Frontiers in Computer Science and Artificial Intelligence
Frontiers in Computer Science and Artificial Intelligence (FCSAI) is an international, peer-reviewed, monthly, open-access journal dedicated to advancing research and innovation in all areas of computer science and artificial intelligence. The journal provides a global platform for researchers, scholars, practitioners, and industry professionals to disseminate original and high-quality scientific contributions that address contemporary challenges and emerging trends in the digital world. FCSAI publishes cutting-edge research articles, review papers, case studies, technical reports, and short communications covering a broad spectrum of topics including—but not limited to—machine learning, deep learning, data science, natural language processing, computer vision, robotics, cybersecurity, software engineering, cloud and edge computing, intelligent systems, human–computer interaction, and emerging AI applications across disciplines. The journal is committed to promoting ethical research practices, fostering innovation, and encouraging interdisciplinary collaboration. Through a rigorous double-blind peer-review process, FCSAI ensures the publication of impactful, reliable, and scientifically sound research that contributes to the advancement of knowledge in computer science and artificial intelligence. As a monthly journal, FCSAI offers frequent publication opportunities, rapid dissemination of new findings, and up-to-date coverage of evolving research trends. The journal welcomes submissions from authors around the world and provides timely review, high visibility, and broad dissemination of published works. As part of Al-Kindi Center for Research and Development, UK, the journal benefits from a strong academic network and a commitment to supporting global scholarly communication.
Al-Kindi Center for Research and Development
Integrating Business Intelligence and Data Analytics to Optimize End-to-End Supply Chain Performance in U.S. Manufacturing and Logistics Networks
The growing complexity of the manufacturing and logistics networks in the United States has increased the necessity of using data-driven decision-making across the supply chain. Business Intelligence (BI) and Data Analytics (DA) have become essential facilitators toward enhancing visibility, coordination, and performance in end-to-end supply chain activities. This paper looks at the way that the conglomeration of BI and DA tools can streamline the performance in the supply chain of manufacturing and logistics networks in the United States. The article utilizes a conceptual and analytical framework based on supply chain management and information systems theory in exploring how descriptive, predictive, and prescriptive analytics can be used to improve demand forecasting, inventory management, transportation planning, and operational resilience. According to the findings, organizations that use integrated capabilities of BI and DA gain access to better real-time visibility, lower operational costs, increased responsiveness to disruptions, and increased informed strategic decisions. The paper also identifies the major implementation barriers, such as data silos, systems interoperability, and organizational preparedness. Combining existing practices and performance results, the paper will add research and practical significance to the scholarly literature on how data-driven intelligence can change end-to-end supply chain performance in the U.S. manufacturing and logistics ecosystem.
Business Intelligence; Data Analytics; Supply Chain Performance; Manufacturing Systems; Logistics Networks; End-to-End Supply Chain; Data-Driven Decision-Making; U.S. Manufacturing
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Integrating Predictive Analytics and Business Intelligence for Enterprise-Scale Cybersecurity Threat Detection in the United States
Cybersecurity attacks against enterprises in the United States have increased in terms of intensity, rate, and sophistication, thereby giving rise to a pressing need for the development of predictive defense tools capable of anticipating and detecting potential threats in advance. This paper works towards the development of an end-to-end business intelligence (BI) modeling platform utilizing the strengths of machine learning, anomaly identification, and real-time log analytics to formulate predictive models and provide recommendations to mitigate potential cyber security threats in the enterprise environment. Using a dataset represented by a collection of representative enterprise environment network log entries, user login activity, and system events, the platform makes judicious utilization of supervised and unsupervised learning approaches such as the application of the gradient boosting algorithm, random forests, and autoencoders to recognize precursor signs of hacking activities, malware diffusion, and insider attacks in the enterprise environment. The designed BI platform offers the unique capability of interpreting results from the predictive analytics components and converting them into a form of actional visual analytics to enable security teams to assess risks and automate the process of response to security breaches in the enterprise environment. The results validate the efficacy of the combined BI and machine learning platforms to enhance the threat detection rate by a maximum margin of 28% in comparison to the application of traditional rule-based systems while reducing mean time to detection and mean time to response times in the enterprise environment. The paper forecasts the augmentation of enterprise resilience and aligns with the strategic objectives of the United States regarding the objectives of the NIST AI Risk Management Framework and the National Cybersecurity Strategy. Future work will relate to large-scale implementation and the application of generative AI to enhance the security models designed to predict security breaches in the cloud environment.
Business Intelligence (BI) Systems, Cybersecurity Threat Prediction, Machine Learning–Based Detection, Anomaly Detection Models, Enterprise Security Analytics, Predictive Risk Mitigation
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Specific Linguistic Questions that Artificial Intelligence (AI) Cannot Answer Accurately: Implications for Digital Didactics
This study aimed to examine the kinds of specific linguistic questions that AI systems cannot answer accurately, drawing on daily scholarly interactions with Copilot (MC), DeepSeek (DS), Google Translate (GT), Gemini, Monica, rather than artificial test prompts. It also seeks to understand why AI makes mistakes and whether the nature of user questions influences AI performance. In addition, it aims to analyze the nature of these failures through a linguistic and a pedagogical lens to offer recommendations for students and researchers on how to critically evaluate AI output in language and translation tasks. For those purposes, a sample of 50 specific linguistic and translation questions that the author asked to MC, DS, GT, Gemini & Monica between 2023 and late 2025 was collected and anlayzed. The questions were classified into the following categories: phonology, transcription, morphology, lexical questions, pragmatics and culture, explanation or translation of Arabic grammatical terms, books AI cannot fully identify, telling a story in Arabic classical literature, converting a hand-written text to a typed text, translation of technical terms, metaphorical expressions and metonyms, and bibliographic, and scholarly workflow issues. Results of the 5 AI responses to the 50 linguistic and translation questions and tasks revealed recurrent AI shortcomings across diverse domains: phonology and transcription errors, morphological and lexical inaccuracies, pragmatic and cultural misinterpretations, and faulty explanation of Arabic grammatical terms. AI systems failed to identify certain books, struggled with classical storytelling in Arabic classical literature, and could not convert handwritten to a typed text. Technical terminology, metaphorical expressions, and metonyms were often mistranslated, while bibliographic and scholarly workflow tasks showed fabricated references and gaps in organizing references. Collectively, these errors underscore AI’s tendency toward surface-level processing at the expense of linguistic depth and cultural fidelity. Findings of this study suggest directions for refining AI translation models and integrating critical AI literacy into language education. This study also contributes to ongoing debates on the limitations of AI and the use of AI in linguistics by highlighting the pedagogical risks of uncritical reliance on AI output. Despite these small flaws and issues, AI is capable of performing unlimited linguistic tasks with lightning speed and impressive content that humans are incapable of.
Artificial Intelligence (AI), AI linguistic limitations, AI translation limitations, AI responses to author initiated questions, AI inaccuracies, causes of AI errors, improving AI responses, translation didactics, studetnts' linguistic skills, students' searching skills.
Data Lineage and Metadata in Payment Ecosystems: Auditability and Regulatory Readiness across the Life Cycle
Current payment ecosystems create data at high velocity, in small granularity, that needs to satisfy significantly higher regulatory demands in the areas of transparency, auditability, and operational assurance. Nevertheless, the column-level transformations, defining data-handling policies, and timely visibility of legacy metadata platforms and fixed documentation practice are not provided during the audits and regulatory assessments. In the given paper the authors suggest an active metadata-based platform that provides end-to-end, column-based data lineage, between ingestion and reporting, which allows behind-real-time governance of the payment data life cycle. The framework incorporates policy-as-code to automate policy access-control, retention rules and segregation-of-duty checks to do away with manual governance steps and promote uniform enforcement between policy-cycles. They include impact analysis features to measure the downstream impact of schema or logic modification to reduce operational and compliance risks associated with changes to a significant degree. The lineage objects are mapped to a unified business glossary containing the standardized definitions throughout systems as well as enhancing traceability to auditors and regulators. The results of implementation show that measurable improvements can be observed, such as a reduction in the number of audit results, the shortening of the regulation response cycle, and the increase in control evidence because verifiable lineage screenshots and metadata artifacts are generated. The work adds a scalable architecture and validation procedure to attain audit-ready, regulator-congruous information management in the contemporary pay surroundings.
Data Lineage, Active Metadata, Policy-as-Code, Payment Ecosystems, Regulatory Compliance, Audit Readiness, Metadata Governance.
Operationalizing Predictive Modeling in Clinical Workflows: Design, Integration, and Validation of Decision Support Mechanisms within U.S. Healthcare Infrastructure
Readmission is one of the greatest measures of healthcare quality and cost-effectiveness in the United States of America. Diabetes mellitus as a chronic disease has its own peculiarities: it is the disease whose treatment implies long-term prognosis that might be complicated by a range of issues; therefore, it leads to high risks of readmission. Determining what is associated with such readmissions is essential in how patient care is improved and how healthcare spending can be reduced. The present paper involves the analysis of the Diabetes 130-US Hospitals for Years 1999-2008 dataset on the basis of an extensive investigation of the visual data analysis and descriptive statistics. The coding is done to determine the important demographic, clinical and treatment-related factors as related to 30-day readmission in patients with diabetes. This study method uses data visualization techniques, which are a frequency distribution, a correlation heat map, and a comparison chart that demonstrate critical trends and patterns in the dataset. Among the most important results of the research, there are the findings showing the consistency of associations of readmission risks and such factors as the age of a patient, the time spent in a hospital, the way how a patient was admitted and whether a patient had a previous experience of being in hospital. The use of medication, especially when it comes to the subject of insulin and oral hypoglycemic drugs, also shows significant associations with the rates of readmission. These visual results assist a participant to comprehend the factors that can make the situation riskier, thus forming the basis of using evidence-based therapy in practice. This study highlights the range that visual analytics has in healthcare research in the sense that it defines meaningful relationships between data that can facilitate the decision making process in the medical field and policy making. A thorough understanding of these visual patterns can lead medical professionals in formulating specific interventions to ensure that the number of unnecessary readmissions among diabetics is reduced. This research provides a comprehensive examination of the dataset in visualization and description terms that will ultimately support the general mission of improving quality of care and solutions to more cost-effective healthcare management in the context of the U.S. hospital system.
Diabetes Mellitus, Hospital Readmission, Inspection of Visual Data, Electronic Healthcare Records (EHR), Clinical Decision Support and Exploration of Healthcare Data
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Predictive Capacity Planning and Cost Optimization for Polyglot Cloud Databases Using Machine Learning
Polyglot persistence has redefined the data management environment and now organizations can mix various database paradigms under the same cloud ecosystems. Nevertheless, this architectural flexibility brings along new operational issues- especially during capacity planning, cost optimization and predictability of performance. The given paper suggests a machine-learning-infused model of predictive resource distribution and real-time cost optimization in polyglot cloud database settings. Basing on the works related to cloud economics, NoSQL management, ML-based tuning, serverless computing, and hybrid data lakes, this study combines the time-series forecasting, cost-aware optimization, and performance modeling into a single system. Simulation experiments on heterogeneous workloads show that the accuracy of resource utilization, cost variability, and system performance are greatly improved over heuristic-based scaling, which has been used traditionally. The findings indicate that an AI-based system of dealing with polyglot clouds data bases in the contemporary distributed setting is feasible.
Polyglot persistence; cloud data; cost optimization; capacity planning; machine learning; resource prediction; time series prediction; work load modeling; non-homogenous data systems
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ML-Driven Performance Tuning Framework for Heterogeneous Unified Databases
Modern organizations increasingly operate in heterogeneous database environments encompassing relational systems such as Oracle, SQL Server, and PostgreSQL, alongside NoSQL platforms such as MongoDB and Cassandra. This diversity introduces significant challenges in performance tuning because each engine exhibits distinct workload behaviors, tuning parameters, and performance indicators. Traditional manual tuning approaches are limited in terms of scalability and effectiveness, particularly under dynamic, high-volume workloads. This study proposes a unified, Python-based machine-learning framework designed to automate performance tuning across heterogeneous databases. The framework incorporates cross-database metric collection, feature engineering, supervised and unsupervised learning models for bottleneck prediction, and an intelligent recommendation engine that generates actionable strategies. Experiments conducted using benchmark workloads and real-world performance traces demonstrated notable reductions in query latency, improvements in throughput, and increased system stability across all supported database engines. Comparative analysis indicates that the ML-driven approach outperforms vendor-native recommendations and manual heuristics in terms of both accuracy and tuning impact. The findings highlight the feasibility and effectiveness of adopting a unified ML-driven tuning architecture for diverse database environments. Future extensions include integrating reinforcement learning, expanding support for additional database paradigms, and developing automated rollback-safe execution workflows.
Machine learning; database tuning; heterogeneous databases; performance optimization; bottleneck prediction; latency
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Real-Time Monitoring of Patient Adherence Using AI
Patient compliance with prescribed therapies remains a problem in healthcare practice, especially in chronic disease management. Non-adherence up to the point where it may result in suboptimal response to the treatment, more hospitalizations and higher expenditures on health care. The latest advances in Artificial Intelligence (AI) and wearable technologies have created real-time adherence monitoring systems that work proactively to track patient behavior and medication intake. This paper presents a detailed architecture of an AI-based adherence monitoring framework that involves the deployment of wearable sensors, secure transmission protocols, edge processing nodes, and cloud-based analytics engines. The proposed system can capture important health metrics like heart rates, motions, medication ingestion, and sleep patterns through smart bands and IoT devices. Data is encrypted, sent to the cloud, and analyzed by machine learning modes (CNN, LSTM, and XGBoost) that identify non-adherence in real time based on behavioral patterns. Feedback is received from the patients and clinicians through mobile alerts, dashboards, and EMR/EHR integration. Experimental tests with real-world data show out-performance in predictive accuracy (up to 97.7%) and increased adherence rates (6.1%–32.7%) to a traditional approach. This research demonstrates the promise of AI in transforming adherence monitoring from a passive process to a dynamic, intelligent and patient-focused mode. The system is scalable, secure, and ready for real-time deployment with promising implications for chronic disease management and personalized care.
Patient Adherence, Real-Time Monitoring, Wearable Sensors, Machine Learning, Remote Patient Monitoring
Cost-of-Quality Reduction via Data Attribute Recommendation (DAR): Master-Data Stewardship at Scale in Manufacturing
The manufacturing businesses are still experiencing a high rate of digital change, which amplifies the need to have precise, consistent, and full master data to aid in production, supply-chain integration, quality control, and overall enterprise analytics. Nevertheless, the quality of master-data has continued to be a edging problem; resulting in unnecessary rework, scrap, warranty requests, non-compliance with regulations, and wastes within the system. All these inefficiencies add up to what is known in industrial circles as Cost of Poor Quality (CoPQ) a direct and indirect economic cost paid by manufacturers. To alleviate this load, the traditional interventions used by organisations have been manual data-stewardship processes, data quality validation using rules, and workforce interventions. However, these methods do not scale to high volume, high-dimensional data that is characteristic of modern business setups represented by mass customisation and multi-variant product configurations. In this paper, I present and empirically test a scalable, machine-learned solution called Data Attribute Recommendation (DAR) an automatic system that suggests values which are the best to put in missing, ambiguous or inconsistent master-data attributes. DAR has a goal to scale stewardship processes and at the same time minimize CoPQ. DAR uses supervised and semi-supervised learning architectures, similarity based clustering, probabilistic inferences and ontology mediated data standards to provide automated attribute completion, anomaly detection and confidence-assessed predictions. Also, DAR is referred to a closed-loop master-data governance architecture, which encourages lifelong learning and cuts back on the human validation overhead. The suggested solution is tested under an industrial setting that entails bill-of-materials (BOM), process variables, material requirements, and supplier metadata of a Tier-1 automobile parts supplier. DAR reached the average accuracy of attribute-recommendation of 92.7 percent that saved 62 percent of workload in manual stewardship and enhanced the completeness of data-quality by 71 percent to 96 percent overall. Simultaneously, the implementation also led to a reduction in CoPQ of an estimate of 14% through a lesser number of specification errors, better procurement precision, and lowered rework in assembling. The research will provide a detailed approach to integrating the principles of data-governance to manufacturing along with AI-inspired attribute-recommendation models. The paper discusses the architecture of DAR, data-pipeline design, modelling strategies, performance metrics and integration blueprint. Also, the paper presents the implications of DAR to future Industry-4.0 quality-engineering principles, such as predictive quality management, autonomous decisioning, and next-generation digital-thread realizations.
Master Data Quality, Cost of Quality, Attribute Recommendation, Manufacturing Analytics, Data Governance, Industry 4.0, Machine Learning, Digital Transformation, Data Stewardship Automation
Human vs AI translation of Common Names of Chemical Compounds: A comparative Study
In a study by Al-Jarf (2022), undergraduate student-translators had difficulty translating English common names of chemical compounds (CNCC) to Arabic. They gave correct responses to fewer than 20% of the test items and left 55% blank. Their most common translation strategy was literal translation and transliteration of English names. The students reported that they are not familiar with most of the English and Arabic chemical compound names. Based on these findings, the current study aims to find out if student-translators can use Artificial Intelligence (AI) to translate CNCC from English to Arabic and vice versa, % of chemical compound names correctly translated by AI and the translation strategies used by AI. Two samples of English and Arabic CNCC were translated by Microsoft Copilot (MC). Data analysis showed that MC translated 72% of the chemical compounds in the sample correctly. It gave more Arabic-English than English-Arabic correct equivalents (40% & 32% respectively). It gave more correct equivalents when I specified the domain and when I asked for all the equivalents that MC knows. MC gave literal word-for-word translation to terms as (Lunar caustic > الكاوي القمري(نترات الفضة); transliteration (Stearic acid >حمض الستيريك instead of حمض الشمع; a faulty derivative (Chlorinating powder > مسحوق التكلور instead of مسحوق الكلورة) and explanation (Ammonia liquor سائل الأمونيا). Although some English chemical compounds have multiple Arabic equivalents, MC gave only one unless prompted to give all. Even though MC is characterized by breadth of knowledge of English and Arabic chemical terminology and quick access to each term and in which context(s) it is used, in few cases, it failed to match the English chemical compound name with its Arabic equivalent and vice versa. Since translation-students’ knowledge of CNCC is limited, they are advised to specify the kind of terms to be translated and to ask AI to give as many equivalents as possible. They should give AI the context, nuance, and direction so that it matches the term with its correct equivalent. Results and recommendations are given in detail.
Chemical compounds, common names of chemical compounds, chemical terms, chemical translation, Artificial Intelligence, human translation, literal translation, word-for-word translation, transliteration
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
AI-Driven Data and Business Analytics for Smarter Wealth Management: Improving Financial Decision-Making, Risk Insights, and Portfolio Efficiency
Wealth management is entering a new era, where artificial intelligence (AI), data analytics, and business intelligence tools are reshaping how financial decisions are made. This study provides an in-depth examination of how AI-driven data analytics enhances every stage of the wealth management process—from collecting and interpreting complex financial data to predicting market movements and optimizing client portfolios. Unlike traditional advisory approaches that rely heavily on manual judgment, AI systems can process massive volumes of structured and unstructured data, uncover hidden risk patterns, evaluate asset performance in real time, and generate actionable insights with far greater accuracy and speed. The research explores how machine learning models improve long-term forecasting, stress testing, and risk scoring, while advanced analytics tools support personalized asset allocation tailored to client goals, risk tolerance, and market conditions. The study also discusses how AI-powered platforms reduce human bias, identify early warning signals for market disruptions, automate rebalancing strategies, and expand financial access through intelligent robo-advisory services. Furthermore, the paper analyzes the operational efficiencies gained by financial institutions, including real-time monitoring dashboards, automated compliance checks, and predictive client behavior modeling. By combining data-driven intelligence with human expertise, AI-enabled wealth management delivers more stable portfolio performance, improved transparency, enhanced risk mitigation, and better financial outcomes for both advisors and investors. This research demonstrates that AI and advanced analytics are not simply tools—they represent a fundamental shift toward smarter, adaptive, and more resilient wealth management systems.
Artificial Intelligence (AI); Wealth Management; Financial Advisory; Portfolio Optimization; Risk Analysis; Machine Learning; Predictive Analytics; Robo-Advisors; FinTech; Investment Strategy; Algorithmic Trading
Zero-Trust Architectures for Securing U.S. Critical Infrastructure
Zero Trust Architecture is an emerging trend in cybersecurity in the USA, changing the tact of cybersecurity strategies in groundbreaking ways. Whereas traditional models of security depend on a depth defense strategy, assuming implicit trust within internal networks, Zero Trust is designed on the principle of "never trust, always verify." This takes into consideration stringent verification processes for identities, irrespective of the location or network environment. ZTA, through the use of multifactor authentication, micro-segmentation, and behavioral analytics, among other advanced technologies, enables an organization's capability to defend itself against insider attacks, ransomware, and advanced persistent threats. The main principles of Zero Trust Architecture, challenges related to the implementation of this approach, and tangible benefits provided to modern enterprises are discussed in this paper. In this paper, through an in-depth case study analysis and empirical evidence, we try to demonstrate how the adoption of the Zero Trust framework advances security, reduces the attack surface, and enables organizations to respond effectively against emerging threats. The findings underpin that moving to a Zero Trust model is not only a tactical shift but also a strategic one for organizations in the USA in light of the current threat landscape, which aims at the protection of their key digital assets.
Zero-Trust Architectures; Securing Critical Infrastructure; Multifactor authentication; Cybersecurity; Cyber-Attack Mitigation
Quantum-Resilient Cryptographic Protocols for U.S. Data Security
As quantum computing technology advances, its capability to undermine widely adopted cryptographic systems presents a significant challenge to modern cybersecurity. This paper presents a strategic roadmap that shall help companies and industries in the USA understand how to proactively manage the risks associated with quantum-enabled attacks. The study proposes a quantum-resistant cryptographic framework, *STL-QCRYPTO*, which is targeted at bringing industry-specific methodologies for implementing quantum-safe security measures. The proposed framework guarantees long-term protection against the transformative impact of quantum computing. Emphasizing practical ways of deploying quantum-safe systems, this study gives active insight into mitigating emerging quantum cyber risks. The roadmap provides industries with the necessary tools to safeguard against key security threats that may emerge in the quantum era through a structured timeline and targeted recommendations.
Quantum-Resilient Cryptography; Data Security; Data breaches; STL-QCRYPTO; Quantum computing
Unified Temporal Tokenization: A Hybrid Semantic and Numeric Mapping for Time-Aware Large Language Models
This paper introduces the Unified Temporal Tokenization (UTT) framework — a hybrid encoding mechanism that bridges numeric and semantic time representations for time-aware Large Language Models (LLMs). UTT translates continuous time-series signals and symbolic temporal descriptors into unified hybrid tokens, preserving hierarchical periodicity while retaining contextual meaning. UsinG the UCI Electricity Load dataset, we demonstrate that the Hybrid Temporal Tokenizer (HTT) improves prediction stability, interpretability, and efficiency on CPU-only environments, establishing a foundation for temporal reasoning in LLMs. Based on this, the paper expounds on improvements of the hybrid temporal embeddings in increasing the aptitude of the model to capture cyclical and contextual relationships at several levels of time. The framework suggested combines numeric continuity with semantic periodicity, which makes the LLMs reason about time-sensitive patterns, including consumption cycles of a day, seasonal variations, and events. Experiment analysis of LSTM, TemporalConv, and Transformer variations demonstrates improvements as regular in ensemble prediction-related accuracy, interpretation, and computationally wise than traditional numeric-only tokenization. In addition, the UTT architecture is flexible in many areas, which can provide a single response to enterprise analytics, internet data streams, and economic forecasting problems that require the use of temporal reasoning. This study integrates semantic and numeric temporal mapping and offers a scalable framework of time-aware large language models, which spans the language-based and data-based temporal intelligence. Using the UCI Electricity Load dataset, we demonstrate that the Hybrid Temporal Tokenizer (HTT) improves prediction stability, interpretability, and efficiency in CPU-only environments—establishing a foundation for temporal reasoning in LLMs.
Copyright (c) 2025 Frontiers in Computer Science and Artificial Intelligence
Deep Learning for Intelligent Supply Chain Optimization: Enhancing Operational Efficiency and Waste Reduction in U.S. Service Industries
U.S. service industries face persistent inefficiencies driven by volatile demand patterns, short product life cycles, and operational fragility across logistics networks. These conditions create a structural forecasting challenge where traditional statistical models struggle to capture event-driven variability, leading to systematic overstocking, stockouts, and waste. This study addresses that challenge by developing a deep learning–driven supply chain optimization framework that integrates high-resolution calendar features, lagged demand patterns, and sequence-based neural forecasting architectures. Using the M5 dataset as a proxy for service-sector operational behavior, we benchmark classical models (Naive, Linear Regression, LightGBM) against LSTM, N-Beats, and a lightweight Transformer model designed for Colab-scale experimentation. We evaluate the forecasting outputs through a simulation of inventory policies including (s, S) and base-stock systems, capturing holding, shortage, and waste costs under multiple demand scenarios. Results show that Transformer-based models consistently outperform statistical baselines on multi-horizon forecasting, improving RMSE and MAE across varied temporal contexts. When integrated with inventory policies, deep models reduce stockouts and expired-inventory waste while lowering total operational costs compared with heuristic or statistical forecasting systems. Sensitivity and ablation analyses confirm the value of calendar encodings, lag structure, and longer receptive fields in improving predictive stability. Computational profiling confirms that optimized neural architectures can run efficiently on edge-constrained environments such as Colab GPUs. These findings demonstrate that deep learning–enabled forecasting provides a practical and high-impact path toward intelligent supply chain optimization in U.S. service industries.
Quantum Resilient Blockchain Protocols for Securing U.S. Financial Systems
Blockchain technology has been in recent years among the most adopted technologies in the U.S. It has disrupted whole industries, ranging from finance and health to supply chain management and verification of digital identities. Nevertheless, such a tendency does not imply that the Blockchain systems are by any means fully protected against threats emanating from other emergent technologies, like quantum computing. State-of-the-art algorithms like Secp256k1 and Schnorr afford high levels of security in Blockchains; notwithstanding, they are vulnerable to quantum attacks. To solve this problem, a series of quantum-resistance algorithms has been proposed. Performance analysis of Quantum-resistant algorithms on a Blockchain in this respect, therefore, originates from getting effective insights into the efficiency of quantum-resistant algorithms in real-world scenarios. In light of the above need, we prototyped and analyzed the Falcon algorithm for a quantum-resistant Blockchain. Falcon is preferred because it provides a smaller signature and key size compared to Crystals-Dilithium and Sphincs++. This paper proposes a systematic quantum-resistant Blockchain and suggests different approaches to select quantum-resistant algorithms based on different Blockchain scenarios. These results from our approach and benchmark have implications for the future development and adoption of quantum-resistant Blockchains.
Quantum-Resilient Blockchain; Quantum Computing; Falcon algorithm; Blockchain Security; Post-quantum signatures; Cryptography
Development of AI-Driven Machine Learning Systems for Real-Time Detection and Automatic Mitigation of Advanced Cyber Threats Across Critical Infrastructure
A growing threat to critical infrastructure systems including energy grid, water treatment plants, transportation and health care systems has emerged as a result of advanced cyber-attack methods (advanced persistent threats (APTs), ransom ware, and coordinated denial-of-service attacks). The conventional signature-based and passive intrusion detection mechanisms are no longer adequate. The proposed article suggests the creation of AI-driven machine learning (ML) systems to be used to detect anomalous network and system behaviors in real-time and automatically mitigate detected threats in a critical infrastructure setup. The suggested system incorporates supervised and unsupervised ML algorithms, deep-learning systems, and reinforcement-learning agents to observe real-time information streams of industrial control systems (ICS), supervisory control and data acquisition (SCADA) networks, and IoT-enabled equipment. After a threat has been detected, the platform initiates automated mitigation measures, e.g. network segmentation, firewall rulebook, process isolation, or threat-intelligence sharing, to reduce the effect of the threat and downtime. The article introduces an architecture for the framework, scalability, and low latency considerations of the implementation, and a test based on simulation data of representative infrastructure environments. Findings reveal that the AI-based system is capable of identifying new threats much quicker than old-fashioned and minimizes the incidence of false-positive outcomes as well as the possibility of taking mitigation measures within tolerable time frames to maintain an operational resilience. Deep-learning decisions might be interpreted with limitations, and integrating deep learning with legacy infrastructure is a problem. The paper provides an example of the resilience of critical infrastructure through the application of AI-ML systems that prepare the infrastructure to operate in proactive, rather than reactive, cyber security modes.
Critical infrastructure, real time cyber threat detection, machine learning, artificial intelligence, automatic mitigation, industrial control systems, SCADA security, anomaly detection, reinforcement learning, deep learning
Autonomous Threat Intelligence Aggregation and Decision Infrastructure for National Cyber Defense
The speed of the cyber threats to national infrastructures has focused the necessity of autonomous and intelligence-based defense systems that are urgent. Conventional intrusion detection and response models are overly manual in compliance, have little data correlation, and set-based rules, which are inadequate to respond to modern, evolving adversaries. The study outlines the design and assessment of an Autonomous Threat Intelligence Aggregation and Decision Infrastructure that will enable the improvement of national cyber defense preparedness by automation, intelligence integration, and contextual decision-making. The suggested framework uses artificial intelligence (AI) and machine learning (ML) to streamline the process of collecting, analyzing, and correlating the data on the threats of various origins. Raw events in the network are processed by the system and converted into structured and machine-readable intelligence aligned to the Structured Threat Information eXpression (STIX 2.1) and Trusted Automated eXchange of Intelligence Information (TAXII 2.1) standards. The mapping of the identified threats onto familiar adversarial tactics, techniques, and procedures (TTPs) by integrating the MITRE ATT&CK framework is another way to promote contextual understanding. Model training and validation were conducted using UNSW-NB15 which contains more than 2.54 million labeled network records. Machine learning models, especially the Random Forest and Support Vector Machine (SVM), were found to be very accurate and stable in the process of detecting and classifying different types of attacks. The experimental findings proved the capability of the system to autonomously bring together intelligence, lay the emphasis on the most significant threats, and distribute the real-time notifications to the defense nodes through standardized protocols. The proposed study adds to the expanding literature of AI-based national cyber defense by suggesting a scalable, interoperable, and adaptive framework, which will decrease response time, decrease human reliance, and increase situational awareness in general. The results of the study support the possibilities of autonomous systems to change the approach to cyber defense, which is based on reactive detection to proactive and collaborative national intelligence campaigns.
Autonomous Cyber Defense, Threat Intelligence Aggregation, Machine Learning, STIX/TAXII Framework, MITRE ATT&CK Mapping and National Cyber security
Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0/
An Integrated MIS–Cybersecurity Governance Framework for Risk-Adaptive IT Project Management in Critical Infrastructure Systems
Protecting critical infrastructure from fast dissever cybersecurity needs a unified approach that brings together governance, technology and project execution. This research proposes an Integrated MIS-Cybersecurity Governance Framework. It is about strengthening risk adaptive IT project management in pertinent national infrastructure sectors such as healthcare, energy, transportation, and finance. Using principles from MIS, zero trust security, and project risk analytics, the framework constructs a multilayer decision support architecture. It instills cybersecurity controls, local measures for anomaly detection and compliance automation into the IT project lifecycle. The study draws on a mixed-method evaluation, comprising interviews with experts in the field and historical breach data and simulation-based project models to demonstrate that organizations that employed this type of governance structure reduced project-related cyber vulnerabilities, cost overruns, and downtime. Results show that MIS workflows being aligned with cybersecurity intelligence is improving visibility across departments, making incident response much faster and giving increased strength to the effort to update infrastructures. The framework provides a scalable, data-driven blueprint for securing large-scale digital transformation projects that will support the national security and economic resilience of the US.
An Integrated MIS–Cybersecurity Governance Framework; Risk-Adaptive IT Project Management, Critical Infrastructure Systems
Integrating Generative AI for Personalized Banking: Opportunities, Risks, and Future Directions.
Amidst the deluge of digital transformation, generative artificial intelligence (GenAI) represents a game changer for banking, providing an attractive route to provide highly personalised services, maximise operational efficiencies and enhance risk monitoring. This paper examines the diverse roles GenAI will play in banking, including transforming customer-facing engagements via personalized financial planning, dynamic product suggestions and conversational interactions that are context aware. At the same time, the technology has appeared to automate internal processes—like onboarding employees, processing documentation, safeguarding against fraud and keeping an eye out for compliance violations—that have been hampered in past years by incumbently archaic systems.Yet, prospects are balanced by sizeable risks. The principal challenges include privacy and security of the data, bias and fairness in the models, lack of explainability, regulatory liability risk and concentration risk related to relying on common AI providers or infrastructure. New work is cautioning that if there are not proactive interventions to mitigate these concerns, the benefits of GenAI itself could be drowned out through operational mishaps and/or ethical pushback. Recent literature and industry examples are reviewed to unpack these domains of risk in the domain of banking.
Explainable AI, Graph Neural Networks, Malware Propagation, Supply-Chain Attacks, Cybersecurity, Software Dependencies
Ethical and Regulatory Challenges of AI Adoption in the Banking Sector: A Global Perspective
AI is being rapidly adopted across the banking industry and is expected to drive significant improvements in operational efficiency, customer service, risk assessment and new product offerings. But this shift raises a number of ethical and legal challenges, which are rarely accounted for at a worldwide level. This paper examines some important considerations in the application of AI in banking: algorithmic bias and fairness; transparency and explainability; data privacy and consent; accountability and liability; system risk and stability; cross border regulatory fragmentation; governance of 3rd party AI provider/ vendor. Based on global regulatory canvassings and market summaries (e.g., the Financial Stability Board’s surveil of adoption of AI), and via a comparison of the strategies pursued by leading jurisdictions (e.g., EU’s Artificial Intelligence Act), it shows that although they are leveraged across financial institutions for tasks such as credit underwriting, fraud detection, or customer interface handling, self-learning algorithms do often face lackluster new supervision frameworks, black-box model behavior and inconsistent supervisory expectations. Classifying, evaluating and mitigating risks The analysis reveals ethical (e.g. unfair discrimination, hacking of customer data), social (e.g., impact upon employment) and regulatory (compliance failure, operational hazard) risks are highly interconnected: for example, in some cases, regulatory complexity can slow innovation by up to 35%. In conclusion, this world-wide perspective reveals that adopting AI in a responsible way from the standpoint of bank regulation requires an approach rich in diversity — involving solid governance systems, transparency of stakeholders, ongoing monitoring, cross -border regulatory co-ordination and an ethics by design frame work to see their return from AI without undermining trust, consumer protection or financial stability.
AI-Driven Cybersecurity, Adversarial Machine Learning, Explainable Artificial Intelligence (XAI)
AI-Powered Anomaly Detection in Solar SCADA Systems: Challenges and Solutions
The digital transformation of solar energy environments has left them exposed to cyber and operational threats, especially in SCADA systems that control photovoltaic (PV) plants. Conventional rules-based monitoring solutions are becoming inadequate to manage how large, complex and real-time the smart grid has become. In this paper, an AI-based anomaly detection framework using deep learning models (CNN and LSTM) is proposed to detect abnormal patterns in solar SCADA data streams. Both process variables (e.g., voltage, current, irradiance, inverter temperature) and network traffic metrics are considered by the proposed system while querying false data injection, DoS attack and communication-layer anomalies. Experimental subject results obtained on hybrid real-time datasets prove that the proposed model attains more than 96% detection efficiency, substantial decrease in false alarm rate (FAR) and latency when compared to conventional machine learning classifiers. The framework further includes online adaptation that provides adaptive retraining capabilities for addressing concept drift due to environmental and operational changes. These results demonstrate that the AI-based anomaly detection capability not only improves cybersecurity of cyber-physical systems and the data integrity, but it also helps predictive based maintenance and operation robustness of PVs. Lastly, the paper reviews major hurdles (e.g., paucity of datasets, model explanation and edge deployment limitations) and proposes practical solutions to embed intelligent anomaly detection in nextgeneration secure-, autonomous- and sustainable-based solar SCADA ecosystems.
Solar energy, SCADA systems, anomaly detection, deep learning, cybersecurity
Copyright (c) 2022 https://creativecommons.org/licenses/by/4.0/
A Neural Network-Based Security Model for Real-Time Solar Energy Monitoring Systems
Smart grids and their increasingly linked solar power are driving the demand for more resilient, TETRA-compatible real-time cyber security solutions to protect vital data and infrastructure. This paper presents a neural network-driven security model dedicated to real-time solar energy monitoring systems for the purpose of identifying and counteracting several kinds of cyber-threats including false data injection, DoS attacks as well as unauthorized access. The model is built based on the hybrid deep learning that uses CNN to extract features, and LSTM to identify temporal patterns in data streams. Real-time data from PV monitoring devices are preprocessed by normalizing and adaptively selecting features to increase accuracy and responsiveness. Experimental results show that the proposed model outperforms standard machine learning-based classifiers in detection accuracy, false positive rates and boot response time. The embedding of the NN into the solar monitoring infrastructure guarantees that threats are made visible before an attack occurs, thus strengthening system security, energy data traceability and grid robustness. These observations demonstrate that deep neural architectures have the potential to shape autonomous and adaptive cybersecurity mechanisms addressing intelligent energy infrastructures in an era of Internet-of-Things (IoT).
Smart grids, solar power, TETRA-compatible, cybersecurity, deep learning
Predictive Analytics and AI-Based Forecasting Models for Loan Default and Portfolio Optimization
The adoption of predictive analytics and artificial intelligence (AI) in financial risk management has changed the way institutions evaluate creditworthiness and its lending portfolios are managed. This paper, entitled “Predictive Analytics and AI-Based Forecasting Models for Loan Default and Portfolio Optimization,” focuses on high-level development and using of advanced machine learning algorithms to predict loan default rates as well as optimize portfolio performance. We use historical financial, behavioral and transactional data to build models (logistic regression, random forest, XGBoost, deep neural networks), which improve predictive performance for early risk identification. The AI-driven forecasting was achieved by the use of dynamic feature engineering, real-time anomaly detection and explainable AI (XAI) methods to make sure that it is not only interpretable but also compliable with regulation. In addition, portfolio optimization is performed by AI driven allocation strategies including reinforcement learning and evolutionary algorithms that balance risk and return. The results show that the hybrid AI models are superior to traditional statistical methods in terms of prediction precision, default rate lowering and portfolio robustness under market turmoil. This effort adds to the emerging literature on financial technology by providing a scalable, transparent and data-driven method for credit risk management and sustainable financial choice.
Adversarial Machine Learning, Explainable Artificial Intelligence (XAI), Federated Threat Intelligence
Achieving Transparency and Trust with Explainable AI (XAI) in Financial Services
The swift proliferation of Artificial Intelligence (AI) in financial services has ushered a new era with respect to decision-making mechanisms across the domains including, but not limited to credit scoring, fraud detection, investment advising and risk analysis. Yet the growing complexity and non-transparency of AI models have raised issues around transparency, fairness and accountability. In this paper, we tackle the position of XAI in order to support trustful and transparent ethical verdicts within the financial world. In addition, interpretability techniques like SHAP (SHapley Additive Explanations) or counterfactual reasoning are used to increase human understanding of algorithmic results and enable compliance with regulatory requirements like the European Banking Authority (EBA) AI Governance Guidelines. The study is examining how explainability refines model auditing and bias identification while enhancing stakeholder faith, mitigating systemic risk, and driving responsible AI adoption. Results indicate that XAI narrows the chasm between algorithmic efficacy and ethical responsibility by turning black-box “black hole” systems into transparent, audit-ready, and human-centered decision forms. The study argues that embedding XAI principles will be imperative to ensure trustworthy AI governance, continuous innovation and compliance as the FinTech landscape evolves.
Zero-Day Threat Detection, Adaptive Meta-Learning, Graph Neural Networks, (GNNs),Federated Cyber Defense,Reinforcement Learning for Security
Pronunciation Errors in Arabic YouTube Videos Narrated by AI
Arabic has three long vowels /a:/, /u:/, /i:/ and three short vowel /a/, /u/, /i/ which are represented by diacritics marked over and under consonant letters. In words that have short vowels, only the consonants are written. Arabs usually read without any diacritics except for the Holy Quran and the Prophet’s Hadiths. The absence of short vowel diacritics poses pronunciation difficulties for Artificial Intelligence (AI). Henceforth, this study aims to anlayze a sample of Arabic YouTube videos narrated by AI to find out which Arabic words are mispronounced by AI narrators. It was noted that AI narrators speak with a natural voice, good expression and intonation. They make no grammatical or syntactic errors. But they make pronunciation errors, especially in diacritics and homographs (words that are spelled the same but have different pronunciations and meanings depending on the short vowel diacritics which are not usually shown on written words). This means that AI has difficulty matching the pronunciation of a homophone with the context in which it is used. They confuse short vowel diacritics on the suffix /ta/, /ti/ /tu/ suffix تاء التأنيث when it refers to first, second, or third person, masculine or feminine, imperative and past tense (كَتَبَ كُتِب كُتُب كَتّبَ كُتّب كتبتَ كتبتُ كتَبتِ كُتِبَتْ كُتّبت). This affects comprehension in L2 learners and causes cacophony and distortion for native speakers and non-native speakers of Arabic. The article sheds light on how AI reads Arabic aloud, classification of pronunciation errors in AI narration; variations in the type and frequency of pronunciation errors across videos; why AI makes mistakes in pronouncing Arabic but does not make grammatical or syntactic errors in AI-narrated content; and how AI pulls off realistic intonation. Additionally, the article gives suggestions for improvement and recommendation for students learning Arabic as a foreign language.
AI narrated Arabic videos, AI-generated stories, AI narrators, pronunciation errors, Arabic diacritics, Arabic homophones, text-to-speech, language processing
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0
Integrating Cloud IoT and Federated Learning for Privacy-Preserving Autism Monitoring
Constant observation of behavior can significantly enhance the quality of autism treatment but conventional centralized AI models are extremely dangerous in terms of privacy and sharing data. This paper suggests a federated learning (FL)-based hybrid cloud-IoT system to guarantee privacy-aware behavioral monitoring. Wearable and environmental sensor data were locally processed and global updates to models were done through an encrypted cloud aggregator. A test conducted on 48 children with autism showed that FL managed to attain the same accuracy as the centralized model with the transmission of the raw data cut by 96 percent. The findings confirm the practicability of federated IoT learning in a healthcare setting with privacy, latency, and personalization as key considerations.
Federated learning; Cloud IoT; Privacy preservation; Autism monitoring; Edge computing; Behavioral analytics; AI in healthcare
Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/
Hybrid Deep-Learning Model for Predicting Meltdown Probability
Autistic children (Autism Spectrum Disorder or ASD) are susceptible to meltdowns as complex behavioral events that occur through the interplay of emotional, sensory and contextual stressors. Anticipation of these incidents is a major concern to the caregivers and clinicians. In this paper, the author presents Hybrid Deep-Learning Model (HDLM) that combines Long Short-Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost) networks to predict the likelihood of occurrence of a meltdown based on the multimodal IoT sensor data. The hybrid architecture is a composite of temporal sequence learning and features level gradient optimization, which allows the accurate and interpretable forecasting. Real-world behavioral datasets experimental evaluation had 92% accuracy, 0.94 ROC-AUC and reduced latency by 35% over traditional single-model baselines. The findings affirm that HDLM framework has the ability to offer early, explainable risks alerts in adaptive interventions in the care of Autism.
Meltdown prediction, hybrid deep learning, LSTM-XGBoost, autism intervention, behavioral analytics, IoT sensors, explainable AI
Explainability Metrics in Reinforcement-Based Therapy Systems
The learning systems that utilize reinforcement learning (RL) are revolutionizing customized behavioral interventions by being able to dynamically adjust to the reactions of patients. Nonetheless, the darkness of their decision-making remains restraining clinical trust and ethical implementation. In this study, the researchers presented Explainability Metric Framework (EMF), a methodology to quantitatively evaluate interpretability in RL-based therapy models. The proposed measures, which are Policy Transparency (PT), Reward Attribution (RA), and Clinical Alignment (CA) assess model understandability in an algorithmic, therapeutic, and human-centered way. As shown with the help of a hybrid Actor-Critic-based architecture with SHAP-based interpretability modules, it was demonstrated that explainability is improved by 27 percent and action misclassification is reduced by 19 percent as compared to classic RL baselines. This work makes a contribution to a transparent and ethically regulated AI in behavioral healthcare by conforming to the principles of Human-Centered AI and NIST AI RMF.
Reinforcement Learning, Explainable AI, Behavioral Therapy, Human-Centered AI, SHAP Analysis, Clinical Transparency, Trustworthy AI
Emotion-Driven IoT Feedback Loop for Caregiver Training
Emotional sensitivity is one of the pillars of effective therapy of autism, but caregivers do not have real-time instructions on how to interpret affective expression of children. This paper proposes an Emotion-Driven IoT Feedback Loop (EDIFL) that is focused on feeding back data-driven feedback immediately to caregivers during behavioral interventions. The system includes multimodal IoT sensors, edge level emotion recognition and cloud-based adaptive feedback generation. The framework is made more responsive and accountable in autism care, through integrating machine learning, human-centered AI, and NIST AI RMF governance. Findings indicate an increase of 47% in accuracy of caregiver response and decrease of 38% in delayed reactions when compared to the old method of training sessions. The suggested EDIFL model is a great advancement in terms of intelligent, compassionate and morally responsible caregiving assistance.
Emotion-driven IoT, Caregiver feedback, Autism intervention, Human-centered AI, Affective computing, Trustworthy IoT, Edge analytics
Copyright (c) 2024 https://creativecommons.org/licenses/by-nc/4.0