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
AI model that predicts antibiotic resistance from bacterial genomes (AMR) using open sequencing data
Antimicrobial resistance (AMR) prediction from bacterial whole genome sequencing can shorten time to effective therapy, strengthen surveillance, and reduce reliance on slow culture based susceptibility testing. However, many genomic AMR workflows still depend on curated gene and mutation catalogs, which can miss emerging mechanisms, vary in coverage across species, and rarely provide calibrated uncertainty. We present a phenotype first framework that trains models to predict resistant, intermediate, or susceptible outcomes directly from genome sequences using open repositories. Genomes and linked phenotypes are assembled from NCBI SRA and GenBank records and harmonized with BV BRC (formerly PATRIC) antibiotic panels. We compare three modeling families: catalog based baselines (AMRFinderPlus and ResFinder), k mer linear models with stability selection, and sequence transformers fine tuned on long k mer tokens. To support clinical decision making, predicted probabilities are calibrated with temperature scaling and wrapped with conformal prediction to yield distribution free confidence sets. Interpretability is addressed by mapping influential k mers and attention based attributions to genes and known resistance determinants in CARD and MEGARes, producing mutation importance summaries that can be reviewed by microbiologists. We define evaluation protocols that avoid leakage through lineage structure, including temporal splits and site stratified cross validation, and report discrimination, calibration, and abstention metrics. The result is a reproducible template for AMR phenotype modeling that complements rule based tools while providing transparent evidence and quantified uncertainty. The manuscript targets publication in November 2025 and emphasizes open science, auditability, and transferability across pathogens and antibiotics in public health practice.
Antimicrobial resistance; AMR prediction; bacterial whole-genome sequencing; genotype–phenotype modeling; antibiotic susceptibility; k-mer embeddings; sequence transformers; genome foundation models; BV-BRC (PATRIC); NCBI SRA; GenBank; AMRFinderPlus; ResFinder; CARD; MEGARes; interpretable machine learning; mutation importance; calibrated uncertainty; temperature scaling; conformal prediction; deep ensembles; lineage-aware validation; temporal split; public health surveillance.
Copyright (c) 2025 Frontiers in Computer Science and Artificial Intelligence
Authoritative Data Stores for Treasury Deposits: Sourcing, Certification, and Provisioning Standards for Enterprise Downstreams
The rapid growth of enterprise data volumes and the increasing demand for timely, accurate, and auditable financial reporting have compelled large organizations to rethink traditional extract–transform–load (ETL) architectures supporting treasury deposit systems. Between 2018 and 2019, enterprises—particularly in banking and financial services—faced mounting limitations with legacy SAS, mainframe COBOL, Informatica, and Oracle PL/SQL-based batch processing platforms. These systems were costly, vertically scaled, and constrained by overnight batch windows that could no longer meet modern regulatory, operational, and analytical requirements. This paper presents a comprehensive architectural and methodological framework for building Authoritative Data Stores (ADS) for treasury deposits using Apache Spark as the core distributed processing engine. The framework addresses data sourcing, certification, governance, and standardized provisioning to downstream consumers such as regulatory reporting, risk analytics, finance, and real-time dashboards. It elaborates on enterprise migration drivers, architectural comparisons between legacy ETL systems and Spark-based ecosystems, and the key challenges encountered during large-scale modernization initiatives. A structured migration methodology is proposed, encompassing baseline legacy assessment, code translation, data quality reconciliation, performance optimization, orchestration redesign, and phased cutover strategies. The paper further illustrates best practices through representative case studies demonstrating significant performance gains, cost reduction, and improved scalability. The findings highlight Spark’s role as a unifying compute platform capable of supporting batch, streaming, and advanced analytics workloads while maintaining the integrity and auditability required for treasury deposit data. The study concludes that Spark-enabled Authoritative Data Stores provide a resilient foundation for enterprise financial data modernization, enabling near real-time insights, improved governance, and long-term adaptability in evolving regulatory and business environments.
Authoritative Data Store, Treasury Deposits, Apache Spark, Legacy ETL Migration, Data Certification, Enterprise Data Architecture, Distributed Processing
Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
AI-Enhanced Sustainable Energy Management and Policy Recommendations for the U.S. Power Sector
The transition to a sustainable U.S. power sector is faced with the challenge of balancing reliability, decarbonization, and affordability in more variable demand conditions. There is tremendous potential for artificial intelligence (AI) to make huge contributions to forecasting accuracy, optimizing resource allocation, and in developing policy and evidence-based policy. This paper presents a new model of hybrid artificial intelligence for sustainable energy management applications that consists of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Transformer networks combined with CatBoost meta-learner and then optimized with residual BiGRU-attention and α-blender. The proposed model was tested using the public dataset of hourly electricity generation and demand data from the United States of America with temporal and operational characteristics significant to grid planning. Experimental results indicate a very high performance of the hybrid model compared with baseline methods for all performance measures. Specifically, it had a minimum MAE (0.007503), minimum MSE (0.000102) and RMSE (0.010082) and highest R2 value (0.995954) than single Lstm, GRU, Catboost and Xgboost. Aside from technical performance, the study accomplishes three contributions: efficiency-enhancing energy management using AI-driven energy forecasting and optimization, American context - use of demand forecasting for renewable integration and grid balancing, and extending technical understanding to the policy advice for future adaptive regulations. By integrating AI analytics, policy design, and sustainability policy, this research makes a methodological contribution as well as a governance-centric framework with a focus on setting up AI as an accelerator for the sustainable transformation of America's power grid.
Artificial intelligence, Hybrid deep learning, Energy demand forecasting, Sustainable energy management, Policy recommendations, U.S. power sector.
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
Shift-Left Security Validation of Containers via Kubernetes Admission Webhook
We propose a unified “shift-left” security validation framework that integrates static vulnerability scanning, SBOM generation, image signature verification, policy-as-code enforcement, and best-practice scoring into a single Kubernetes admission-control webhook invoked within the CI/CD pipeline. By atomically intercepting container admission requests, the system produces a Software Bill of Materials, cross-references CVE feeds, validates digital signatures, applies dynamically loaded JSON/YAML policies, and computes a weighted KubeScore for Pod specifications prior to deployment. Evaluation on realistic workloads demonstrates end-to-end processing latency below 200 ms and detection rates exceeding 95 % for critical vulnerabilities and misconfigurations. This consolidated approach eliminates post-deployment scans, accelerates feedback loops, strengthens compliance auditability with immutable logs, and lays the foundation for future AI-driven remediation and multi-cluster policy synchronization. Index Terms – Admission-Control Webhook, SBOM (Software Bill of Materials), Static Vulnerability Analysis, CVE (Common Vulnerabilities and Exposures), Image Signature Verification, Cosign, Notary v2, Policy-as-Code, Open Policy Agent, Kyverno, KubeScore, kube-bench, Dynamic Policy Loading, Hot-Reload, Automated Remediation, Self-Healing, Immutable Audit Logs, CI/CD Pipeline Integration, Kubernetes Best Practices, SBOM Formats: CycloneDX, SPDX, Threat Modeling, Compliance Automation.
Shift-Left Security Validation; Containers; Kubernetes Admission Webhook
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
Why Developers Fail When Building First-Generation Agents- Common Pitfalls and Cryptographically Grounded Design Solutions
The development of autonomous software agents, built upon large language models (LLMs) and integrated with external tools, marks a significant paradigm shift in computation. However, first-generation agent deployments often exhibit critical failure modes that undermine reliability and security. This article, grounded in established cryptographic and access control theory, analyzes five primary development pitfalls: reasoning failures, runaway loops, missing context, transient state loss, and faulty planning logic. We argue that these failures stem from an inadequate foundational separation between the agent's generative capability (the LLM) and its operational integrity (state, authorization, and execution). We propose a theoretical Verifiable Context-Aware Access Control (VCAAC) model. This model enforces trust not just on the agent's identity, but on the verifiable proof of its state and the computational path taken to reach a decision, utilizing Zero-Knowledge Proofs (ZKPs) and distributed capabilities to mitigate security risks inherent in autonomous execution. The discussion includes a realistic threat model and highlights scalability limitations, focusing exclusively on hypothetical benefits without recourse to fabricated performance data.
Autonomous Agents, Zero Trust, Cryptography, Access Control, Verifiable Computation, LLM Security, Agent Alignment
AI-Driven Big Data Analytics for Precision Medicine and Healthcare Intelligence: A Unified Framework for Cancer, Chronic Disease, and Clinical Decision Optimization
The explosion of various healthcare data from genomics to medical imaging to electronic health records, wearable sensors, and real-time clinical systems opens new opportunities for precision medicine. Yet traditional statistical and rule-based decision tools are stretched to the limits of analyzing the high-dimensional and multimodal and constantly changing data. Recent research has identified that AI-driven big data analytics can be used to scale up and provide better early detection of diseases, personalized treatment planning and clinical decision making in oncology and chronic diseases (Ahmed et al., 2025; Manik et al., 2025a). This paper synthesizes AI-enabled big data analytics for precision medicine, drawing on evidence from cancer diagnostics, prediction of chronic diseases, genomics-based drug discovery, and explainable clinical intelligence. It brings together the domains of deep learning, hybrid machine learning architectures, multimodal data fusion, and explainable AI (XAI) techniques that are subsumed into consolidated deep learning models into one unified framework that aims to enhance the performance of diagnostic accuracy, interpretability, and translational readiness (Forhad Hossain, 2025; Islam et al., 2025). The framework focuses on ethical data governance and scalable analytics pipelines, and clinician-centric transparency to bridge the gap between algorithmic innovation and real-world healthcare implementation. Synthesized results of findings show the consistent superiority of certain AI-relying big data analytics over traditional methods for disease classification, risk stratification, and therapeutic decision support and for certain other new challenges concerning model explainability, data privacy, and healthcare equity (Rahman et al., 2025; Samiun et al., 2025). By combining cross-domain evidence and proposing a unified analytical architecture, this work is designed to provide researchers, practitioners, and authorities with practical and useful knowledge by responding to the demand to operationalize at scale AI-powered precision medicine.
AI-Driven Precision Medicine; Big Data Analytics; Multimodal Healthcare Data; Explainable Artificial Intelligence; Predictive Clinical Modeling; Personalized Healthcare Systems
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0
Artificial Intelligence for Revenue Forecasting, Risk Management, and Financial & Business Planning in International Sports Tourism: The FIFA World Cup USA 2026 Case
The upcoming FIFA World Cup USA 2026 represents one of the largest international sports tourism events in history, generating complex financial flows, multidimensional risks, and unprecedented planning challenges for host nations, cities, and private stakeholders. This study examines the role of Artificial Intelligence (AI) in enhancing revenue forecasting, risk management, and financial and business planning within the context of international sports tourism, using the FIFA World Cup USA 2026 as a case study. Drawing on predictive analytics, machine learning models, and big data integration, the paper demonstrates how AI-driven systems can improve the accuracy of demand forecasting, optimize pricing and taxation strategies, and support dynamic budget allocation across public and private sectors. The study further explores AI applications in risk identification and mitigation, including economic volatility, infrastructure cost overruns, cybersecurity threats, crowd management risks, and uncertainty in tourism inflows. By integrating real-time data from ticketing platforms, hospitality services, transportation systems, and macroeconomic indicators, AI enables scenario-based financial planning and evidence-based decision-making. The findings highlight that AI-supported financial frameworks not only enhance fiscal sustainability and investor confidence but also contribute to resilient and inclusive economic outcomes for host regions. This research provides practical insights for policymakers, financial planners, tourism authorities, and sport’s governing bodies seeking to leverage AI for strategic planning and long-term value creation in mega sporting events.
Revenue Forecasting, Risk Management, Financial Planning, Business Analytics, Sports Tourism, FIFA World Cup 2026, Predictive Analytics, Public Finance, Tourism Economics
Parallel Deep Learning for Cybersecurity-Oriented Multi-Platform Social Media Bot Detection
Social media is a key unit of today's cyber communication society; however, being widely available, social media networks also contribute to the diffusion of social bots, which are then abused for forging global digital weapons, such as spreading fake news, organized manipulation and infiltration, or malicious propaganda actions. However, identifying these bots in a multi-platform setting is difficult due to the heterogeneity of data structures and behaviours, as well as platform-specific issues. In this paper, we have presented a parallel DNN-based social media bot detection model for various platforms. The proposed architecture has a multi-head parallel structure sharing the latent representations and simultaneously learning the shared latent features and platform-specific independent detections. What it allows, however, is for the framework to handle both generic bot prototype behaviours and platform-specific functionalities in a single solution that scales well. The proposed approach is evaluated on two real-world datasets taken from Twitter and TikTok, including behavioural, engagement, and account-level features. Experiments show strong and consistent detection performance across the tested platforms. The detection heads on Twitter reach 94.47% accuracy, 94.42% F1-score, and a ROC-AUC of 94.77%, while those on TikTok obtain an accuracy of 91.29%, an F1-score of 92.92% and a ROC-AUC score of 96.30%. High precision–recall performance also confirms the effectiveness of the proposed system in detecting automated harmful accounts with low false positive rates.
Cyber Security, Bot Detection, Artificial Intelligence, Social Media
Socio-Technical Integration of AI Decision Support in Autism Care: Implications for Caregiver Workflows, Trust, and Workforce Sustainability
The integration of artificial intelligence into autism spectrum disorder care promises improved behavioral monitoring, early escalation detection, and enhanced decision support for caregivers. However, the success of such systems depends not only on technical accuracy but also on their alignment with human workflows, trust dynamics, and workforce sustainability. This study examines the socio-technical dimensions of AI deployment in autism care, focusing on how AI decision support systems interact with caregiver roles, responsibilities, and organizational contexts. We propose a human-centered socio-technical framework that integrates AI capabilities with caregiver workflows, trust calibration, and governance mechanisms. Using simulated care environments informed by empirical studies, the framework is evaluated in terms of caregiver workload, decision confidence, trust, and system acceptance. Results suggest that socio-technical alignment significantly improves caregiver engagement and long-term system sustainability. This research highlights the necessity of designing AI systems that support—not disrupt—the human foundations of autism care.
Autism spectrum disorder; Socio-technical systems; Caregiver decision support; Human-centered AI; Workforce sustainability; Trustworthy AI; Clinical workflows
A Trust-Calibrated Federated Learning Framework for Predicting Behavioral Escalation in Children with Autism Using Edge IoT Systems
Behavioral escalation episodes in children with autism spectrum disorder present complex clinical, ethical, and technological challenges due to their unpredictability, individual variability, and the sensitivity of behavioral data. Artificial intelligence and Internet of Things technologies have demonstrated potential for early escalation detection; however, centralized learning architectures introduce privacy risks, governance challenges, and caregiver mistrust. This study proposes a trust-calibrated federated learning framework deployed on edge IoT systems to predict behavioral escalation while preserving data locality and embedding human trust directly into the learning process. The framework integrates reinforcement learning for temporal behavior modeling, confidence-weighted federated aggregation, and caregiver-driven trust calibration aligned with AI risk management principles. Experimental evaluation using simulated autism care scenarios demonstrates improved prediction accuracy, reduced false alerts, and measurable gains in caregiver trust compared to centralized and non-trust-aware baselines. The proposed approach advances privacy-preserving, human-centered artificial intelligence for autism care and provides a scalable pathway for ethically aligned deployment in real-world environments.
Autism spectrum disorder; Federated learning; Edge IoT; Behavioral escalation prediction; Trustworthy AI; Reinforcement learning; Human-centered AI
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
Multimodal Behavioral Signal Fusion Using Wearable IoT and Context-Aware Machine Learning for Early Autism Escalation Detection
Behavioral escalation in children with autism spectrum disorder is a multifactorial phenomenon influenced by physiological, behavioral, and environmental factors that interact dynamically over time. Early detection of escalation remains challenging because many existing systems rely on single-modality signals or static thresholds that fail to capture individualized and context-dependent patterns. This study proposes a multimodal behavioral signal fusion framework that integrates wearable Internet of Things sensing with context-aware machine learning to enable early and reliable escalation detection. The framework combines physiological indicators, motion dynamics, and environmental context using adaptive feature weighting and temporal modeling to capture personalized escalation signatures. Experimental evaluation using simulated autism care scenarios informed by empirical studies demonstrates that multimodal fusion significantly improves early detection accuracy and robustness compared to unimodal approaches while reducing false alerts. The proposed architecture advances personalized, privacy-aware autism care by leveraging context-aware intelligence for proactive intervention.
Autism spectrum disorder; Multimodal data fusion; Wearable IoT; Context-aware machine learning; Behavioral escalation detection; Personalized monitoring; Smart healthcare
Operationalizing the NIST AI Risk Management Framework for AI-Driven Autism Care Systems: A Governance-Centric Technical Architecture
Artificial intelligence–driven systems are increasingly deployed in autism spectrum disorder care to support behavioral monitoring, escalation prediction, and caregiver decision-making. While these systems offer substantial clinical and operational benefits, they also introduce risks related to safety, privacy, bias, accountability, and trust—particularly when applied to vulnerable pediatric populations. The NIST Artificial Intelligence Risk Management Framework provides high-level guidance for managing AI risks, yet practical operationalization within real-world autism care systems remains limited. This study proposes a governance-centric technical architecture that embeds NIST AI Risk Management Framework principles directly into the design, deployment, and lifecycle management of AI-driven autism care systems. The framework translates abstract governance functions into concrete technical controls, audit artifacts, and operational workflows aligned with reinforcement learning, IoT-based behavioral monitoring, and caregiver decision support. Through a structured risk taxonomy, control catalog, and implementation blueprint, this research demonstrates how trustworthy AI principles can be transformed into deployable, auditable, and scalable autism care solutions.
Autism spectrum disorder; AI governance; NIST AI Risk Management Framework; Trustworthy AI; Clinical decision support; AI safety; Risk-aware system architecture
Systematic Review and Meta-Analysis of 2024–2025 Studies on AI Arabic Translation, Linguistics and Pedagogy
This study aims to conduct a systematic review (SR) and meta-analysis (MA) of twenty articles by the author published between 2024–2025 on the use of AI models such as Microsoft Copilot (MC), DeepSeek (DS), and Google Translate (GT) in translation, linguistics, and education. It aims to answer the following question: What do the author’s 2024–2025 AI studies collectively reveal about AI performance, limitations, and sociolinguistic implications? After identifying the corpus, applying eligibility (inclusion and exclusion) criteria, describing corpus characteristics, information sources, study design, data extraction, quality assessment, meta-analysis procedures, and data synthesis, and presenting the PRISMA flow, the articles were classified into seven thematic clusters: AI translation of technical terms and metaphorical expressions; AI and phonological processing; AI’s ability to recognize and decode Arabic, Japanese, and Chinese calligraphic text images; AI interaction with real-world discourse, political language, and encrypted communication; AI and student translators; linguistic competence of four AI models and the different errors they produce; and human attitudes toward AI and academic practices. The SR and MA results revealed varied AI translation performance across domains, with technical terms, metaphorical expressions, and culturally embedded terminology presenting distinct challenges. Subclusters highlight differences in domain-specific terminology, structural patterns, folk expressions, and diachronic shifts. AI struggled with emphatic negation and full-text academic discourse but showed moderate success in chemical translation compared to human translators. Phonological interpretation and encrypted Arabic posed additional complexity, while calligraphic decoding and linguistic reasoning showed partial recognition. Temporal patterns also emerged: studies from early 2025 showed lower accuracy and higher hallucination rates, while those from late 2025 reflected improved performance, likely due to incremental model updates and domain accessibility. The MA used proportion-based effect sizes and a mixed-methods synthesis, combining quantitative accuracy measures with qualitative discourse analysis. Finally, human attitudes toward AI-generated academic work ranged from cautious acceptance to critical rejection, shaped by perceived quality, ethical concerns, and disciplinary norms. Together, the findings offer a nuanced understanding of AI’s evolving linguistic behavior and its implications for translation pedagogy, academic integrity, and cross-cultural communication.
Meta-analysis, systematic review, AI translation studies, 2024-2025 studies, Al-Jarf’s AI research, linguistic performance, AI error analysis, AI calligraphic text recognition, AI phonological processing, translation pedagogy
Explainable Reinforcement Learning for Caregiver Decision Support in Autism: A Human-in-the-Loop Safety Architecture
Behavioral escalation in children with autism spectrum disorder (ASD) presents significant challenges for caregivers due to its unpredictability, individualized triggers, and potential safety implications. Artificial intelligence–based decision support systems, particularly those using reinforcement learning, have shown promise in anticipating escalation events and recommending timely interventions. However, the opacity of many reinforcement learning models and the absence of meaningful human oversight undermine caregiver trust, ethical acceptability, and real-world adoption. This study proposes an explainable reinforcement learning framework embedded within a human-in-the-loop safety architecture for caregiver decision support in autism care. The framework integrates interpretable state representations, policy-level explanation mechanisms, and caregiver override and feedback loops to ensure transparency, accountability, and shared decision authority. Using simulated autism care scenarios informed by prior empirical studies, the proposed system is evaluated on prediction accuracy, intervention appropriateness, caregiver trust, and override frequency. Results indicate that explainability-aware reinforcement learning improves caregiver confidence and decision quality while maintaining competitive predictive performance. This research advances trustworthy, human-centered artificial intelligence by operationalizing explainability and safety as core design principles rather than post hoc additions.
Autism spectrum disorder; Explainable artificial intelligence; Reinforcement learning; Human-in-the-loop systems; Caregiver decision support; AI safety; Trustworthy AI
Generative AI for Supplier Relationship Management: Applications, Challenges, and Future Directions
This research paper explores the transformative potential of generative artificial intelligence (AI) in supplier relationship management (SRM). As organizations face increasingly complex global supply chains, generative AI offers promising capabilities to enhance communication, optimize negotiations, predict supplier performance, and mitigate risks. This paper examines current applications, implementation challenges, and future directions for generative AI in SRM. By analyzing emerging use cases and technological developments, we identify key opportunities for organizations to leverage generative AI in creating more resilient, efficient, and collaborative supplier relationships. While significant challenges remain, including data privacy concerns and integration with existing systems, the strategic deployment of generative AI in SRM represents a competitive advantage for forward-thinking organizations. This study investigates the transformative role of generative artificial intelligence (AI) in supplier relationship management (SRM). With the growing complexity of global supply chains, generative AI presents significant opportunities to enhance communication, streamline negotiations, predict supplier performance, and mitigate risks. The paper delves into current applications of generative AI, the challenges encountered during implementation, and potential future directions for SRM. By examining innovative use cases and recent technological advancements, we highlight critical opportunities for organizations to harness generative AI to foster more resilient, efficient, and collaborative relationships with suppliers. Despite notable challenges, such as data privacy issues and integrating AI with existing systems, the strategic application of generative AI in SRM can provide a distinct competitive edge for progressive organizations.
Generative AI, SRM, LLM, GAN, Simulations, Machine Learning
Copyright (c) 2026 Frontiers in Computer Science and Artificial Intelligence
A Spatio-Temporal AI Framework for Ecosystem Monitoring and Climate-Resilient Community Planning
Rapid environmental changes driven by climate variability and human activities are placing unprecedented stress on ecosystems and the communities that rely on them. Traditional ecological monitoring systems, while valuable, often struggle to capture the complexity of spatio-temporal processes influencing biodiversity loss, vegetation dynamics, soil erosion, hydrological fluctuations, and hazard exposure. Spatio-temporal machine learning (ML) models offer a transformative analytical approach, integrating satellite imagery, sensor data, historical climate records, and ecological metrics to assess ecosystem health with high spatial precision and temporal continuity. This paper examines how spatio-temporal ML architectures—including Convolutional LSTMs, Spatio-Temporal Graph Neural Networks (ST-GNNs), and hybrid CNN-Transformer models—can be leveraged to monitor ecosystems and strengthen community resilience. We propose a multi-scale Spatio-Temporal Resilience Monitoring Framework (STRMF) that synthesizes environmental data across scales to identify early warning signals of ecosystem stress, land-cover changes, vegetation decline, floodplain shifts, wildfires, and water-resource exploitation. Evaluations using simulated datasets and historical Earth Observation archives demonstrate that spatio-temporal ML models outperform static spatial or purely temporal predictors. Convolutional LSTM networks, for instance, accurately predict vegetation health by capturing spatial neighborhood dependencies alongside weekly temporal dynamics, while ST-GNNs reveal latent connectivity patterns in watersheds and ecological corridors, highlighting upstream disturbances that may threaten downstream communities. Spatio-temporal insights further enhance community resilience by linking ecological indicators with social exposure metrics such as agricultural dependence, water accessibility, settlement patterns, and vulnerability to hazards. The predictive risk maps generated through STRMF support climate-sensitive planning, sustainable resource allocation, and disaster-risk mitigation, guiding interventions such as riverbank reinforcement, selection of drought-resistant crops, early-warning activation, and sustainable land-use zoning. Incorporating Explainable AI (XAI) methods ensures transparency in ecological drivers, enabling policymakers and communities to understand the underlying causes of ecosystem stress and the consequences of inaction. Overall, this research indicates that next-generation climate governance can be informed by spatio-temporal ML models, facilitating data-driven, anticipatory, and ecosystem-focused resilience planning. By integrating ecological science, climate technology, and community-informed decision-making, these models empower societies to proactively respond to environmental changes, safeguarding biodiversity, livelihoods, and long-term sustainability.
Spatio-Temporal Machine Learning, Ecosystem Monitoring, Community Resilience, Climate Data Integration, Convolutional LSTM, Graph Neural Networks
Hybrid Deep Learning and Machine Learning Approaches for Industrial Power Load Forecasting: A Review
Industrial Power Load in the United States is rising gradually due to rapid manufacturing facilities enhancement in the recent year. Due to this expansion, accurate forecasting of industrial electricity load plays a vital role for power generation, transmission, and distribution planning for a specified manufacturing zone. The statistical model of forecasting faces a significant challenge due to the nonlinearity and multi scale nature of industrial load data. But recent application of Hybrid Deep Learning and Machine Learning models has demonstrated superior performance in industrial power load forecasting over statistical forecasting model. This paper presents a structured and comparative review of the recent Hybrid Deep Learning and Machine Learning based Data driven approaches for Industrial power load forecasting. The findings of our study highlight the effectiveness of the hybrid approach for reliable and high precision industrial power load forecasting which enhances the intelligent energy management in modern industrial systems.
Ethical and Human-Aligned Artificial Intelligence for Public Welfare, Financial Integrity, and Pediatric Healthcare Decision Systems
Artificial intelligence increasingly governs decisions in public welfare administration, financial integrity systems, and pediatric healthcare, where errors, bias, or opacity can result in significant human harm. While advances in predictive modeling have improved efficiency and scale, insufficient ethical alignment, transparency, and human oversight continue to undermine trust and legitimacy. This research proposes an ethical, human-aligned artificial intelligence framework that integrates behavioral analytics, explainable decision modeling, trust calibration, and governance-aware controls across public-sector, financial, and healthcare environments. Drawing on prior work in autism behavioral prediction, IoT-enabled health monitoring, financial fraud detection, cybersecurity, human-centered AI, and ethical governance frameworks, the study develops a unified methodology for responsible AI deployment. Through cross-domain simulation and analytical evaluation, the framework demonstrates improved fairness, reduced false positives, enhanced interpretability, and stronger alignment with human judgment. The findings underscore the necessity of embedding ethics and human alignment as core architectural properties in AI systems operating within high-impact socio-technical domains.
Ethical Artificial Intelligence; Human-Centered AI; Public Welfare Systems; Financial Integrity; Autism Healthcare Analytics; AI Governance
Cross-Domain Trust-Aware Artificial Intelligence for Behavioral Risk Prediction in Pediatric Healthcare, Financial Systems, and Public Decision Environments
Artificial intelligence is increasingly deployed in domains where automated decisions directly affect vulnerable populations, including pediatric healthcare, financial security, and public welfare systems. While predictive accuracy has advanced substantially, the absence of trust calibration, explainability, and cross-domain risk governance continues to limit real-world adoption. This research proposes a cross-domain, trust-aware artificial intelligence framework that integrates behavioral analytics, Internet of Things–enabled data collection, explainable decision modeling, and ethical risk governance. The framework unifies insights from pediatric autism care, financial fraud detection, cybersecurity, and public-sector decision systems to demonstrate how behavioral intelligence can be leveraged responsibly across socio-technical environments. Drawing on reinforcement learning–based autism monitoring, cloud IoT architectures, human-centered AI principles, financial behavior analytics, and ethical AI governance models, the study develops a unified methodology for trustworthy decision automation. Simulated evaluations demonstrate reduced false alerts, improved human trust, and stronger alignment between automated outputs and stakeholder judgment. The findings highlight the necessity of trust-aware, explainable, and ethically governed AI architectures for sustainable deployment in high-impact domains.
Trustworthy Artificial Intelligence; Behavioral Analytics; Autism Spectrum Disorder; Explainable AI; Financial Fraud Detection; Ethical AI Governance
Explainable Trust-Centric Artificial Intelligence for Integrated Healthcare, Financial Security, and Cyber-Risk Management
The rapid deployment of artificial intelligence across healthcare, finance, and cybersecurity has intensified concerns regarding transparency, trust, and ethical accountability in automated decision-making systems. While predictive models demonstrate strong performance in isolated domains, their real-world adoption remains constrained by limited explainability and insufficient alignment with human judgment. This research proposes an Explainable Trust-Centric Artificial Intelligence (ETC-AI) framework that unifies behavioral analytics, explainable machine learning, and governance-aware risk modeling across healthcare, financial security, and public systems. Drawing on advances in autism behavioral monitoring, cloud-based IoT architectures, cybersecurity for connected medical devices, financial fraud detection, and ethical AI for welfare systems, the framework operationalizes trust as a measurable and adaptive system property. Through cross-domain simulation and analytical evaluation, the study demonstrates improved interpretability, reduced false alerts, and enhanced decision confidence among human stakeholders. The findings support a shift toward explainable, trust-centric AI architectures capable of responsibly managing risk across interconnected socio-technical domains.
Explainable Artificial Intelligence; Trust-Centric AI; Cybersecurity; Financial Fraud Detection; Healthcare Analytics; Ethical AI Governance
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 and Data Analytics 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 Business Intelligence and Data Analytics 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 Business Intelligence and Data Analytics 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 with implications interpreted specifically within the U.S. manufacturing and logistics context.
Business Intelligence; Data Analytics; Supply Chain Performance; Manufacturing Systems; Logistics Networks; End-to-End Supply Chain; Data-Driven Decision-Making; U.S. Manufacturing
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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Predictive Analytics for Smart City Energy Management Using Machine Learning Techniques
Accurate short-term electricity demand forecasting is a critical requirement for data-driven planning and operational support in smart city energy systems. Urban energy consumption exhibits strong temporal dependencies, nonlinear behavior, and sensitivity to exogenous factors such as weather and human activity patterns, which limit the effectiveness of traditional linear forecasting methods. This study examines the application of machine learning techniques for short-term energy demand prediction, aiming to enhance forecast accuracy and interpretability in smart city energy management contexts. A comparative modeling framework is developed, using linear regression as a baseline and Random Forest, Gradient Boosting, and Long Short-Term Memory (LSTM) networks as advanced learners. The models are trained on historical load data enriched with temporal and meteorological features and evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). The results demonstrate that nonlinear and sequence-based models consistently outperform linear baselines, with the LSTM achieving the lowest error and highest explanatory power by effectively capturing temporal dynamics in urban energy demand. Feature-importance analysis across models reveals that recent historical load is the dominant predictor, complemented by calendar effects and weather variables, underscoring the combined influence of behavioral regularity and environmental conditions. These findings indicate that machine learning-based forecasting models provide a more accurate and informative basis for short-term energy planning in smart cities, supporting data-driven decision-making while highlighting the need for robustness and interpretability in real-world deployments.
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
Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0
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
Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
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
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