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
Adaptive Threat Detection Framework for IoT-Enabled Healthcare, Financial, and Connected Systems in the United States
Internet of Things (IoT) technologies have significantly transformed healthcare, finance, and smart connected systems by providing real-time communication and automation and intelligent data processing. Although all these developments have occurred, the fact that such a large number of IoT devices are becoming interconnected has created significant cybersecurity concerns and new opportunities for sophisticated cyber threats like Distributed Denial of Service (DDoS), botnet attacks, brute force attacks, spoofing, and malware attacks to target critical infrastructures. In IoT environments, which can be dynamic and heterogeneous, traditional intrusion detection systems are not effective in detecting new and changing attack patterns. This work introduces an Adaptive AI-Driven Threat Detection Framework aimed at safeguarding IoT healthcare, financial, and connected systems, which entails intelligent detection of anomalies and threat analysis in real time. The proposed framework leverages a realistic IOT network traffic dataset called CICIoT2023 that includes a wide variety of attack categories and realistic network traffic for modern cybersecurity research. Technologies such as Advanced machine learning algorithms, deep learning algorithms including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) and Random Forest are applied to classify malicious traffic and benign traffic efficiently. The framework includes adaptive learning mechanisms that are able to continually analyze the behaviors of the network and increase the accuracy of intrusion detection and decrease false positive rates. To improve the performance of the system and to optimize the computational time, data pre-processing, feature extraction, normalization, and model optimization techniques are used. Experimental results prove that the proposed scheme is accurate, precise, recall and F1-score in the detection of cyber threats in heterogeneous IoT networks. This study plays a key role in advancing the field of scalable and intelligent cybersecurity solutions designed to safeguard sensitive healthcare data, financial transactions, and smart infrastructures that are integrated into the digital landscape. The proposed architecture also offers important lessons in the integration of AI and adaptive security mechanisms into future IoT cybersecurity solutions.
Internet of Things (IoT), Adaptive Threat Detection, Artificial Intelligence, Intrusion Detection System, Deep Learning and Cybersecurity
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Operationalizing Post-Quantum Cryptography in Multi-Cloud Environments
The emergence of quantum computing is a serious threat to traditional cryptographic algorithms, making it critical to transition to post-quantum cryptography (PQC). This work proposes a holistic conceptual and analytical framework for deploying PQC in multi-cloud environments where the complexity of different infrastructure, workloads and key management schemes pose significant security and efficiency problems. The proposed framework for crypto-agility follows a layered approach, with a cryptographic abstraction layer, a policy-based orchestration engine, a centralized hardware security module (HSM) management plane, and an ongoing monitoring module to facilitate a smooth transition from classical to hybrid and fully quantum-resistant cryptographic systems. He researches identifies and critically analyzes major challenges in deploying PQC technologies in multi-cloud environments, such as inter-cloud interoperability, computational complexity, latency, key management, and regulatory and compliance considerations. The framework tackles these challenges by incorporating hybrid cryptographic protocols that blend classic cryptographic algorithms with NIST-approved PQC algorithms, namely ML-KEM (FIPS 203) and ML-DSA (FIPS 204), to maintain backward compatibility and increase security against quantum-powered adversaries. Moreover, the framework proposes dynamic policy-driven algorithm selection to implement context-sensitive cryptographic enforcement based on sensitivity labels, security threats, and regulatory considerations. An experimental framework is proposed to assess the efficiency and scalability of PQC algorithms deployed in a multi-cloud environment, including metrics such as CPU usage, memory usage, latency and throughput with different workloads. Although this research is conceptual, it draws on previous benchmarking to discuss the securityperformance trade-offs associated with PQC. The results highlight the need for centralized trust anchors, crypto-agility and hybrid deployment models to address risks from cryptographic diversity and ensure the sustainability of security operations. This study advances the research on quantum-safe cloud security by offering a systematic, scalable and policy-oriented framework for PQC deployment. It provides practical guidance for businesses, cloud architects, and security professionals on how to safeguard against quantum vulnerabilities and ensure efficient operations in distributed systems by implementing quantum-safe cryptographic infrastructures.
Post-Quantum Cryptography (PQC), Multi-Cloud Security, Crypto-Agility, ML-KEM, ML-DSA, Hybrid Cryptography, Hardware Security Module (HSM), Quantum-Safe Security, Cloud Computing, Key Management, Cryptographic Migration, Distributed Systems Security, Policy-Driven Security, Quantum Computing Threats 1
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Artificial Intelligence and Data Analytics in U.S. Mental Health Insurance: Predictive Risk Modeling, Cost Efficiency, and Equitable Access
The integration of Artificial Intelligence and data analytics into the United States mental health insurance system is becoming structurally necessary. Traditional actuarial models rely heavily on backward looking claims data, which fail to capture early stage mental health risks and lead to delayed interventions, rising costs, and unequal access. This study examines how Artificial Intelligence driven predictive risk modeling can transform mental health insurance by identifying high risk individuals earlier, improving resource allocation, and enhancing care outcomes while controlling costs. Using machine learning techniques such as supervised learning, natural language processing, and behavioral pattern recognition, insurers can integrate diverse data sources including electronic health records, social determinants of health, and real time behavioral indicators into dynamic risk assessments. The paper also evaluates the economic impact of Artificial Intelligence adoption and shows that predictive analytics can reduce avoidable hospitalizations, stabilize claims patterns, and improve cost efficiency through targeted preventive care. However, these benefits are not automatic. Without careful design, Artificial Intelligence systems can reinforce existing disparities, especially among underserved populations with limited data representation. This study therefore examines issues related to algorithmic bias, transparency, and regulatory compliance, emphasizing the importance of ethical frameworks and governance mechanisms. Finally, the research explores how Artificial Intelligence can promote equitable access by supporting personalized care pathways and expanding insurance inclusivity, particularly in rural and low income populations. The findings indicate that Artificial Intelligence has strong potential to reshape mental health insurance, but its success depends on disciplined implementation, policy alignment, and accountability. The study concludes that the real advantage for insurers lies not in adopting Artificial Intelligence, but in using it responsibly to balance financial performance, risk management, and social equity.
Data Analytics, Mental Health Insurance, Predictive Risk Modeling, Cost Efficiency, Algorithmic Bias, United States Healthcare System, Insurance Analytics, Behavioral Health Data
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Artificial Intelligence and Big Data for Precision Medicine: A Review of Bioinformatics-Driven Healthcare Applications
Healthcare is in the middle of a quiet but profound shift. Genomic sequencers, hospital information systems, wearables and imaging archives now generate data faster than clinicians can read it, and that flood is reshaping what “evidence-based care” means. We review more than forty recent studies that bring artificial intelligence (AI), machine learning and big-data analytics into bioinformatics and precision medicine, spanning oncology, drug discovery, cardiology, neurology, public-health surveillance and healthcare operations. Reported accuracies and AUCs range from roughly 80% in early drug-discovery pipelines to above 94% in deep-learning-based pancreatic and breast imaging. Yet our reading also suggests a more cautious story: many models still suffer from limited external validation, opaque decision logic and uneven access to high-quality multi-omics data. We propose a layered conceptual framework that connects heterogeneous data sources, federated and privacy-preserving pre-processing, predictive and explainable AI engines, and downstream clinical applications. The paper closes with a discussion of remaining barriers, interpretability, fairness, regulatory uncertainty and workflow integration and outlines research directions for the next several years.
Precision medicine, bioinformatics, machine learning, multi-omics, explainable AI, big data analytics, clinical decision support
Failure-Mode Analysis, Hallucination Detection, and Prompt-Injection Testing: A Production-Readiness Framework for Enterprise Agentic AI
As large language models increasingly move from experimental chat interfaces into enterprise workflows, the primary challenge is no longer model capability alone but production trustworthiness. Agentic AI systems introduce new risk surfaces because they can retrieve information, reason across context, call tools, act on user intent, and interact with external systems. This article proposes a practical production-readiness framework centered on three pillars: failure-mode analysis, hallucination detection, and prompt-injection testing. Failure-mode analysis helps teams identify where agentic systems can break under real operating conditions. Hallucination detection evaluates whether generated outputs remain faithful, factual, and grounded in approved sources. Prompt-injection testing examines whether malicious or conflicting instructions can override system intent, expose sensitive information, or trigger unauthorized actions. Together, these practices convert AI reliability from an informal review process into an evidence-driven engineering discipline. The article argues that enterprises should treat these controls as mandatory gates before deploying large language model and agentic AI systems into high-impact business workflows.
Agentic AI; LLM evaluation; hallucination detection; prompt injection; AI red teaming; enterprise AI governance; failure-mode analysis; production readiness
AI-Based PCB Layout Constraint Manager: Intelligent Automation for High-Speed PCB Design Rule Enforcement
Modern printed circuit board (PCB) design for high-performance computing systems — including server-grade processors, graphics processing units, and AI accelerators — demands increasingly rigorous layout constraints. Traditional constraint management systems rely on static rule sets, manual configuration, and repetitive engineer oversight, all of which introduce bottlenecks and errors at scale. This article presents an AI-based PCB Layout Constraint Manager (AI-PLCM), a system developed at Advanced Micro Devices, Inc. (AMD) to intelligently automate the generation, validation, and optimization of PCB layout constraints. The proposed system leverages machine learning models trained on historical board designs, natural language processing for constraint specification, and graph-neural-network-based routing analysis to deliver real-time constraint checking and adaptive recommendations. Results from deployment across multiple AMD product lines demonstrate a 47% reduction in design rule violations at tape-out, a 31% improvement in first-pass layout success rate, and significant reductions in engineering review cycle time. This work underscores the transformative potential of AI integration in electronic design automation (EDA) workflows.
PCB design, layout constraints, design rule checking (DRC), electronic design automation (EDA), machine learning, graph neural networks, natural language processing, high-speed design, signal integrity, AMD
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Intelligent Cloud Monitoring Using AIOps in the Microsoft Azure Ecosystem
The growing complexity of cloud-native applications has made conventional monitoring techniques ineffective for ensuring system performance and reliability. This research explores the use of Artificial Intelligence for IT Operations (AIOps) in the Microsoft Azure environment to achieve intelligent, automated and predictive cloud monitoring. Our system leverages Azure Monitor, Log Analytics and Azure Machine Learning to ingest and analyses extensive telemetry data, such as logs, metrics and distributed traces. An integrated machine learning framework, which combines unsupervised anomaly detection (such as clustering and statistical thresholding) with supervised learning models is used to detect anomalies in real time. The framework is validated with cloud workload datasets from cloud-native microservices deployed in the Azure Kubernetes Service (AKS). Self-healing mechanisms through automation workflows using Azure Automation and Logic Apps help automate responses. The results of the experimental studies show that the proposed AIOps approach enhances anomaly detection rates up to 92% and lowers mean time to resolution (MTTR) by around 30-40% when compared to traditional monitoring systems. The framework is scalable, flexible and cost-effective in cloud ecosystems. This study demonstrates the value of combining AI analytics with cloud-based observability tools to shift IT operations from reactive to proactive and self-healing modes.
Keywords AIOps; Cloud Computing; Microsoft Azure; Azure Monitor; Log Analytics; Machine Learning; Predictive Analytics; Anomaly Detection; Cloud-Native Systems; IT Operations Automation; Self-Healing Systems; Observability.
Copyright (c) 2026 Frontiers in Computer Science and Artificial Intelligence
B2B Integration Patterns in the Chemical Industry
B2B integration patterns in the chemical industry are structured methods, models and communication protocols that support the exchange of information and coordination of activities between manufacturers, suppliers, distributors, logistics partners, and regulatory agencies. The chemical industry is tightly regulated, safety-sensitive and globally integrated, necessitating robust B2B integration patterns to enable efficient operations, compliance, and supply chain resilience.This paper explores the main B2B integration patterns used in the industry, such as point-to-point, hub-and-spoke, enterprise service bus (ESB), and API-led integration. Although traditional systems like Electronic Data Interchange (EDI) are still prevalent for exchanging standardized documents, they are increasingly augmented or replaced by more agile integration approaches that use Application Programming Interfaces (APIs), cloud-based integration services (iPaaS) and event-driven integration patterns.The paper also discusses the importance of industry standards like GS1 (formerly Chemical Industry Data Exchange - CIDX) that specify data exchange protocols and formats for the chemical supply chain. The paper assesses the impact of these integration patterns on key processes such as order-to-cash, procure-to-pay, stock management, and regulatory compliance. Special attention is given to the secure sharing of safety and regulatory data such as Safety Data Sheets (SDS) to comply with international regulations such as REACH and the Globally Harmonised System of Classification and Labelling of Chemicals (GHS). Additionally, the research examines emerging patterns such as digital transformation programs, integration in the cloud, blockchain for traceability, and artificial intelligence for predictive analytics in supply chain. It also considers key issues with data integration, legacy systems and security. Particular emphasis is placed on contemporary security considerations in B2B integration, such as data sovereignty, partner identity and access management, and API security (including OAuth 2.0 and certificate authentication), which are critical for safeguarding data in a connected ecosystem. Overall, successful integration patterns improve visibility, agility and collaboration within the chemical supply chain. Through the integration of strategies with industry standards and the adoption of emerging security and digital technologies, companies can enhance efficiency, compliance and maintain their competitive edge in a rapidly evolving data-driven world.
Keywords: B2B integration, chemical industry, Electronic Data Interchange (EDI), Application Programming Interfaces (APIs), supply chain integration, data interoperability, cloud integration (iPaaS), digital transformation, regulatory compliance, Chemical Industry Data Exchange, REACH Regulation, Globally Harmonized System of Classification and Labelling of Chemicals, event-driven architecture, enterprise integration patterns, chemical supply chain.
Governance Readiness Beyond Predictive Performance: An Empirical Benchmark for Higher-Education Early Warning Systems
Higher-education early warning systems (EWS) are predominantly evaluated on predictive discrimination, yet institutional deployment requires that models simultaneously satisfy calibration, explanation stability, subgroup fairness, and operational feasibility. This paper proposes and empirically evaluates a structured governance-readiness benchmark comprising four measurable domains applied to the 2020 Beginning Postsecondary Students Longitudinal Study (BPS:20/22), a nationally representative complex survey of approximately 22,320 first-time undergraduates representing roughly 3.3 million students nationally. The study compares a gradient-boosted classifier (XGBoost) and an institution-aware multilevel logistic regression under a survey-weighted evaluation protocol with balanced repeated replication (BRR) variance estimation and bootstrap explanation stability testing. BPS:20/22 is used as a national governance benchmark; it does not represent a real-time campus intervention system. Findings indicate that the model achieving higher discrimination (AUC: 0.814 vs. 0.772, BRR 95% CIs: [0.801, 0.827] and [0.758, 0.786]) exhibits substantially lower explanation stability (rank-stability ρ_S: 0.713 vs. 0.893), larger subgroup disparities across race/ethnicity and income dimensions, and lower operational efficiency within the constrained alert threshold. Under four of five institutional priority scenarios summarizing predictive, calibration, stability, and fairness dimensions, the governance-readiness composite favors the institution-aware model. Operational actionability, evaluated as a parallel deployment constraint, similarly indicates that accuracy-based selection may underestimate accountability risk under discrimination-centered evaluation. The paper concludes with a recommendation for Algorithmic Impact Statements as a recommended minimum governance disclosure practice and provides a reproducible benchmark framework subject to restricted-use data access for institutional EWS review.
Early warning systems; governance readiness; explainability stability; algorithmic fairness; complex survey design; higher education analytics; BPS:20/22; benchmark evaluation
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A Systematic Literature Review on the Taxonomy of Ransomware Attacks
Ransomware attacks actively threaten different entities including individuals along with businesses while endangering fundamental infrastructure through software weaknesses and deceptive email practices. The absence of standardized classification criteria in modern ransomware taxonomies hinders effective threat information sharing. This lack of uniformity also complicates response coordination. The research develops an extensive classification structure to systematize ransomware attack categorization by examining vector encryption techniques and ransom payment approaches. The research adopted a systematic literature review approach which aligns with PRISMA standards to study existing taxonomies before suggesting an enhanced classification scheme. Standardizing threat classification establishes better techniques to detect attacks respond to incidents and develop security policies which enable intelligence sharing. Security professionals will receive an organized system for ransomware classifying through these findings which leads to enhanced threat forecasting and cyber defense capabilities against developing threats. Keywords: Taxonomy, Ransomeware attack, vulnerabilities, Detection, Malware.
Systematic Literature Review;Taxonomy of Ransomware Attacks
Market basket analysis for healthcare services to identify bundled care offerings
The complexity of inpatient care, which has recently increased and is accompanied by the growing demand for cost-effective and coordinated treatment models, has enhanced the necessity of the use of evidence-based bundled care services in hospitals. Market Basket Analysis (MBA) is a time-tested data mining method that offers a systematic method of finding naturally occurring co-occurring clinical services, which respectively create care bundles. This study uses the association rule mining on the 2010 New York State SPARCS Hospital Inpatient Discharge data, which is a de-identified, large scale dataset of more than 2.6 million inpatient records including detailed data on diagnosis, procedures, service use, level of severity and payment source. Service-level item sets were generated using Apriori and FP-Growth algorithms, whereby CCS procedure codes, diagnostic groups and treatment occasions within each inpatient stay were clustered together and allowed identification of commonly occurring combinations of services. This study will identify the clinically significant trends in inpatient episodes that may be used in the design of cost-efficient bundled care. The association rules were tested by means of support, confidence and lift measurements in order to identify high-value service pairs and clusters within large inpatient categories like cardiology, orthopedics, obstetrics and general surgery. Findings indicate that there are strong synergies, e.g., cardiovascular imaging is always accompanied by a particular lab panel and surgical procedure, whereas orthopedic surgery visits often involve diagnostic tests and after-surgical care. These lessons point to service patterns that hospitals can use to develop standardized, value-based care bundles that can decrease fragmentation, improve clinical coordination, and help to optimize resources. This study illustrates the possibility of an MBA to change the approach of inpatient service analysis, which has been focused on descriptive reporting to direct clinical pathway design. Utilizing data on pre-2025 hospitals occurring in the real world, the results are used to advance data-driven bundled care models in accordance with the modern healthcare changes. The model can be expanded to multi-year datasets, outpatient services and predictive models of proactive care planning.
Market Basket Analysis, Bundled Care Offerings, Healthcare Data Mining, SPARCS Inpatient Dataset, Association Rule Mining and Hospital Service Utilization
Cognitive Hives: A Distributed Systems Theory for Conflict-Aware Multimodal Intelligence
The very recent developments in large-scale foundation models have shown impressive language, vision, and multimodal reasoning. But current architectures are still fundamentally monolithic and rely on centralized parameter scaling rather than structural distribution. Multimodal complexity heightens the constraints of monolithic systems with respect to their latency, interpretability, internal conflict management and scalability. The paper presents the idea of Cognitive Hives - a theory of distributed systems of conflict-aware multimodal intelligence. A Cognitive Hive is a system of special-purpose expert models that run with common temporal synchronization and defined conflict-arbitration rules. We define the architectural layers, communication semantics, arbitration functions, and temporal cohesion mechanisms required for stable distributed reasoning. The framework comprises a common time-based backbone, message relaying, and graphical conflict detection to enable scalable cooperative intelligence. We also examine infrastructure needs, relative structural features, and implications for the enterprise. Cognitive Hives signify a shift from scale-through-size to scale-through-structure and provide a principled approach to distributed artificial cognition.
Distributed AI, Multimodal Reasoning, Conflict Resolution, Temporal Synchronization, Cognitive Architecture, Hybrid Temporal Tokenization, Distributed Systems Theory, Arbitration Models
Artificial Intelligence for Sustainable and Climate-Resilient Apparel Supply Chains: A Narrative Review and Integrative Framework
This study examines the role of artificial intelligence (AI) in enhancing environmental sustainability and climate resilience in apparel supply chains. Although prior research has extensively explored sustainable supply chain management, circular economy practices, and AI-driven supply chain resilience, these streams remain largely fragmented. To address this gap, this paper adopts a narrative review approach, analyzing 25 peer-reviewed studies published between 2015 and 2021 across databases including Scopus, Web of Science, and Google Scholar. The findings reveal that apparel-specific research is strong in identifying sustainability challenges, such as environmental degradation, labor issues, and governance complexity, while AI and resilience studies provide advanced insights into predictive analytics, optimization, and risk management but are predominantly situated in general supply chain contexts. There is a clear lack of integration among AI capabilities, environmental sustainability, and climate resilience within apparel supply chains. This paper contributes by synthesizing these disconnected streams and proposing an integrated perspective that links AI technologies to supply chain functions, sustainability outcomes, and resilience capabilities. The study highlights that AI can serve as a bridging mechanism by improving forecasting, resource efficiency, and proactive risk management, while also identifying key barriers such as data limitations, lack of transparency, and organizational readiness. The findings have important implications for researchers, managers, and policymakers seeking to develop greener, more resilient apparel supply chains.
Artificial Intelligence (AI); Apparel Supply Chains; Sustainable Supply Chain Management (SSCM); Climate Resilience; Circular Economy; Supply Chain Resilience; Environmental Sustainability
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Artificial Intelligence in Project Management: A Systematic Review of Applications, Challenges, and Future Directions
Artificial intelligence (AI) has emerged as a transformative technology in project management, offering advanced capabilities for data-driven decision-making, predictive analytics, and process automation. This study presents a systematic literature review of AI applications in project management, focusing on peer-reviewed journal articles published between 2015 and 2021. A total of 25 relevant studies were selected following PRISMA guidelines and analyzed using both thematic and bibliometric approaches. The findings reveal that AI is predominantly applied in areas such as project scheduling, cost estimation, risk management, safety monitoring, and project portfolio selection, with a strong concentration in construction project management. Machine learning and deep learning techniques are widely used, enabling enhanced prediction accuracy and real-time monitoring. The thematic analysis highlights key benefits, including improved efficiency, better decision-making, and enhanced safety performance. However, significant challenges remain, particularly related to data quality, model interpretability, organizational readiness, and ethical concerns. The bibliometric analysis further demonstrates that research in this field is structured around four main dimensions: AI techniques, application domains, performance outcomes, and emerging challenges. A temporal shift from traditional optimization methods to advanced data-driven approaches is also observed. This study contributes to the existing literature by providing a comprehensive synthesis of AI applications in project management and identifying critical research gaps. The findings suggest that future research should focus on explainable AI, integration with emerging technologies such as digital twins and IoT, and the development of human–AI collaborative systems to enhance project management practices.
Artificial intelligence, project management, machine learning, systematic review, bibliometric analysis, construction management
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AI-Driven Digital Twin Framework for Real-Time Optimization of Smart Manufacturing Systems in Industry 5.0
There has been tremendous improvement in terms of automation and connectivity owing to the implementation of smart manufacturing as part of Industry 4.0; however, some issues continue to hamper the process of real-time adaptation, prediction, and decision making based on humans. Industry 5.0 represents the new era where there is the need for manufacturing systems that are not only resilient but sustainable and human-centric. This calls for frameworks combining AI in cyber-physical systems. In this paper, a novel Digital Twin with AI technology is introduced to provide an optimized approach for manufacturing systems following the Industry 5.0 guidelines. This approach consists of an intelligent multi-level architecture including physical systems, IoT-based data acquisition, digital model building, AI-based analytics techniques, and human-in-the-loop decision-making. It allows constant synchronization between the two worlds of physics and cyberspace, leading to proactive decision making and better system performance. A systematic approach is described here including data pre-processing, modeling, use of machine learning, and improvements to the system based on feedback. The analysis using simulation techniques shows the ability of the approach to increase efficiency, reduce downtime, and optimize predictive maintenance through real-time optimization. Additionally, human-AI interaction provides for flexibility in dealing with situations in manufacturing settings. The suggested solution addresses the main challenges that exist in existing Digital Twin applications by emphasizing such key factors as scalability, interoperability, and intelligence. This research provides a thorough conceptual framework that closes the gap between AI-powered analysis and manufacturing systems designed to serve humans. As it can be concluded from the results, Digital Twins enabled by artificial intelligence are expected to play a crucial role in future smart manufacturing.
AI-driven Digital Twin, Smart Manufacturing
Real-Time Optimization, Human-AI Collaboration, Predictive Analytics
Enhancing Retail Checkout Efficiency Through a Hybrid YOLOv8-Based Grocery Detection and Billing System
Traditional retail checkout systems that rely on barcodes are limited at handling the non barcoded shopping items like fresh produce. To tackle this problem, this paper will introduce a deep learning approach to grocery item detection and automated billing system through computer vision. A local dataset comprising 1,109 images in 27 grocery classes was gathered and labelled and augmented with Roboflow to enhance stability. Several object detection models based on the YOLO algorithm such as, YOLOv5s, YOLOv8n, YOLOv8s, YOLOv11n and YOLOv12n were trained and tested in unvaried conditions. Experimental outcomes demonstrate that more recent architectures are far superior in detecting, with YOLOv12n having the best localization accuracy on the test set (mAP@0.5:0.95 of 0.847), and the YOLOv8s giving good tradeoffs between accuracy and efficiency to be used in the real world. The chosen model (YOLOv8s) was incorporated into a web-based image-based billing system, which confirmed the possibility of a scalable and inexpensive AI-based checkout system. The suggested framework provides a platform upon which future research can proceed to enhance the real-time performance and dataset generalization.
Automated billing system, grocery item detection, object detection, YOLO, computer vision, deep learning, retail automation
A Secure Microservices Reference Architecture for Financial Cloud Systems Using Spring Boot and Event-Driven Design
The rising tendency of using cloud computing in the finance sector has increased the requirement for architectures that provide the advantages of high scalability and security compliance. Monolithic systems are no longer sufficient in satisfying the dynamic demands of modern finance applications, particularly in the processing of transactions in real-time and the security of sensitive information. Microservices architecture in combination with event-driven approaches has been identified as a promising solution in resolving the problems associated with the above issues. However, the existing approaches often fail to provide a unified framework in the context of security as a vital architectural component. This study introduces a secure reference architecture for microservices in financial cloud environments, developed using Spring Boot and incorporating event-driven design principles. The developed architecture includes all necessary components, such as API gateways, domain-driven microservices, event streaming platforms, and a comprehensive security component that is based on zero-trust concepts. It focuses on secure communication, identity-based access control, and data protection for all layers of the developed framework. The event-driven design concept facilitates real-time processing and loose coupling of microservices, thus improving responsiveness and fault tolerance. This framework is a significant development in financial environments and provides a foundation for further advancements in cloud-based financial systems.
Microservices Architecture; Financial Cloud Systems; Event-Driven Architecture; Spring Boot; Data Security
A 6G-Enabled AI-Powered Intelligent Healthcare Integrating IoMT and Edge Computing
Advancements in wireless communication technologies are reshaping healthcare by facilitating intelligent, real-time, and patient-centered services. Current 5G-based healthcare infrastructures face challenges related to latency, reliability, scalability, and data processing, limiting their support for applications such as remote robotic surgery, immersive telemedicine, and continuous critical patient monitoring. To overcome these issues, this study introduces a 6G-enabled Intelligent Healthcare System (6G-IHS) that combines the Internet of Medical Things (IoMT), edge intelligence, 6G communication, and AI-driven cloud computing within a multi-layered architecture. The framework targets stringent 6G requirements, including sub-millisecond latency, ultra-high data rates, and ultra-reliable low-latency communication (URLLC). A mathematical model characterizes key parameters such as end-to-end latency, data rate, reliability, energy consumption, and AI diagnostic performance. An edge processing mechanism is incorporated to lower latency and support real-time decisions, while AI enables predictive analytics and intelligent healthcare functions. Simulation results indicate that 6G-IHS surpasses traditional cloud-based and 5G systems, achieving latency under 1 ms, data rates above 100 Gbps, reliability over 99.9999%, and up to 30% energy reduction in IoMT devices. AI integration enhances diagnostic accuracy to 90–98%, supporting early detection and personalized treatment. This framework offers a scalable, secure, and efficient approach for future healthcare systems, illustrating the potential of 6G to advance intelligent, connected healthcare environments.
Artificial Intelligence (AI), Internet of Medical Things (IoMT), 6G, URLLC, 6G-HIS, Healthcare, Wireless communication, Robotic
Developing AI-Based Financial Forecasting and Cybersecurity Systems for the U.S. Digital Economy
The digital transformation of the U.S. economy has intensified the need for robust financial forecasting and proactive cybersecurity solutions. This study integrates advanced machine learning techniques—Long Short-Term Memory (LSTM), Prophet, and Random Forest Regressor—for financial time-series forecasting across twelve leading U.S. companies. In parallel, a cybersecurity framework was developed using the CICIDS2017 dataset and trained with a Random Forest Classifier to detect cyber intrusions. The forecasting models demonstrated high accuracy, with Random Forest achieving the best performance across most companies, while LSTM showed strong consistency in sequential trend learning. The cybersecurity model achieved an accuracy of 99.89% and an F1-score of 99.72%, effectively identifying attack patterns. Finally, a conceptual “AI Risk Intelligence Decision System” is proposed, fusing financial risk indicators with cyber threat intelligence to support early decision-making in digital financial operations. The proposed framework enhances predictive accuracy and cyber resilience, demonstrating strong potential for integration into real-world financial and cyber risk monitoring systems.
Enterprise Data Migration Strategies for High-Assurance Information Systems
Enterprise data migration is one of the essential parts of digital transformation strategies in the development and implementation of high-assurance information systems (HAIS), which include various fields such as healthcare services, defense systems, financial services, and smart manufacturing. The HAIS must meet specific data security, availability, and compliance requirements. However, traditional data migration approaches cannot meet these expectations because they cannot perform real-time validation, risk mitigation, and zero-downtime execution. This paper presents a novel AI-aided data migration framework that focuses on HAIS. The framework is based on the integration of pre-migration intelligence, secure execution, real-time validation, risk mitigation, and post-migration. Advanced technologies such as artificial intelligence and machine learning are used in data migration. Blockchain is also used to ensure data auditability. Additionally, digital twins are employed to ensure data validation. Adaptive data migration strategies such as hybrid and incremental approaches are also included in the framework. This paper presents a novel data migration model based on compliance-centric and risk-centric data migration. This model allows organizations to ensure reliable data migration. The paper proves that the integration of intelligent automation and real-time validation can significantly improve data migration accuracy.
Prise Data Migration, High-Assurance Systems, Artificial Intelligence, Data Integrity, Digital Transformation
From Utility Bills to Retrofit Finance: An AI Framework for Energy-Burden-Aware Underwriting in Residential Decarbonization
This study develops a literature-driven conceptual framework for energy-burden-aware underwriting within the context of U.S. residential decarbonization finance. It addresses a critical and growing disconnect between the policy-driven urgency to decarbonize residential buildings and the financial constraints faced by households most in need of retrofit investments. Notably, those households that stand to benefit the most from energy-efficiency improvements are often underserved by traditional underwriting models. Conventional lending frameworks prioritize stable income streams, established credit histories, and property-based collateral signals, whereas energy-burdened households—characterized by high utility costs, inefficient housing conditions, and constrained liquidity—exhibit affordability dynamics that are poorly captured by these models but are highly relevant for socially beneficial investment. Drawing upon interdisciplinary literature spanning energy burden, building decarbonization, alternative data underwriting, explainable artificial intelligence, and algorithmic accountability, this paper proposes an AI-enabled underwriting framework that integrates utility-bill histories, housing attributes, retrofit cost-benefit dynamics, and consumer protection considerations. Importantly, the framework emphasizes transparency and interpretability, avoiding reliance on opaque risk-scoring mechanisms. Methodologically, the study employs a structured integrative review of academic and policy-oriented sources, synthesizing insights into a multimodal framework encompassing data governance, feature engineering, model development, explainability outputs, fairness diagnostics, and financing pathway design. The contributions of this research are threefold. First, it reconceptualizes utility payment data from a signal of financial distress into a meaningful input for transition finance. Second, it establishes a direct linkage between household energy burden and underwriting design principles. Third, it outlines governance and accountability conditions under which AI-assisted retrofit financing can advance decarbonization objectives without reinforcing systemic exclusion. Given the absence of original empirical data, the findings are presented as a conceptual architecture, evaluation framework, and forward-looking research agenda rather than as empirical validation. The proposed framework is intended to guide future implementation and testing by financial institutions, utilities, public green banks, and housing policy stakeholders. paper develops a literature-based conceptual framework for energy-burden-aware underwriting in U.S. residential decarbonization finance. The study addresses a widening disconnect between the climate-policy imperative to decarbonize homes and the financial reality that many households most likely to benefit from retrofit investment are also the least well served by conventional underwriting. Existing lending models privilege stable income, established credit files, and property value signals, whereas the households facing high utility costs, inefficient housing, and limited liquidity often present precisely the affordability patterns that make retrofit investment socially valuable. Drawing on scholarship on energy burden, building decarbonization, alternative-data underwriting, explainable artificial intelligence, and algorithmic accountability, the paper proposes an AI framework that uses utility-bill histories, housing characteristics, retrofit economics, and consumer-protection rules to assess payment feasibility without reducing underwriting to opaque risk scoring. Methodologically, the paper uses a structured integrative review academic and policy sources and synthesizes them into a multimodal framework comprising data governance, feature design, model development, explanation outputs, fairness diagnostics, and financing-channel selection. The paper’s contribution is threefold: it reframes utility bills from a distress signal into a transition-finance input; it links household energy burden to underwriting design; and it specifies governance conditions under which AI-assisted retrofit finance may advance decarbonization without reproducing exclusion. Because no original dataset is analyzed, the results are presented as a conceptual model, evaluation logic, and research agenda rather than empirical claims. The framework is intended to support future testing by lenders, utilities, public green banks, and housing-policy practitioners.
Fintech; artificial intelligence; residential decarbonization; retrofit finance; energy burden; alternative data; algorithmic fairness; explainable AI; underwriting; climate finance
Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0
Machine Learning for Identifying Deepfake-Driven Identity Abuse, Authentication Evasion, and Customer Impersonation in U.S. Banking
Deepfake-enabled identity abuse has moved from a peripheral cyber-risk to an operational threat for U.S. banking, especially in remote onboarding, account recovery, contact-center authentication, and high-risk payment authorization. The problem is no longer limited to obvious synthetic media. Financial institutions increasingly face blended attacks that combine forged identity documents, face or voice cloning, injected video streams, social-engineering pressure, mule accounts, and adaptive retries across channels. This paper develops a publication-ready research framework for machine learning systems that identify deepfake-driven customer impersonation, authentication evasion, and identity abuse in U.S. banking environments. The study synthesizes regulatory guidance, public fraud data, biometric spoofing research, deepfake detection literature, and banking fraud analytics to propose a multimodal detection architecture spanning document forensics, face presentation attack detection, deepfake-video detection, speaker anti-spoofing, behavioral biometrics, device and network telemetry, graph-based entity resolution, and risk-calibrated decisioning. Real public evidence motivates the design: FTC data show reported fraud losses reached $12.5 billion in 2024, including $2.95 billion in imposter-scam losses, while FinCEN reported increasing suspicious activity narratives involving deepfake media targeting financial institutions. The proposed methodology treats identity abuse as a sequential, multimodal, and adversarial classification problem rather than a single-screen biometric check. The paper argues that the most effective defense is not one detector but an explainable ensemble with smart friction, human escalation, and governance aligned to NIST identity-proofing standards, anti-money-laundering expectations, and consumer-protection obligations. By linking technical detection with operational controls, the study provides a practical blueprint for authentication, resilient fraud prevention, and more trustworthy digital banking systems.
deepfakes; banking fraud; identity proofing; account takeover; customer impersonation; voice cloning; face anti-spoofing; graph neural networks; explainable AI
Machine Learning Models for Predicting Patient Treatment Switching Using Claims Data
Treatment switching - including discontinuation, add on therapy, and brand to generic substitution—can have meaningful clinical and economic consequences for patients, payers, and health systems. Administrative claims databases provide large scale longitudinal records of medication use and health care utilization, enabling the study of real world treatment trajectories; however, traditional analytic approaches often struggle to translate high dimensional claims signals into timely and actionable switching predictions. This paper reviews and synthesizes machine learning (ML) strategies that leverage claims data to predict treatment switching across multiple therapeutic areas, including rheumatology, neurology, psychiatry, and cardiovascular disease. The review summarizes major methodological approaches—tree based ensembles, regularized regression, neural networks, and temporal/sequential models—and highlights common feature engineering practices for handling high dimensionality and class imbalance. Across the referenced studies, ML approaches generally improve predictive performance compared with simpler baselines, with clinical phenotype proxies, prior medication burden, utilization intensity, and adherence patterns frequently emerging as important predictive signals. The paper also discusses implications for clinical decision support, real world evidence generation, and market access strategy. Persistent challenges include sparse clinical granularity in administrative data, heterogeneity in switching definitions, class imbalance, and limited cross system transportability; integrating claims with richer clinical sources (e.g., EHR) and performing prospective and external validation remain important directions for future work.
Machine Learning; Treatment Switching; Administrative Claims Data; Pharmacotherapy; Predictive Analytics; Real-World Evidence; Medication Adherence
Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0
Use-Case-Driven Model Selection and Configuration for Enterprise AI Workflows
Enterprise adoption of generative artificial intelligence (GenAI) has grown rapidly, with 78% of organizations deploying AI in at least one business function as of 2025. Yet studies indicate that 95% if enterprise GenAI pilots fail to deliver measurable business impact. A key contributor to this failure is the ad-hoc, trial-and-error approach that organization use to select and configure large language models (LLMs) for production workflows. Multiple commercial and open-source models are now available, each with different strengths, but enterprise lack a practical methodology for matching models to specific workflow needs and configuring them well. This paper proposes a reference architecture for use-case-driven LLM selection and configuration in enterprise environments. The first layer introduces a Workflow task profiling framework that breaks the workflows into characterized task units along four dimensions: task nature, data profile, output requirements and interaction pattern. The second layer presents a Task-Driven Capability Matching approach that starts from the task profile, identifies the capability categories the task requires, and then identifies candidate models that satisfy those requirements. This task-first direction ensures selection decisions are driven by what the workflow needs, not by model marketing claims or benchmark rankings. The last layer defines a two-dimensional configuration approach where each parameter is driven by the task profile, the selected model, or both. Task-driven parameters such as chunking strategy and corpus structure might remain stable across model changes, while model-specific parameters such as prompt format and message structure change with the model. This enables model transitions while also accounting for relevance of the configurations to the workflow requirements and not just the models. The framework is demonstrated through end-to-end walkthroughs of representative enterprise workflows.
Enterprise Readiness for Generative AI: The Critical Role of Data Engineering
Generative artificial intelligence (GenAI) is accelerating enterprise transformation by enabling natural language interfaces, automated knowledge work, and new modes of decision support. Yet many GenAI initiatives stall after pilot stages—not because models underperform, but because enterprises lack the data engineering maturity required to operationalize these systems at scale. This paper argues that enterprise readiness for GenAI is primarily a data engineering problem: GenAI outcomes depend on the ability to reliably ingest, integrate, govern, and serve high quality data across fragmented environments. Through a literature based conceptual analysis, the study synthesizes the data engineering capabilities most critical to GenAI adoption, including scalable data pipelines, modern storage and processing architectures, integrated enterprise data platforms, and robust governance mechanisms for security, privacy, lineage, and compliance. The paper proposes a conceptual framework that links enterprise readiness dimensions (digital infrastructure, leadership alignment, talent, and governance) to data engineering capability as a mediating layer that enables sustainable GenAI deployment. The findings position data engineering as a strategic prerequisite for trustworthy, cost effective, and scalable GenAI systems, and provide a practical lens for enterprises to assess and strengthen readiness before broad rollout.
Generative Artificial Intelligence; Enterprise Readiness; Data Engineering; Data Infrastructure; Data Pipelines; Artificial Intelligence Adoption; Digital Transformation
Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
Beyond FICO: Enhancing Mortgage Default Forecasting and Inclusive Lending via Multimodal Graph Neural Networks and Urban Mobility Analytics
Mortgage underwriting in the United States still relies heavily on FICO-style bureau scores, debt-to-income ratios, and loan-to-value cutoffs, even though default emerges from a broader system of household liquidity, neighborhood shocks, housing-market conditions, and lender relationships. This paper develops a framework for improving mortgage default forecasting and inclusive lending through multimodal graph neural networks (GNNs) enriched with urban mobility analytics. Drawing on evidence from mortgage finance, credit scoring, housing economics, network science, and mobility research, the study argues that FICO-centered pipelines understate both relational risk and place-based resilience. Borrowers with similar scores can face different default hazards when commuting stability, job accessibility, local price volatility, and origination-channel exposures differ. The paper synthesizes relevant sources and proposes an empirical design combining Freddie Mac loan-level performance data, HMDA disclosures, FHFA house-price indices, American Community Survey neighborhood variables, and Census LEHD commuting-flow measures. A heterogeneous graph architecture connects borrowers, loans, properties, tracts, lenders, and employment-access nodes, while multimodal encoders fuse tabular, spatial, temporal, and network signals. The framework is evaluated against logistic regression, gradient boosting, and non-graph deep learning baselines using discrimination, calibration, fairness, and prudent-inclusion metrics. The core contribution is a governance-aware blueprint for safer and fairer underwriting. By treating bureau scores as one modality rather than the dominant lens, lenders can identify hidden resilience among thin-file applicants and hidden fragility among superficially strong files. The paper concludes that multimodal GNN systems can outperform FICO-dominant underwriting while supporting more inclusive, auditable mortgage credit allocation in the United States.
Mortgage default, inclusive lending, FICO, graph neural networks, urban mobility analytics, credit risk, HMDA, Freddie Mac
Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
Neuro-Symbolic Data Warehousing: Bridging AI Reasoning with Enterprise Analytics
The rapid adoption of artificial intelligence in the enterprise has dramatically changed data-driven decision-making. Nevertheless, traditional data warehousing designs mostly support statistical and descriptive analytics, with no facilities for symbolic reasoning or elicitable intelligence. This weakness makes it difficult to integrate the state-of-the-art AI reasoning with the enterprise analytics infrastructure. Neuro-symbolic artificial intelligence, which seeks to integrate neural learning with symbolic reasoning, is believed to be a promising solution for improving the capabilities of analytical systems through interpretability, logical inference, and knowledge representation. This paper discusses the meaning of neuro-symbolic data warehousing as a new construct that combines neural network learning models with symbolic reasoning processes within an enterprise data warehouse. The research paper explores the relevance of such architectures for improving enterprise analytics by providing explainable insights, enabling semantic querying, and supporting decision-making with knowledge. A conceptual architecture for neuro-symbolic data warehousing is proposed, with a discussion of the interaction among conventional data warehouse constituents, knowledge graphs, and AI reasoning engines. The paper also covers implementation issues, such as data integration, knowledge representation, system scaling, and governance. According to the findings, neuro-symbolic methods have the potential to significantly enhance enterprise analytics, making decision support systems more transparent, interpretable, and context-aware. Having machine learning and symbolic reasoning in data warehousing can help organizations achieve even more powerful analytical processing while preserving transparency and trust in AI-driven decision-making.
Neuro-Symbolic Artificial Intelligence, Enterprise Data Warehousing, Knowledge Graphs, Explainable Artificial Intelligence (XAI), Enterprise Analytics, Semantic Data Integration, Intelligent, Decision Support Systems, Hybrid AI Architectures
Modernizing Legacy Banking Systems: Migration Strategies and Cost Optimization in Financial Enterprises
The banking industry is currently at an inflection point. Legacy platforms, many of which are decades old utilizing COBOL have been driving core banking processes globally. However, many of these legacy platforms are struggling to support the growing demand for innovation and improved customer experience. This paper examines the imperative for modernization within financial organizations, by analyzing why banks cannot wait much longer before they begin to transform the underlying technology that supports their business mode. Using current case studies from the financial services industry and new methodologies, this paper provides an overview of possible migration methods to support the transition, and how to best achieve both the reduction of risk and the maximum potential for innovation. This study suggests that a hybrid approach of strategic re-platforming and incremental modernization provides the most effective way to measure the success of a project using a wide range of metrics. Additionally, cost optimization was identified as one of the key elements in achieving a successful modernization project, and cloud-native architecture was identified as having significant potential to improve long term financial and operational performance. These findings should provide a practical roadmap for financial executives who are trying to navigate the challenging journey from legacy constraint to technological freedom.
Legacy Banking Systems, Cloud Migration, Financial Technology, Modernization Strategies, Cost Optimization, Digital Transformation, AI, FinTech
Enhancing Data Quality and Trust in AI Systems Through Robust Data Engineering
The workability of AI systems in all kinds of industries lies in real-time and almost-real-time analytics in the sphere of healthcare, finance, and smart cities. Nevertheless, these systems have major problems with keeping the data of high quality because of the volatility of data streams in real-time, data islands, and unequal quality criteria of data. AI systems also rely much on data engineering processes in order to assure data integrity and consistency in data pumped into models. But, to maintain the best quality of data it cannot be limited to the technical systems, but also to governance and ethical issues, especially when it comes to instilling trust and transparency in AI models. The paper will discuss the major concerns of data quality, model transparency, and AI governance in terms of real-time systems and near-real-time systems. The research suggests a set of frameworks that combine AI-based data engineering processes, data lineage and automated data quality assurance methods to support data integrity. The paper also addresses why transparency and explainability of AI models are needed to create trust and promote ethical AI systems. Through a review of the most recommended practices in data quality control and AI governance models, this article will present an all-encompassing roadmap towards ensuring the data reliability and reliability of the AI models in the active life cycles. The suggested structures provide solutions that organizations can apply in order to defeat the obstacle of data quality, and also develop more open, ethical, and lawful AI structures.
Real-time analytics; Near-real-time data; Data quality; AI-driven systems; Data engineering; Model transparency; AI governance; Trust in AI; Data lineage; Ethical AI; AI explainability; Data quality assurance; Machine learning models; Automated data processing; AI ethics
AI- and Data Science–Driven Healthcare Revenue Optimization and Payment Integrity Modeling in U.S. Public and Private Insurance Systems
The increasing complexity of healthcare reimbursement systems in the United States has intensified financial inefficiencies, claim denials, fraud exposure, and payment inaccuracies across both public and private insurance markets. This study examines the transformative role of artificial intelligence (AI) in healthcare revenue optimization and payment integrity within Medicare, Medicaid, and commercial insurance systems. By integrating machine learning algorithms, predictive analytics, natural language processing (NLP), and anomaly detection frameworks, AI-enabled systems can proactively identify billing irregularities, reduce improper payments, optimize reimbursement cycles, and enhance regulatory compliance. The research proposes a multi-layered AI framework combining fraud detection models, claims prediction systems, automated coding validation, and risk-adjusted payment analytics to strengthen financial sustainability across healthcare institutions and insurance providers. The study further evaluates the economic impact of AI adoption on revenue cycle management (RCM), cost containment, and administrative burden reduction. Additionally, ethical considerations, data governance challenges, and explainability requirements are examined to ensure transparency and compliance with federal healthcare regulations. Findings suggest that AI-driven payment integrity systems can significantly reduce fraud, waste, and abuse (FWA), improve claim acceptance rates, and enhance long-term fiscal stability in both public and private healthcare insurance programs. The paper concludes by outlining policy recommendations and implementation strategies to support scalable, secure, and equitable AI integration in U.S. healthcare financial ecosystems.
Healthcare Revenue Optimization; Payment Integrity; Fraud Detection; Revenue Cycle Management (RCM); Medicare and Medicaid Analytics; Claims Denial Prevention; Machine Learning in Healthcare Finance; Health Policy Analytics