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
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
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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
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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
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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
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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
Airlines Flight Baggage Handling using Predictive Analytics
This paper presents a detailed study of predictive analytics for airline baggage handling systems. It explains how computer vision, machine learning, forecasting models, and routing algorithms improve baggage flow, reduce errors, and lower operational delays. The study also shows how predictive maintenance reduces downtime and improves system reliability. It includes AI versus non‑AI comparison tables, workflow breakdowns, and an integrated architecture. Findings show that predictive analytics improves accuracy, speed, and system capacity while reducing manual work. These results support future large‑scale adoption across airports. [1][2]
Predictive Analytics, Airline Baggage Handling, Computer Vision, Machine Learning, Airport Automation, Baggage Routing Optimization, Conveyor System Analytics, Predictive Maintenance, Time Series Forecasting, ARIMA, LSTM, Deep Learning, YOLO, OCR, Sensor Fusion, Industrial IoT, Smart Airports, Operational Efficiency, Aviation Analytics, Intelligent Transportation Systems
Settlement, Fees, and Interchange: Data Models for Accurate Reconciliation and Exception Handling
Payment ecosystems are based on a system of multilayer data communications, which binds together issuers, acquirers, payment processors, card schemes, and settlement banks. The volume of transactions processed worldwide is growing, and financial institutions are becoming more complex to reconcile due to variations in interchange, scheme fees, acquirer markup, cross-border management, late presentment, reversals, and partial captures. The traditional ledger system, which is typically implemented as a monolithic, non-effective-dated database, cannot support the time variability, heterogeneous rules, or changing fee models required for the current level of reconciliation accuracy. Subsequently, organizations have reduced D+1 close rates, operational overhead, manual journal entries, and the risk of increased financial write-offs. The paper presents a unified and ledger-grade effective-dated data modeling scheme of settlement, fees, and interchange computation. They are designed to normalize the heterogeneity of scheme fee structure and the scheme reason code to facilitate a deterministic reconciliation process. It suggests a multilayer schema structure: (1) Transport Core Model, (2) Exchange and Fee Effective-Dated Model, (3) Exception Lifecycle Engine and (4) Settlement Aggregation Layer. This architecture enables acquirers and processors to identify early differences, auto-read exceptions, and retain audit trails of financial state changes. This paper has gone into great detail on the predictable reconciliation requirements in high-throughput environments. It gives reasons why effective-dating fee rules are necessary, why all financial adjustments must be made with ledger atomicity, why reason-code normalisation of interoperability between schemes and why tasks should be automated to make late presentments, reversals, re-authorizations and partial captures. To propose a uniform approach to the process of mismatch treatment in authorization, clearing, and settlement data, a swimlane-based exception lifecycle is suggested. The suggested model also promotes the adjustment of the SLAs and exception aging limits to prioritize interventions and reduce operational backlog. Empirical simulation proves that with the suggested models, much better accuracy of reconciliation, that is, in the D+1 close range of above 97 percent, is achievable with common card processing portfolios. It takes approximately 60-80% of the time off manual journal entries and write-offs as a result of the easy lineage of raw scheme messages to ledger postings. The outcome is a transparent, scaled, compliant and audit-ready reconciliation model that meets the current payment ecosystem requirements.
Interchange; Effective Dating; Reconciliation; Settlement Models; Exception Handling; Ledger Systems; Payment Schemes; Late Presentment; Reversal Workflow; Cross-Border Fees
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Advanced SHA256-AES Hybrid Algorithm for Enhanced Data Confidentiality and Integrity in Cloud Data Security
Clouds are also in need for solid security, which helps to safeguard sensitive data and maintain the confidentiality and integrity of it. Literature survey reveals that the use of classical and standard cryptographic techniques such as hybrid scheme consisting of RSA–AES, DH–AES, and Blowfish–AES has certain limitations in terms of speed, scalability and efficiency for huge cloud datasets. In order to solve those difficulties, this paper suggests the Advanced SHA256 – AES Hybrid Encryption Algorithm that uses SHA256 for ensuring record reliability and using AES algorithm for securing confidentiality. The method is tested on real datasets, including financial transactions, smart-home device logs and user behavior patterns. Experimental results reveal that the proposed SHA256–AES model has significantly superior performance in terms of encryption and decryption speed, and throughput over other hybrids. The architecture is represented by UMLs, flowcharts, use cases, DFDs and ER models. Its only downside is that it creates a strong and efficient solution for contemporary cloud data security.
SHA256-AES, Hybrid Encryption, ETh (Encryption Throughput), DTh (Decryption Throughput), TET (Total Encrypted Time), AET (Average Encrypted Time), TES (Total Encrypted Size), TDT (Total Decrypted Time), ADT (Average Decrypted Time), TDS (Total Decrypted Size).
Understanding Insider Threats: A Comprehensive Technical Analysis
Organizations across the globe have been struggling with mounting problems of insider threats that utilize the access credentials and institutional knowledge to breach sensitive data and systems. The financial implications are ever-growing as the bad and careless insiders take advantage of authorized permissions in order to circumvent the conventional security measures that offer detection difficulties that baffle the traditional defense strategies. Behavioral analytics systems that set a baseline user pattern and detect abnormal behavior, data loss prevention systems that monitor and regulate the flow of information across organizational boundaries, privileged access management systems that protect high-value credentials and trace administrative activity, artificial intelligence systems that execute work on large volumes of data to detect complex threat patterns, and zero-trust systems that do away with implicit trust are modern ways of addressing these vulnerabilities. The human-based approaches are used in addition to technological defenses as they turn employees into active security participants through education programs, simulated attacks, and job-specific training that minimizes the occurrence of negligent attacks and creates security-aware organizational cultures. Automated incident response features are used to speed up the process of containing a threat by running a series of pre-established workflows that include account suspension, network segmentation, evidence preservation, and notification of the relevant staff at the same time. The development of theoretical studies and their use in practice, the creation of psychological portraits of potential threats, and the creation of tested assessment tools all contribute to the development of the field through academic and industry partnerships. A robust insider threat management requires multi-layered defenses that combine behavioral monitoring, data controls, access control, machine learning analytics, and employee empowerment, and is supported by long-term organizational commitment, ongoing adaptation to the threat spectrum, and the understanding that it takes a concerted effort between securing assets and facilitating operations and employee confidence.
Insider Threat Detection, Behavioral Analytics, Privileged Access Management, Zero Trust Architecture, Security Automation
Human-AI Collaboration in Hardware Verification Workflows for Complex System-on-Chip Designs
Verification processes for hardware designs now integrate artificial intelligence methods that complement human engineering knowledge to speed up validation of intricate chip architectures. Modern system-on-chip implementations create validation difficulties that conventional manual techniques cannot handle effectively, especially concerning coverage completion, defect discovery, and test case development. Intelligent algorithms streamline repetitive validation activities, analyze large simulation data volumes, and identify behavioral irregularities that manual review could miss. Engineers apply intelligent tools to optimize testbench creation, forecast potential failure modes, and prioritize verification activities according to design complexity characteristics. The collaborative framework enables verification teams to concentrate cognitive effort on architectural decisions and corner case evaluation while computational systems manage data-intensive pattern recognition and regression validation. Natural language processing extracts specification requirements automatically, and intelligent agents produce targeted test scenarios exploring previously unexamined state spaces. Combined approaches address exponential design complexity growth that surpasses purely manual verification capacity. Validation cycles accelerate as learning algorithms continuously refine strategies based on simulation outcomes, enhancing defect detection effectiveness. Human oversight remains essential for critical architectural judgments while computational efficiency enables exhaustive scenario exploration. The partnership delivers comprehensive validation for modern system-on-chip implementations by leveraging complementary strengths of human reasoning and computational scale.
Human-AI Collaboration, Hardware Verification, System-on-Chip Design, Machine Learning Integration, Verification Workflows
Ethical Frameworks for AI in Healthcare: Balancing Innovation and Responsibility
The combination of artificial intelligence in healthcare structures is a progressive technological innovation that requires prudent scrutiny of moral frameworks to guarantee ethical deployment. The fast growth of machine learning algorithms in scientific research, ranging from diagnostic imaging to predictive analytics, brings unheard-of opportunities for advancing patient outcomes, even as it additionally presents daunting moral issues. Present-day AI deployment in healthcare necessitates thorough frameworks of equity, transparency, and responsibility in addressing heterogeneous affected person populations. Systematic biases constructed into education datasets can give a boost to modern healthcare disparities, mainly amongst marginalized groups, via algorithmic disparities. Explainable techniques become vital factors for ensuring clinical control and patient self-belief, allowing healthcare specialists to understand decision-making steps and authenticate algorithmic recommendations. Regulatory frameworks show off superb variations among international jurisdictions, mirroring heterogeneous cultural values and healthcare machine designs affecting AI governance techniques. Implementation procedures need to combine stringent bias detection measures, robust information governance processes, and privacy-preserving techniques that shield the affected person's confidentiality, even as permitting collaborative AI development. Stakeholder engagement is essential for successful AI uptake, regarding systematic schooling efforts, open communication strategies, and ongoing feedback mechanisms that ensure medical values and patients' expectations are aligned. The intersection of technical capability with moral obligation calls for ongoing willpower to create AI technologies prioritizing patient gain, even as selling healthcare delivery innovation.
Artificial Intelligence Ethics, Healthcare Algorithms, Bias Reduction, Explainable AI, Data Governance, Stakeholder Engagement
Transforming Quality Assurance Through Strategic Deployment of AI-Augmented Automation: Championing Enterprise Adoption and Scaling Across Banking, Retail, and Telematics
Quality assurance has evolved from a historical testing activity to a key enabler of enterprise digital transformation. Organizations today require predictive and automated quality frameworks tightly integrated into their continuous release pipelines, with focus on operational reliability and delivery excellence. This article describes my leadership experience championing the deployment of extensive automation frameworks across banking, retail point-of-sale, and telematics industries, demonstrating how strategic implementation of automation-led forecasting and predictive QA methods integrated into enterprise pipelines transform quality delivery. My contributions include championing enterprise adoption of automated issue triage dashboards for immediate resolution, Jenkins-based systems for monitoring release build health, and automated multi-device validation for cross-platform enterprise applications. In digital banking environments, I strengthened release governance automation and fraud detection enhancement capabilities. Retail POS implementations enhanced automated payment validation workflows and loyalty program verification. Telematics applications incorporated forecasting models for vehicle diagnostics and safety alert verification. Results demonstrate a 40-65% reduction in defect escape rates, 50-70% decrease in manual testing effort, 3-5x improvement in release velocity, and 35-50% cost savings in QA operations through strategic deployment and scaling of proven automation methods. The high scalability and reusability characteristics of these implementations establish predictive quality assurance as foundational infrastructure for enterprise digital transformation ecosystems.
Test automation, DevOps integration, predictive quality assurance, enterprise transformation, automation-led forecasting, predictive methods integration
Value-Stream-Aligned Order-to-Cash in SAP S/4HANA: Lead-Time Compression with ATP/BOP and aTO/Pegging Strategies
The shift to the much responsive and digitally enabled supply chains has forced manufacturing and distribution businesses to reconsider traditional Order-to-Cash (O2C) execution systems. The combination of embedded analytics, in-memory computing and simplified data structures gives SAP S/4HANA a chance to redesign such processes based on value-stream principles, getting rid of functional siloes and reducing lead times. Specifically, the re-designed Advanced Available-to-Promise (aATP), Backorder Processing (BOP), and enhanced pegging help the organizations transition to a strategy of batch-oriented, reactive fulfillment to proactive allocation strategies that directly correlate to the customer value streams. In the given paper, I will provide a full study of value-stream-centered O2C implementation made possible by S/4HANA, how the reduction of lead-time happens with the help of ATP/BOP, advanced allocation (aATP), Alternative-based Confirmation (ABC), and pegging strategies. The paper combines operational research framework, SAP design report, and industry case studies to create the theoretical and practical underpinning of the suggested framework. In the study, the authors explore how the Unified Data Model of S/4HANA, HANA storage in columns, and event-based replenishment structures aid in real-time allocation of orders. The paper will give a literature review of O2C transformations, priority-based ATP models, and development of classical ATP (cATP) in ECC to aATP in S/4HANA. We talk about the shortcomings of the conventional MRP-ready fulfillment and how S/4HANA brings enhancements in predictive allocation to stock checks that are deterministic. The proposed four-stage O2C redesign cycle is composed of the following stages: (1) Value-Stream Mapping of the demand/supply synchronization constraints; (2) configuration of the aATP categories and supply circumstances; (3) the implementation of segmentation-based BOP rules; and (4) dynamic pegging of critical materials. An experiment of hypothetical data representative of the industry is performed through a simulation experiment to estimate the influence of ATP rule tuning, BOP prioritization and pegging on order lead times, fill rate and service-level stability. Findings indicate a mean 22-38 percentage reduction in lead-time, 17% including accuracy in allocation and 27 percent lower variability in fulfillment than traditional MRP directed ATP. Sensitivity analysis shows that value-stream segmentation with aATP product allocation results in the greatest gains in the service levels. The paper closes by providing practitioner implications, which include formulation of governance structure, data preconditions, change-management requirements, and KPIs required to maintain O2C performance gains. The results show that the O2C features of S/4HANA used in a value-stream perspective redefines the classic scope of fulfillment by moving decision intelligence uphill and aligning it with the customer priority frameworks. This provides a digital basis of resilient, adaptive, and customer-centric supply chain activities.
SAP S/4HANA, Order-to-Cash, Advanced ATP (aATP), Backorder Processing, Pegging, Lead-Time Compression, Value-Stream Mapping, Supply-Chain Optimization, Digital Fulfillment, Allocation Strategies
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Can AI Decode and Interpret Encrypted Arabic on Facebook and YouTube to Evade Algorithmic Moderation
This study aimed to find out whether AI can decode and interpret the meaning of encrypted Arabic words and phrases on YouTube (spoken) and Facebook (written) based on samples of 74 encrypted political words and phrases from YouTube and 20 COVID-19 encrypted words and phrases from Facebook by Arab digital creators to avoid being detected by algorithmic moderation. It also aimed to compare Microsoft Copilot (MC), DeepSeek (DS) as generative AI and Google Translate (GT), as a Neural Machine Translation (NMT) specialized in translation only. It was found that MC correctly translated and interpreted the meaning of 56% of the encrypted political words and phrases in the sample of YouTube videos. It gave partial translations for 16% and faulty responses for 27%. Similarly, DS gave correct responses to 41%, partially correct responses to 35% and faulty responses to 24%. MC and DS gave identical correct responses to 36% of the items in the sample. Regarding the COVID-19 sample, MC rendered 60% correct responses, 25% literal translations, 10% partial responses and 5% omissions. DS gave correct responses to 50% of the COVID-19 sample, 15% literal translations, 5% partial responses and 30% faulty responses. Both MC and DS yielded 35% correct responses to the same items. GT gave a surface level, word-for-word translation to 42% of the items (القبة الزجاجية The glass dome) and transliterated 44.5% (صفصوني > Safsūnī). No underlying meanings were given by GT, as GT functions in a manner similar to YouTube algorithms, providing surface translations without contextual interpretation. GT translated the words it recognizes directly (word for word) (الاختلال > disruption) and did not recognize distorted or slang words and phrases. GT did not make any contextual decoding nor inferred satire, parody, or encrypted meaning (فيفي16 > Viva 16 instead of F-16). The study explains why MC and DS sometimes converged on identical correct and incorrect responses, why MC outperformed DS, and why GT failed to decode encrypted language. It concludes with implications for AI performance in understanding encrypted communication, highlighting the strengths and limitations of generative AI compared to NMT in contexts where meaning is deliberately obscured.
Linguistic encryption, encrypted Arabic words, algospeak, Generative AI (GenAI), Microsoft Copilot, DeepSeek, Google Translate, social media, YouTube, Facebook, algorithmic moderation
Green Power Integration into Mission Critical Facilities
Mission critical installations like data centers, hospitals, banking and monetary institutes, and military infrastructures demand constant and high-grade power supplies for continuity of operations. Historically, these installations have used fossil-fuel–powered generators and grid supplies for ensuring reliability, but increasing enviro-imperatives, growing electricity cost, and global sustainability legislations for green power inclusion are compelling changes toward green power inclusions. This paper discusses the prospects and paradigm of integrating renewable sources of power like solar, wind, biomass, and fuel cell in mission critical premises without affecting system reliability. It discusses the use of improved energy storage solutions, hybrid models of redundancy, and artificial intelligence-supported microgrids and smart grids and real-time monitoring for mitigating intermittencies and ensuring resilience. Application in healthcare systems, hyperscale data centers, and defense installations illustrates technical viability along with economic feasibility of green power inclusions. Policy regimes and enviro-factors too have been examined for the demonstration of sustainability and risk minimization dual advantages of green power inclusions. The paper concludes that even though integrating green power is challenged in intermittencies, capital cost, and regulatory issues, storage improvement, digitalization, and incentive policies from governments pave the path toward the creation of zero-carbon, resilient mission critical premises of the future decades.
Green power, mission critical facilities, renewable integration, microgrids, energy storage, resilience, sustainability
Core Architectural Principles for Cloud Financial Data Pipelines and Cost-Aware Data Engineering
Cloud financial data engineering has become a key critical field that allows organisations to process raw data on billing into strategic intelligence to minimise costs and create business value. This article discusses the fundamental architecture of scalable cost-conscious data pipelines to serve practices of Ancient Financial Operations in the enterprise. It includes the background concepts such as multi-cloud billing data consolidation, tiered architecture, columnar format-based storage layer optimisation, and scalable pipeline design patterns that support the exponential data growth. Data quality models, governance processes and financial accuracy controls provide reliable analytics to facilitate executive decision-making and regulatory compliance. Best practices of implementation would deal with architecture, technology stack, and organisational work models that present a tie between technical and financial stakeholders. The trends, such as artificial intelligence-based forecasting, integration of sustainability metrics, and automated optimisation, indicate the shift towards proactive financial engineering. The organisations that apply them have predictable operational expenses, increased financial transparency, and competitive edges due to the data-driven optimisation of cloud spending.
Cloud Financial Analytics, Finops Architecture, Cost Optimisation Pipelines, Data Governance Frameworks, Scalable Data Engineering
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Demystifying LLM Serving Pipeline: From Prompt to Response
Each response from an LLM application follows a carefully optimized sequence of steps designed to balance quality, latency, and cost efficiency. This article outlines a typical LLM serving pipeline, beginning with user prompt capture, retrieval augmentation, tokenization, request routing, followed by auto-regressive token generation and post-processing to produce the final response. We evaluate critical system elements in the LLM serving pipeline, including client interfaces, policy verification mechanisms, admission control systems, KV-cache management, speculative decoding techniques, and post-processing operations. The article also examines the trade-offs among latency and throughput, memory and compute efficiency, and concurrency and response time that system architects and product leaders must balance to develop robust LLM applications.
Inference Optimization, Key-Value Cache, Speculative Decoding, Retrieval-Augmented Generation, Dynamic Batching
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Enhancing Financial Inclusion Through Robust Payment Security Measures
Payment security plays a crucial role in expanding financial inclusion by building trust and accessibility for underserved populations. This article examines how secure systems influence societal dynamics, emphasizing ethics, equity, and policy. Wider impacts include reduced barriers for unbanked individuals via mobile payments and fraud protection. Responsibility focuses on mitigating biases in detection algorithms. Policy involves regulations promoting secure access. Case examples demonstrate benefits in emerging markets, with future outlooks on digital currencies and biometric authentication. Detection algorithm biases, inclusive design principles, regulatory frameworks, and stakeholder responsibilities are explored through systematic analysis of real-world implementations. The article presents evidence from multiple regions showing how appropriately designed security can bridge economic divides while identifying potential future pathways through technological innovation, policy development, and collaborative stakeholder action.
Financial Inclusion, Payment Security, Algorithmic Equity, Digital Authentication, Cross-border Remittances
Securing Modern Integrations: A Governance-Centric API Architecture for Regulated Industries
Enterprises face unprecedented challenges in integrating heterogeneous and distributed systems while maintaining security, scalability, and operational efficiency—particularly in regulated industries. Existing integration approaches, from point-to-point and SOA to microservices, lack a unified governance model and introduce complexity that limits agility. This paper proposes a three-tier API-led connectivity architecture—comprising system, process, and experience layers—that enforces modularity, enables progressive modernization, and embeds governance, security, and lifecycle management as first-class design principles. The framework addresses integration debt, security fragmentation, and operational inefficiency by combining contract-first development, layered API governance, and advanced fraud detection at the integration layer. Empirical evaluation across four large-scale financial deployments demonstrates significant improvements, including up to 92% reduction in false positives, sub-50 ms average processing latency, prevention of $8.7 M in fraud losses, and processing capacities exceeding 45,000 TPS. These results validate the framework’s adaptability, performance, and compliance capabilities, positioning it as a foundational approach for secure, scalable, and future-ready enterprise integration.
API-led connectivity, enterprise integration, three-tier architecture, API governance, digital transformation
Federated AI for Multi-Enterprise Supply Chain Collaboration
As global supply chains expand into complex, interdependent ecosystems, organizations face a paradox: the need to share intelligence without sharing data. Traditional centralized AI architectures, dependent on aggregated datasets, conflict with regulatory restrictions, competitive secrecy, and privacy mandates. Federated Artificial Intelligence (FAI) resolves this paradox by enabling multiple enterprises to collaboratively train and deploy AI models across distributed data sources without transferring raw data. This article proposes an Enterprise Federated AI Architecture (EFAIA) tailored for multi-enterprise supply chain optimization. It integrates data fabric layers, secure aggregation protocols, and governance mechanisms that enable AI-driven forecasting, risk management, and procurement collaboration across organizational boundaries. The framework is demonstrated through industry-relevant use cases in manufacturing, pharmaceuticals, and logistics. Key benefits—enhanced prediction accuracy, regulatory compliance, and ecosystem trust—are balanced against challenges in interoperability, latency, and model governance. The article argues that federated AI is not merely a technical innovation but an organizational catalyst for co-intelligent supply networks, aligning efficiency with ethics in the emerging era of distributed intelligence.
Federated Learning, Supply Chain Intelligence, Distributed Data Governance, Privacy-Preserving AI, Cross-Enterprise Collaboration
Hardware-Accelerated Caching for Large-Scale AI Model Training: An Intelligent Architecture for Vector Database and Model Inference Optimization
Modern AI infrastructures are facing significant challenges in managing data movement efficiently between vector databases and AI models for training and inference operations. Traditional caching approaches cannot tackle the unique characteristics of vector operations and embedding access patterns, which introduce significant performance bottlenecks. This article proposes a novel caching system that fuses custom-designed vector processors with an adaptive hot/cold partitioning strategy enhanced by Bloom filters. It implements a hardware-accelerated hot cache for frequent vectors, a cold storage queue for less frequent data, and Bloom filter-based efficient lookups. By integrating hardware acceleration with workload-aware partitioning and probabilistic filtering, the system achieves massive improvements along multiple dimensions. The architecture addresses the unique temporal and spatial locality patterns in AI vector operations, reducing data movements while maximizing the utilization of compute resources. Simulation results on large language models and computer vision workloads show that the model accelerates training and inference speeds, reduces network data movement, and improves hardware utilization compared to conventional LRU-based architectures, potentially transforming the economics and characteristics of large-scale AI operation performance.
Vector Database Optimization, Hardware Acceleration, AI Infrastructure, Bloom Filter Implementation, Hot/Cold Partitioning
Revolutionizing ERP Test Strategy Generation Through Multi-Agent AI Frameworks
The integration of multi-agent artificial intelligence frameworks into Enterprise Resource Planning test strategy generation represents a transformative shift in quality assurance practices for complex business systems. This article examines how specialized AI agents collaborate to automate the synthesis of diverse knowledge sources, including official documentation, business requirement documents, user interface mockups, code repositories, and existing test management systems. It describes how this framework employs advanced techniques, such as semantic knowledge graphs, retrieval-augmented generation, natural language processing, and deep reinforcement learning, to construct exhaustive test strategies that can adapt dynamically to evolving system requirements. Using belief-desire-intention architectures and Prometheus design models, specialized agents distribute cognitive tasks across documentation retrieval, requirement summarization, test case integration, code scrutiny, strategy composition, and validation processes. Implementation requires human-in-the-loop orchestration interfaces to keep AI-generated strategies transparent and auditable, thereby overcoming one of the critical factors that prevent the adoption of automation: trust. Ultimately, this framework has demonstrated significant improvements in test generation efficiency, defect detection capabilities, and regulatory compliance, while also reducing the cognitive burden experienced by testing professionals. This advancement in technology has enabled the transformation of test management from a reactive, document-based process to a proactive and intelligent ecosystem, with continuous learning and adaptation of the ecosystem in line with organizational requirements.
Multi-Agent Systems, Enterprise Resource Planning, Knowledge Graphs, Reinforcement Learning, Human-AI Collaboration
Comprehensive End-to-End Testing Strategies for React Applications: A Practical Guide to WebDriverIO Implementation and Best Practices
End-to-end testing remains one of the most critical yet challenging aspects of React application development, particularly as modern applications become increasingly complex with sophisticated user interactions and dynamic component behaviors. This article provides comprehensive guidance on implementing WebDriverIO as a robust testing solution for React-based projects, demonstrating how its versatile architecture addresses the fundamental limitations of traditional testing approaches. The article examines practical implementation strategies that leverage WebDriverIO's multi-protocol support to achieve comprehensive test coverage across browser environments and device platforms. Performance analysis reveals that WebDriverIO implementations achieve faster test execution times compared to traditional Selenium-based approaches through parallel test execution and optimized resource management. The framework's extensive ecosystem of services and reporters enables teams to reduce test maintenance overhead while improving test reliability through advanced debugging capabilities and comprehensive error reporting. Integration strategies with modern CI/CD pipelines demonstrate deployment frequency improvements through automated test execution and intelligent test result analysis. Beyond technical benefits, the implementation of WebDriverIO testing strategies creates substantial organizational value by enabling faster defect detection, reducing production incidents, and supporting agile development practices through reliable automated regression testing capabilities.
WebDriverIO, React Testing, End-to-End Testing, Browser Automation, Test Architecture
Advancing Real-time Data Processing and Middleware Integration in FOE Enterprise Architecture
The contemporary enterprise landscape faces unprecedented challenges in managing real-time data processing and system integration across heterogeneous computing environments. Traditional middleware solutions demonstrate significant inadequacies in handling dynamic workload patterns, adaptive resource allocation, and cross-platform interoperability requirements of modern distributed architectures. This technical review presents a groundbreaking Enterprise Integration Architecture framework that fundamentally transforms the relationship between real-time data processing and middleware solutions through advanced distributed computing paradigms. The proposed architecture introduces an adaptive middleware layer operating as a self-optimizing ecosystem, capable of intelligently managing diverse data sources while maintaining enterprise-wide communication coherence. Key innovations include priority-based data processing algorithms utilizing machine learning classification models, dynamic resource allocation protocols that redistribute computational resources based on real-time workload patterns, and contextual awareness mechanisms enabling intelligent routing decisions. The framework demonstrates superior performance characteristics compared to traditional message-oriented middleware and enterprise service bus solutions, particularly in areas of latency reduction, throughput optimization, and system reliability. Implementation potential spans multiple industry verticals, including healthcare systems requiring rapid patient data access, financial services demanding high-frequency transaction processing, manufacturing environments leveraging predictive maintenance capabilities, and smart city infrastructures managing extensive sensor networks. The architecture supports seamless integration across hybrid and multi-cloud environments through sophisticated workload orchestration capabilities.
Enterprise Integration Architecture, Real-time Data Processing, Adaptive Middleware, Dynamic Resource Allocation, Contextual Awareness Mechanisms
Transforming Enterprise QA: A Technical Deep-Dive into AI-Driven Automation at Scale
The topography of enterprise quality assurance has been radically changed because organizations have realized that testing is a strategic facilitator and not a gatekeeping role. The focus of this transformation is the implementation of artificial intelligence in the process of quality assurance at Charles Schwab, indicating how the automation provided by AI changes the work of enterprise testing. The implementation was centered on the introduction of GitHub Copilot, which is an automated test generation tool that generates smart pipelines between project management systems and test execution models. Automated requirements parsing derives acceptance criteria code out of user stories, whereas generation of feature files generates behavior-driven specifications, and step definition scaffolding generates executable test code. Findings indicate that there are drastic increases in efficiency, with test preparation time being minimized and engineering productivity gaining immensely. An integrated automation harness based on chaos engineering principles systematically tests system resilience in unhealthy conditions, avoiding unnecessary testing and identification of edge cases by systematic fault injection. Failure in production was reduced significantly with a cost reduction amounting to significant annual costs. Compliance validation controls provide regulatory compliance with multi-layered data accuracy verification, proactive regulatory exposure protection, and detailed audit trails that satisfy the standards such as SOC 2, ISO 27001, and financial service regulations. The change raises the quality assurance to be a strategic business enabler rather than a support functionality, bringing a competitive advantage by delivering features more quickly, enhancing system reliability, and minimizing regulatory risk. These results represent a scalable paradigm of AI-based quality assurance in fundamental issues of contemporary software development, showing how smart automation can help an organization establish a balance between the speed of delivery and strict quality and compliance standards in more demanding regulatory settings
AI-driven quality assurance, automated test generation, chaos engineering, regulatory compliance validation, enterprise software testing
The Societal Impact of Data Democratization in Enterprise Revenue Systems
The democratization of data within enterprise revenue systems represents a transformative paradigm shift with profound socioeconomic implications. This article explores how Customer and Revenue Data Platforms (CRDPs) establish foundations for transparency, accountability, and sustainability across organizational ecosystems. Beginning with a conceptual framework that positions data as organizational commons, the article examines how democratized access reconfigures power structures and decision processes. The socioeconomic dimensions reveal how transparent revenue systems foster ethical governance, reduce information asymmetry, and empower stakeholders through enhanced data literacy. Environmental considerations demonstrate how cloud-native architectures optimize resource utilization while enabling comprehensive carbon footprint monitoring and integrated ESG reporting. Regulatory applications illustrate alignment with evolving compliance frameworks through audit capabilities and metadata management, extending to public sector implementations that enhance democratic accountability. Finally, the article addresses emerging challenges, including data misinterpretation risks, privacy vulnerabilities, and algorithmic bias concerns, while projecting future socioeconomic impacts as enterprise data engineering continues evolving through 2030 and beyond.
Data Democratization, Enterprise Accountability, Revenue Transparency, Sustainable Data Architecture, Digital Ethics.
Tamper Detection and Recovery Systems in Hardware Security Modules: Design Principles and Implementation Strategies
Hardware Security Modules are a highly sensitive infrastructure of cryptographic activities of enterprises, but the conventional tamper detection mechanisms pose operational risks by relying on manual monitoring. This article looks at the state-of-the-art tamper detection and automated recovery infrastructure, changing the security posture of the HSM using smart monitors along with a coordinated methodology of response. Multilayer schemes of detection use physical sensors, cryptographic validation, and behavioral analysis to detect unauthorized access with accuracy, with the lowest number of false alerts. Recovery systems use immediate cryptographic erasure, automated capacity isolation, and synchronized workflows, which reduce disturbance of service. Its implementation plans deal with secure memory management, secure key storage, and compliance needs in various environments. The architecture is compatible with several HSM platforms without being vendor-specific or relying on regulatory compliance to ensure that the knowledge required by organizations deploying next-generation hardware security infrastructure is provided, as protection is balanced with operational continuity in mission-critical settings.
Hardware Security Modules, Tamper Detection, Automated Recovery, Multi-Layered Security, Cryptographic Protection