, validated through pilot testing across 18 organizations and expert review panels. Organizations achieving higher maturity levels demonstrate statistically significant operational benefits, including 67% reduction in incident frequency, 43% faster resolution times, 89% improvement in regulatory compliance metrics, and 52% enhancement in stakeholder confidence scores.
B2B Payment Systems, Incident Management, Operational Resilience, Maturity Framework, Financial Services Technology, Empirical Research
Automated Cloud Migration Pipelines: Trends, Tools, and Best Practices – A Survey
The rapid advancement of cloud computing has necessitated efficient and reliable cloud migration strategies, as organizations increasingly transition their data to the cloud. This survey examines the evolving landscape of automated cloud migration, highlighting its benefits, risks, strategies, tools, and emerging trends. This survey examines the evolving landscape of automated cloud migration, highlighting its benefits, risks, strategies, tools, and emerging trends. It discusses migration approaches, rehosting, refactoring, re-platforming, repurchasing, and retaining and explores trends in automated migration, including AI-driven workload assessment, risk optimization, multi-cloud and hybrid adoption, and DevSecOps integration. Essential tools such as Azure Data Migration, AWS Cloud Data Migration, Docker, and Kubernetes are reviewed, along with best practices including CI/CD integration, Infrastructure as Code, agile development, cost modeling, and AI-driven adaptive control to ensure robust, scalable, and efficient migration pipelines. The survey identifies key challenges, including data security and privacy risks, regulatory compliance, operational disruptions, migration costs, complexity of legacy applications, and vendor lock-in. To address these, future research directions are proposed, such as AI-driven security and compliance frameworks, automated cost optimization models, intelligent workload assessment, adaptive multi-cloud strategies, and self-healing migration pipelines. By consolidating current strategies, tools, challenges, and research opportunities, this survey provides a comprehensive reference for organizations planning cloud adoption and establishes a foundation for future innovations in secure, efficient, and cost-effective cloud migration solutions.
Cloud Migration, Automation, Hybrid Cloud, Containerization, AI Optimization, Migration Tools
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0
Advanced Strategies for Substation Asset Management: Leveraging Artificial Intelligence and Predictive Analytics
The dependability and resilience of smart grids today are heavily dependent on effective substation asset management and accurate fault identification. Traditional techniques suffer from not being able to handle the complexity, nonlinearity, and high-dimensionality of smart grid data, and hence fault classification in real time is less than optimal. In this study, we propose a hybrid model that combines MLP, LightGBM, and LR to enhance the efficiency, robustness, and accuracy of substation fault detection. The model leverages the capability of deep learning to detect complex, nonlinear patterns and tree-based models to enable effective handling of high-dimensional, structured data. We evaluate the proposed model on the Smart Grid Asset Monitoring the dataset with an accuracy of 98.61%, precision of 99.00%, and recall of 98.66%, which is better than conventional ML and DL approaches. The hybrid system delivers an efficient and scalable real-time solution for substation monitoring towards predictive maintenance and wise decision-making. Our results confirm that integrating heterogeneous modeling techniques can significantly enhance fault classification and detection in smart grid networks, paving the way for even more smarter and trustworthy energy management systems.
Substation Asset Management, AI, Hybrid models, MLP, LightGBM, Sustainability, Smart Grid Asset, Logistic Regression, Machine Learning, Deep Learning
Fine-Tuning MARBERT for Sentiment Analysis in Jordanian Arabic Dialects Using a Synthetic Dialectal Corpus
Arabic Natural Language Processing (NLP) has recently witnessed remarkable progress with the emergence of transformer-based architectures such as AraBERT and MARBERT. However, dialectal variation across the Arab world continues to pose a substantial challenge to model generalization, particularly for underrepresented dialects such as Jordanian Arabic. The present study introduces an efficient end-to-end framework for evaluating and improving sentiment analysis performance on Jordanian social media data. A dedicated corpus of 900 authentic social media posts was collected through a Python-based scraping and preprocessing pipeline, designed to capture Jordanian lexical markers, code-switching with English tokens, emojis, and platform-specific linguistic noise. The dataset was evenly distributed across three sentiment categories positive, negative, and neutral and subsequently partitioned into training, validation, and test sets following an 80/10/10 ratio. We fine-tuned the MARBERT model on this curated corpus using transfer learning and evaluated its performance through macro-averaged F1-scores, precision, and recall metrics. The results indicate a marked improvement in both dialect recognition and sentiment differentiation compared with baseline performance, with the fine-tuned model achieving a macro F1-score of 0.88. This study contributes an openly reproducible pipeline for low-resource dialect modeling, offering methodological insight into sentiment analysis for Jordanian Arabic and establishing a foundation for future validation on larger, human-annotated datasets.
Copyright (c) 2025 Journal of Computer Science and Technology Studies
Strategic Divestitures in HR Technology: A Case Study on the Societal and Organizational Impact of Abbott's StarLIMS Separation
Corporate divestitures in the modern globalized economy need to be advanced coordination involving several business functions, and HR technology is the decisive pillar for ensuring continuity of operations and worker well-being. Abbott's StarLIMS separation undertaking illustrates how strategic workforce transitions can ensure data privacy, business continuity, and worker well-being if implemented with precision and moral foresight. The exercise entailed extensive coordination in multiple countries while establishing Workday-specific infrastructure and maintaining continuous payroll and system access throughout organizational transition. The divestiture is a classic example of the essential nature of HR technology in facilitating smooth workforce transitions while promoting adherence to ethical obligations as well as regulatory compliance necessities. Key insights show the wider social contribution of HR technology infrastructure towards global workforce stability, economic continuity in impacted communities, and sustaining fundamental social pillars during organizational transformation. The project illustrates important issues around equity, inclusivity, and an ethical approach to access management strategies in divestiture planning. The case is a demonstration of how to navigate the tangled levels of regional regulatory contexts and still have sound methods to protect data protection and privacy rights. This experience highlights some important takeaways about divestiture strategies that are future-ready and socially responsible in ways that align with both organizational goals and associated human impacts. This can provide a guiding structure for HR and IT leaders executing complex organizational separations in an increasingly intertwined world of compliance.
Corporate divestitures, HR technology transitions, ethical access management, regulatory compliance, social responsibility
Software-Defined Vehicle Platforms for Safety-Critical Control: Architecture, Updates, and Validation
The current automobile technology is no longer hardware-oriented in designing vehicles but has become Software-Defined Vehicle platforms that have redefined the way cars are designed, built, and supported during their operational life. This change is no longer tied to feature development and inflexible hardware requirements, which allows dynamic over-the-air updates and seamless cross-domain functionality integration. The evolution is to deal with the increasing software complexity and cut down the cost of development, as well as accelerate the time-to-market delivery by having a single common platform across a variety of vehicle types and types of powertrain. Critical application of safety controls requires rigorous compliance with automotive safety standards, especially ISO standards of functional safety and cybersecurity, and it also includes systematized hazard identification and component-based development approaches. Time-Sensitive Networking protocols apply deterministic networking infrastructure offering tight timing control of safety-critical communications with the support of sophisticated encryption and network segmentation schemes. Higher over-the-air update functionalities offer revolutionary advantages, as they make available sophisticated orchestration and validation strategies, the use of digital twins technologies, and rollout strategies, to ensure system integrity and safety of operations throughout the vehicle lifecycle.
Software-Defined Vehicles, Automotive Safety Standards, Time-Sensitive Networking, Over-The-Air Updates, and Vehicle Cybersecurity
Secure Data Transfer and Automated Environment Setup within AWS to Optimize Time and Cost
The increasing complexity of operating multiple AWS accounts regarding production, staging, and testing environments is a major problem in the operational aspects of the modern enterprise that aims at remaining consistent and providing secure data transfer. This article reports a unified automation system based on Jenkins CI/CD pipelines, native AWS services, and principles of Infrastructure as Code to redesign the previously manual processes of environment provisioning into a lean, one-trigger process. The solution fills the key missing links in current methods with the combination of secure cross-account data synchronization and automated environment setup with a six-stage pipeline that includes snapshot creation, data synchronization, cross-account sharing, AMI generation, environment provisioning, and automated testing. With zero-trust security architectures, role-based access control, and automated compliance validation, implementation ensures both the protection standards of an enterprise and, in addition, implementations at a faster rate. The framework composes smart snapshot lifecycle management (serverless Lambda functions) and extensive monitoring (CloudWatch) of all pipeline stages. Implementation within a supply chain application in a retail setting showed significant gains in operational performance, leading to less time to set up the environment and still uphold security compliance and data integrity. The all-in-one solution forms the basis of organizations that aim to streamline cloud infrastructure management using best DevOps practices and automation technology.
Aws Multi-Account Management, Jenkins Pipeline Automation, Infrastructure As Code, Cross-Account Data Synchronization, Cloudformation Templates
The integration of artificial intelligence technologies into network intelligence systems presents unprecedented opportunities for operational enhancement while simultaneously introducing significant ethical challenges that require comprehensive governance frameworks. Modern organizations face increasing pressure to balance technological innovation with responsible deployment practices as AI-driven network surveillance capabilities become increasingly sophisticated and autonomous. This paper examines critical issues in responsible AI implementation, including bias mitigation measures essential for ensuring equitable treatment across diverse network segments and user populations. Network data contains historical patterns that may perpetuate discriminatory decisions when processed by machine learning algorithms without adequate safeguards. Transparency mechanisms constitute fundamental requirements for establishing stakeholder trust and enabling effective human oversight of automated decision-making processes within complex network environments. Explainable AI methodologies become crucial for empowering network administrators to understand algorithmic rationales behind security alerts, configuration recommendations, and traffic prioritization decisions. Privacy protection represents another critical challenge, requiring technical, procedural, and governance controls that preserve individual privacy while supporting legitimate security objectives. Privacy-preserving technologies such as differential privacy, homomorphic encryption, and federated learning offer significant potential for enabling robust monitoring without exposing sensitive user information. Comprehensive governance structures are essential to address end-to-end lifecycle management from initial development to final system decommissioning, incorporating risk assessment protocols, stakeholder engagement mechanisms, and continuous monitoring systems that track ethical performance alongside technical metrics.
Responsible AI, Network Intelligence, Bias Mitigation, Transparency, Surveillance Safeguards, Governance Frameworks