Frontiers in Computer Science and Artificial Intelligence
type: channel
description: 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.
publisher: Al-Kindi Center for Research and Development
language: en
publication name: Frontiers in Computer Science and Artificial Intelligence
issn: 2978-8048
items: Governance Readiness Beyond Predictive Performance: An Empirical Benchmark for Higher-Education Early Warning Systems
items: Market basket analysis for healthcare services to identify bundled care offerings
items: Cognitive Hives: A Distributed Systems Theory for Conflict-Aware Multimodal Intelligence
items: Artificial Intelligence for Sustainable and Climate-Resilient Apparel Supply Chains: A Narrative Review and Integrative Framework
items: Artificial Intelligence in Project Management: A Systematic Review of Applications, Challenges, and Future Directions
items: AI-Driven Digital Twin Framework for Real-Time Optimization of Smart Manufacturing Systems in Industry 5.0
items: Enhancing Retail Checkout Efficiency Through a Hybrid YOLOv8-Based Grocery Detection and Billing System
items: A Secure Microservices Reference Architecture for Financial Cloud Systems Using Spring Boot and Event-Driven Design
items: A 6G-Enabled AI-Powered Intelligent Healthcare Integrating IoMT and Edge Computing
items: Developing AI-Based Financial Forecasting and Cybersecurity Systems for the U.S. Digital Economy
items: Enterprise Data Migration Strategies for High-Assurance Information Systems
items: From Utility Bills to Retrofit Finance: An AI Framework for Energy-Burden-Aware Underwriting in Residential Decarbonization
items: Machine Learning for Identifying Deepfake-Driven Identity Abuse, Authentication Evasion, and Customer Impersonation in U.S. Banking
items: Machine Learning Models for Predicting Patient Treatment Switching Using Claims Data
items: Use-Case-Driven Model Selection and Configuration for Enterprise AI Workflows
items: Enterprise Readiness for Generative AI: The Critical Role of Data Engineering
items: Beyond FICO: Enhancing Mortgage Default Forecasting and Inclusive Lending via Multimodal Graph Neural Networks and Urban Mobility Analytics
items: Neuro-Symbolic Data Warehousing: Bridging AI Reasoning with Enterprise Analytics
items: Modernizing Legacy Banking Systems: Migration Strategies and Cost Optimization in Financial Enterprises
items: Enhancing Data Quality and Trust in AI Systems Through Robust Data Engineering
items: AI- and Data Science–Driven Healthcare Revenue Optimization and Payment Integrity Modeling in U.S. Public and Private Insurance Systems
items: Governance Gaps AI-Driven Fraud Detection: Machine Learning Strategies for Countering Generative Fraud in the U.S. Financial System
items: Artificial Intelligence in Sentencing: Evaluating Machine Learning Models for Sentencing Recommendations in the U.S.
items: An Explainable Machine Learning Framework for Mortality Risk Prediction of Liver Cirrhosis Patients in the U.S. Healthcare System
items: GitOps and Continuous Delivery in Financial Software: Best Practices for Efficient DevOps Pipelines
items: Designing High-Performance OLAP Cubes for Advanced Analytical Decision-Making
items: Ethical and Trustworthy Autonomous Agents in Network SecOps: Transparency, Auditing, and Human-in-the-Loop Overrides
items: Goal-Driven Autonomous Agents for SLA-Aware Network Orchestration
items: Unified OSS–BSS Convergence: Orchestrating Network Performance and Customer Experience in Telecom
items: Observability for LLM apps: what to log, privacy-safe telemetry, KPIs

Governance Readiness Beyond Predictive Performance: An Empirical Benchmark for Higher-Education Early Warning Systems
type: item
description: 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 institut…
subject: Research Article
  topic: section
subject: Early warning systems; governance readiness; explainability stability; algorithmic fairness; complex survey design; higher education analytics; BPS:20/22; benchmark evaluation
  topic: keywords
creator: Nafiz Imtiaz
creator: Tama Rani Kundu
creator: Ankita Roy
creator: Md Ikram Hossain Bhuiyan
creator: Koushikur Rahman
creator: Md Kamrul Islam
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2025-07-10
publication date: 2025-07-10
starting page: 49
ending page: 65
doi: 10.32996/fcsai.2025.4.5.6X

Market basket analysis for healthcare services to identify bundled care offerings
type: item
description: 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 descr…
subject: Research Article
  topic: section
subject: Market Basket Analysis, Bundled Care Offerings, Healthcare Data Mining, SPARCS Inpatient Dataset, Association Rule Mining and Hospital Service Utilization
  topic: keywords
creator: Rakib Hassan Rimon
creator: Nurujjaman
creator: Md Manarat Uddin Mithun
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2025-04-25
publication date: 2025-04-25
starting page: 44
ending page: 67
doi: 10.32996/fcsai.2025.4.3.5

Cognitive Hives: A Distributed Systems Theory for Conflict-Aware Multimodal Intelligence
type: item
description: 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.
subject: Research Article
  topic: section
subject: Distributed AI, Multimodal Reasoning, Conflict Resolution, Temporal Synchronization, Cognitive Architecture, Hybrid Temporal Tokenization, Distributed Systems Theory, Arbitration Models
  topic: keywords
creator: Inesh Hettiarachchi
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2026-04-14
publication date: 2026-04-14
starting page: 56
ending page: 72
doi: 10.32996/jcsts.2026.5.5.7

Artificial Intelligence for Sustainable and Climate-Resilient Apparel Supply Chains: A Narrative Review and Integrative Framework
type: item
description: 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.
subject: Research Article
  topic: section
subject: Artificial Intelligence (AI); Apparel Supply Chains; Sustainable Supply Chain Management (SSCM); Climate Resilience; Circular Economy; Supply Chain Resilience; Environmental Sustainability
  topic: keywords
creator: Apurbaa Sarker
creator: Mahmud Kamal Anamul Haque
creator: Zannatul Mouwa
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2022-04-25
publication date: 2022-04-25
starting page: 79
ending page: 92
doi: 10.32996/fcsai.2022.1.1.9x

Artificial Intelligence in Project Management: A Systematic Review of Applications, Challenges, and Future Directions
type: item
description: 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.
subject: Research Article
  topic: section
subject: Artificial intelligence, project management, machine learning, systematic review, bibliometric analysis, construction management
  topic: keywords
creator: Afsana Munni
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0
date: 2022-01-20
publication date: 2022-01-20
starting page: 68
ending page: 78
doi: 10.32996/fcsai.2022.1.1.8x

AI-Driven Digital Twin Framework for Real-Time Optimization of Smart Manufacturing Systems in Industry 5.0
type: item
description: 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.
subject: Research Article
  topic: section
subject: AI-driven Digital Twin,
  topic: keywords
subject: AI-driven Digital Twin, Smart Manufacturing
  topic: keywords
subject: Industry 5.0,
  topic: keywords
subject: Predictive Analytics
  topic: keywords
subject: Human-AI Collaboration,
  topic: keywords
subject: Real-Time Optimization, Human-AI Collaboration, Predictive Analytics
  topic: keywords
subject: Real-Time Optimization
  topic: keywords
creator: Venkata Naga Kishore Thota
rights: Copyright (c) 2026 Frontiers in Computer Science and Artificial Intelligence
date: 2026-04-15
publication date: 2026-04-15
starting page: 01
ending page: 04
doi: 10.32996/jcsts.2026.5.6.1

Enhancing Retail Checkout Efficiency Through a Hybrid YOLOv8-Based Grocery Detection and Billing System
type: item
description: 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.
subject: Research Article
  topic: section
subject: Automated billing system, grocery item detection, object detection, YOLO, computer vision, deep learning, retail automation
  topic: keywords
creator: Safaina Khan Oishi
creator: Md. Minhazul Islam
creator: Shakhar Das Rony
creator: Rabbi Sadnan Khan
creator: Md. Moshiur Rahman
creator: Mustak Ahmmed
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2026-04-06
publication date: 2026-04-06
starting page: 46
ending page: 55
doi: 10.32996/jcsts.2026.5.5.6

A Secure Microservices Reference Architecture for Financial Cloud Systems Using Spring Boot and Event-Driven Design
type: item
description: 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.
subject: Research Article
  topic: section
subject: Microservices Architecture; Financial Cloud Systems; Event-Driven Architecture; Spring Boot; Data Security
  topic: keywords
creator: Garima Agarwal
rights: Copyright (c) 2026 Frontiers in Computer Science and Artificial Intelligence
date: 2026-04-03
publication date: 2026-04-03
starting page: 39
ending page: 45
doi: 10.32996/jcsts.2026.5.5.5

A 6G-Enabled AI-Powered Intelligent Healthcare Integrating IoMT and Edge Computing
type: item
description: 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.
subject: Research Article
  topic: section
subject: Artificial Intelligence (AI), Internet of Medical Things (IoMT), 6G, URLLC, 6G-HIS, Healthcare, Wireless communication, Robotic
  topic: keywords
creator: Sanjida Ahamed Anonna
creator: Yunisha Basnet
creator: Md Mehedi Hasan
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2026-04-02
publication date: 2026-04-02
starting page: 17
ending page: 29
doi: 10.32996/jcsts.2026.5.5.3

Developing AI-Based Financial Forecasting and Cybersecurity Systems for the U.S. Digital Economy
type: item
description: 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.
subject: Research Article
  topic: section
subject: Financial Forecasting
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: LSTM
  topic: keywords
subject: Prophet
  topic: keywords
subject: Random Forest
  topic: keywords
subject: Intrusion Detection
  topic: keywords
subject: CICIDS2017
  topic: keywords
subject: Risk Intelligence
  topic: keywords
subject: Digital Economy
  topic: keywords
creator: Mahfuz Islam Khan Jabed
creator: Md Riad Mahamud Sirazy
creator: Sourav Mandal
creator: Sharmin Akter Akter
creator: Ali Hassan
creator: Hammed Esa
rights: Copyright (c) 2026 Frontiers in Computer Science and Artificial Intelligence
date: 2026-04-03
publication date: 2026-04-03
starting page: 30
ending page: 38
doi: 10.32996/jcsts.2026.5.5.4

Enterprise Data Migration Strategies for High-Assurance Information Systems
type: item
description: 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.
subject: Research Article
  topic: section
subject: Prise Data Migration, High-Assurance Systems, Artificial Intelligence, Data Integrity, Digital Transformation
  topic: keywords
creator: Vasudevan Ananthakrishnan
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2026-03-27
publication date: 2026-03-27
starting page: 10
ending page: 16
doi: 10.32996/jcsts.2026.5.5.2

From Utility Bills to Retrofit Finance: An AI Framework for Energy-Burden-Aware Underwriting in Residential Decarbonization
type: item
description: 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 d…
subject: Research Article
  topic: section
subject: Fintech; artificial intelligence; residential decarbonization; retrofit finance; energy burden; alternative data; algorithmic fairness; explainable AI; underwriting; climate finance
  topic: keywords
creator: Shah Farhan Rabbani
creator: Yusuf Oli Rahat
creator: Md Kamrul Islam
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0
date: 2024-12-25
publication date: 2024-12-25
starting page: 72
ending page: 88
doi: 10.32996/fcsai.2024.3.2.10

Machine Learning for Identifying Deepfake-Driven Identity Abuse, Authentication Evasion, and Customer Impersonation in U.S. Banking
type: item
description: 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, resilie…
subject: Research Article
  topic: section
subject: deepfakes; banking fraud; identity proofing; account takeover; customer impersonation; voice cloning; face anti-spoofing; graph neural networks; explainable AI
  topic: keywords
creator: Yusuf Oli Rahat
creator: Md Kamrul Islam
creator: Shah Farhan Rabbani
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2026-02-13
publication date: 2026-02-13
starting page: 54
ending page: 67
doi: 10.32996/fcsai.2026.4.3.7x

Machine Learning Models for Predicting Patient Treatment Switching Using Claims Data
type: item
description: 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.
subject: Research Article
  topic: section
subject: Machine Learning; Treatment Switching; Administrative Claims Data; Pharmacotherapy; Predictive Analytics; Real-World Evidence; Medication Adherence
  topic: keywords
creator: YUVACHANDRA MARASANI
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0
date: 2023-06-28
publication date: 2023-06-28
starting page: 59
ending page: 66
doi: 10.32996/fcsai.2023.2.1.6

Use-Case-Driven Model Selection and Configuration for Enterprise AI Workflows
type: item
description: 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.
subject: Research Article
  topic: section
subject: Large Language Models
  topic: keywords
subject: Model selection
  topic: keywords
subject: Agentic AI Workflows
  topic: keywords
subject: GenAI
  topic: keywords
subject: Use-Case-Driven
  topic: keywords
subject: Enterprise AI
  topic: keywords
subject: Effective Model Selection
  topic: keywords
subject: Task-Driven Capabilities
  topic: keywords
subject: AI Workflow Optimization
  topic: keywords
creator: Sathiesh Veera
rights: Copyright (c) 2026 Frontiers in Computer Science and Artificial Intelligence
date: 2026-03-25
publication date: 2026-03-25
starting page: 01
ending page: 09
doi: 10.32996/jcsts.2026.5.5.1

Enterprise Readiness for Generative AI: The Critical Role of Data Engineering
type: item
description: 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.
subject: Research Article
  topic: section
subject: Generative Artificial Intelligence; Enterprise Readiness; Data Engineering; Data Infrastructure; Data Pipelines; Artificial Intelligence Adoption; Digital Transformation
  topic: keywords
creator: YUVACHANDRA MARASANI
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2024-11-25
publication date: 2024-11-25
starting page: 59
ending page: 71
doi: 10.32996/fcsai.2024.3.2.9

Beyond FICO: Enhancing Mortgage Default Forecasting and Inclusive Lending via Multimodal Graph Neural Networks and Urban Mobility Analytics
type: item
description: 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.
subject: Research Article
  topic: section
subject: Mortgage default, inclusive lending, FICO, graph neural networks, urban mobility analytics, credit risk, HMDA, Freddie Mac
  topic: keywords
creator: Imran Hossain Rasel
creator: Md Ibrahim
creator: Anika Anjum Pritty
creator: A S M FAHIM
creator: Nusrat Jahan
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2023-12-19
publication date: 2023-12-19
starting page: 62
ending page: 81
doi: 10.32996/fcsai.2023.2.2.5

Neuro-Symbolic Data Warehousing: Bridging AI Reasoning with Enterprise Analytics
type: item
description: 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.
subject: Research Article
  topic: section
subject:
  topic: keywords
  value: 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
creator: Akash Vijayrao Chaudhari
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2022-05-28
publication date: 2022-05-28
starting page: 53
ending page: 67
doi: 10.32996/fcsai.2022.1.1.7x

Modernizing Legacy Banking Systems: Migration Strategies and Cost Optimization in Financial Enterprises
type: item
description: 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.
subject: Research Article
  topic: section
subject: Legacy Banking Systems, Cloud Migration, Financial Technology, Modernization Strategies, Cost Optimization, Digital Transformation, AI, FinTech
  topic: keywords
creator: Ashmitha Nagraj
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0
date: 2022-05-28
publication date: 2022-05-28
starting page: 43
ending page: 52
doi: 10.32996/fcsai.2022.1.1.6x

Enhancing Data Quality and Trust in AI Systems Through Robust Data Engineering
type: item
description: 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.
subject: Research Article
  topic: section
subject:
  topic: keywords
  value: 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
creator: JAGADEESWAR ALAMPALLY
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2024-04-25
publication date: 2024-04-25
starting page: 120
ending page: 130
doi: 10.32996/fcsai.2024.3.1.12

AI- and Data Science–Driven Healthcare Revenue Optimization and Payment Integrity Modeling in U.S. Public and Private Insurance Systems
type: item
description: 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.
subject: Research Article
  topic: section
subject:
  topic: keywords
  value: 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
creator: Afm Tanvir Anjum
creator: Sirapa Malla
creator: Md Refadul Hoque
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2026-03-09
publication date: 2026-03-09
starting page: 44
ending page: 53
doi: 10.32996/fcsai.2026.4.3.6x

Governance Gaps AI-Driven Fraud Detection: Machine Learning Strategies for Countering Generative Fraud in the U.S. Financial System
type: item
description: With the fast development of the new generation of artificial intelligence (AI) technologies, fraud in the sphere of the financial industry has taken a new form and is a challenge to the detection systems and regulatory regimes. This paper explores how AI-guided fraud detection has weaknesses in governance and assesses machine learning to counter generative fraud in the U.S. banking sector. Since the generation models are capable of creating advanced synthetic identities, deepfake transactions and adaptive fraud patterns, the traditional rule-based monitoring systems and pre-existing oversight frameworks become more limited. Based on the publicly available Credit Card Fraud Detection dataset, this study creates and compares various machine learning models, such as the Logistic Regression, the Random Forest, the XGBoost, and the Neural Networks, in extreme class-imbalanced conditions. The synthetic pattern of fraud and adversarial perturbations are used to measure model robustness and resilience to match any emerging pattern of generative fraud risks. Given imbalance between fraudulent transactions, precision-recall measures, F1-score and Area under the Precision-Recall Curve (AUPRC) are used to perform the evaluation of performance. In addition to technical assessment, the paper analyzes the issues of transparency, explainability, and model accountability that are related to black-box AI systems. The results demonstrate a performance-governance tradeoff: whereas highly developed ensemble and deep learning models have high predictive accuracy, they cause interpretability and regulatory compliance issues. The article reveals key gaps in the governance of the model risk management, auditability, and regulatory compatibility in the financial oversight framework in the U.S. It suggests a unified system of explainable AI, adversarial robustness testing, and more stringent regulatory rules to increase institutional stability to generative fraud. The study adds to the cross…
subject: Research Article
  topic: section
subject: AI-Driven Fraud Detection, Generative Fraud, Machine Learning, Financial Governance, Explainable Artificial Intelligence (XAI) and Model Risk Management
  topic: keywords
creator: Md Manarat Uddin Mithun
creator: Nurujjaman
creator: Rahanuma Tarannum
creator: Rakib Hassan Rimon
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2024-01-25
publication date: 2024-01-25
starting page: 97
ending page: 119
doi: 10.32996/fcsai.2024.3.1.11

Artificial Intelligence in Sentencing: Evaluating Machine Learning Models for Sentencing Recommendations in the U.S.
type: item
description: Artificial intelligence is increasingly deployed in high-stakes decision-making, raising critical questions about accuracy, fairness, and transparency in regulated domains. This study evaluates the use of machine learning models to generate sentencing recommendations within the U.S. criminal justice system, examining whether such models can reliably support judicial decision-making without amplifying existing inequities. Using a comprehensive dataset of sentencing records enriched with engineered features reflecting criminal history, offense severity, demographics, and jurisdictional context, we develop and compare a range of predictive models, including Logistic Regression, tree‑based ensembles (Random Forest, XGBoost, LightGBM), deep learning architectures (MLP, LSTM, Bi‑LSTM), and hybrid ensemble frameworks. Models are assessed on both continuous sentence length prediction and classification of above‑median sentencing, using metrics such as mean absolute error, R‑squared, AUC‑ROC, and F1‑score. Fairness metrics are computed across gender, age, and jurisdictional groups, and interpretability analyses employ feature importance, attention weights, and SHAP values to ensure transparency in decision logic. Results indicate that hybrid and stacked ensembles achieve the best balance of accuracy and fairness improvements over baselines, with interpretability tools confirming alignment with legal reasoning and risk factors. These findings suggest that responsibly governed AI systems can augment sentencing decisions as decision‑support tools, provided continuous bias monitoring and ethical oversight are integrated into deployment practices. The study contributes empirical evidence and methodological guidance for integrating machine learning into judicial contexts.
subject: Research Article
  topic: section
subject: Artificial intelligence
  topic: keywords
subject: machine learning
  topic: keywords
subject: sentencing recommendations
  topic: keywords
subject: criminal justice
  topic: keywords
subject: fairness
  topic: keywords
subject: interpretability
  topic: keywords
creator: Mohammed Nazmul Islam Miah
creator: Md Joshim Uddin
creator: Manohar Kakumani
rights: Copyright (c) 2026 Frontiers in Computer Science and Artificial Intelligence
date: 2026-02-22
publication date: 2026-02-22
starting page: 30
ending page: 43
doi: 10.32996/fcsai.2025.4.3.5x

An Explainable Machine Learning Framework for Mortality Risk Prediction of Liver Cirrhosis Patients in the U.S. Healthcare System
type: item
description: Liver cirrhosis represents a significant source of morbidity and mortality within the United States healthcare system, placing a substantial burden on the U.S. biomedical and clinical care sector and increasing demand for reliable, system-relevant risk assessment tools. Although previous machine learning–based studies have demonstrated promising predictive performance, their limited interpretability, black-box decision-making, and insufficient alignment with U.S. clinical workflows have restricted widespread adoption. To address these challenges, this study presents an explainable and clinically interpretable machine learning framework for mortality risk prediction of liver cirrhosis patients within the U.S. healthcare system using routinely collected clinical and treatment-related data. A publicly available U.S. cirrhosis dataset was analyzed, and a Random Forest classifier was developed and rigorously evaluated using accuracy, precision, recall, F1-score, and confusion matrix analysis, with particular emphasis on minimizing false negative predictions to enhance patient safety. To overcome the transparency limitations of earlier approaches, SHapley Additive exPlanations (SHAP) were integrated in probability space to provide both global and patient-level interpretability. Experimental results demonstrate strong predictive performance while consistently identifying clinically meaningful risk factors, including age, ascites, edema, hepatomegaly, spider angiomas, and treatment type, reinforcing the clinical reliability of the proposed framework within U.S. healthcare environments.
subject: Research Article
  topic: section
subject: Liver Cirrhosis, Explainable Artificial Intelligence, Machine Learning, Mortality Risk Prediction, SHAP Explainability, Clinical Decision Support, U.S. Healthcare System
  topic: keywords
creator: Ekramul Hasan
creator: Nurtaz Begum Asha
creator: Mustafizur Rahaman
creator: Mostafizur Rahman Shakil
creator: Md Musa Haque
creator: Dipta Paul
creator: Md Al Amin
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2026-02-22
publication date: 2026-02-22
starting page: 15
ending page: 26
doi: 10.32996/fcsai.2025.4.3.4x

GitOps and Continuous Delivery in Financial Software: Best Practices for Efficient DevOps Pipelines
type: item
description: The combination of GitOps and Continuous Delivery (CD), utilizing DevOps pipelines to provide an effective model for the development of financial software applications that emphasize efficiency, security and regulatory compliance in high-risk and regulated environments is demonstrated in this paper. The empirical research provides a comprehensive review of previous literature to identify best practices for both methodologies in financial services, including Banking, Insurance and Fintech. Declarative configuration through GitOps has been emphasized to improve auditability and resilience while streamlining deployment using CD. In addition, the empirical research and theoretical frameworks have identified how security can be integrated into DevSecOps as well as legacy modernization and organizational strategies. Case studies from large financial institutions are used to demonstrate practical implementation examples of DevSecOps in Fintech to address the specific issues of data privacy and rapid innovation. Compliance and system integration challenges are also reviewed, along with several clear recommendations for the efficient design of pipelines.
subject: Research Article
  topic: section
subject:
  topic: keywords
  value: GitOps; Continuous Delivery; DevSecOps; Financial Software Engineering; FinTech; CI/CD Pipelines; Cloud-Native Architecture; Regulatory Compliance; Microservices Modernization; Secure Software Supply Chain
creator: Ashmitha Nagraj
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2022-05-25
publication date: 2022-05-25
starting page: 37
ending page: 42
doi: 10.32996/fcsai.2022.1.1.5x

Designing High-Performance OLAP Cubes for Advanced Analytical Decision-Making
type: item
description: The growing size of enterprise data requires analytical systems that can facilitate effective decision-making. Online Analytical Processing (OLAP) cubes have the advantage of allowing multidimensional analysis but have challenges in scalability and latency with handling heterogeneous data. The framework to be proposed in this paper is based on developing high-performance OLAP cubes, combining dimensional modeling optimization, scalable structuring, and contemporary storage approaches. However, the strategy enhances the responsiveness and decision capability of analytics by matching cube architecture and performance evaluation metrics. The article offers insights into the balance between scalability, usability and computational efficiency in the modern analytics setting.
subject: Research Article
  topic: section
subject: OLAP Cubes, Data Warehousing, Decision Support Systems, High-performance Analytics, Multidimensional Modeling, Big Data Processing
  topic: keywords
creator: JAGADEESWAR ALAMPALLY
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2022-06-25
publication date: 2022-06-25
starting page: 31
ending page: 36
doi: 10.32996/fcsai.2022.1.1.4x

Ethical and Trustworthy Autonomous Agents in Network SecOps: Transparency, Auditing, and Human-in-the-Loop Overrides
type: item
description: This paper introduces EthosSecOps, a comprehensive framework designed to enhance transparency, auditability, and ethical alignment in AI-driven intrusion detection and automated response systems. EthosSecOps integrates an Explainability Layer for generating feature-attribution explanations, a Blockchain-backed Audit Store to immutably record alerts, actions, and overrides, and a Policy-Driven Override Engine that empowers human analysts to pause, modify, or abort agent actions. Implemented within a hybrid-cloud telecom environment, EthosSecOps demonstrated 95% attack mitigation accuracy, delivered real-time explanations within 10 milliseconds, and enabled immediate human intervention without disrupting service. The paper details the system's architecture, provides a Python-based audit-logging example, presents empirical evaluation results, and discusses ethical implications for trustworthy autonomous SecOps in regulated and high-availability network operations.
subject: Research Article
  topic: section
subject: Autonomous Agents, Network Orchestration, SLA Compliance, Reinforcement Learning, QoS Monitoring, Software-Defined Networking, Multi-Cloud, SecOps, Cybersecurity
  topic: keywords
creator: Amar Gurajapu
creator: Swapna Anumolu
creator: Vardhan Garimella
creator: Venkata Manikanta Sai Ramakrishna Chundi
creator: Venkata Sita Anand Prakash Gubbala
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2025-02-19
publication date: 2025-02-19
starting page: 63
ending page: 66
doi: 10.32996/jcsts.2025.4.2.7

Goal-Driven Autonomous Agents for SLA-Aware Network Orchestration
type: item
description: Achieving rigorous latency SLAs in dynamic telecommunications environments necessitates continuous optimization of network topology and capacity. This paper presents NetAgent-SLA, a goal-driven autonomous agent framework designed to monitor real-time topological and QoS metrics, implement reinforcement learning policies, and initiate SDN and cloud-API reconfigurations to maintain SLA performance. NetAgent-SLA was deployed on a multi-cloud testbed comprising on-premises SDN, Azure, and AWS platforms, demonstrating the following results: Maintained 99.5% SLA compliance across request rate fluctuations ranging from 500 to 2,000 per second Achieved a 28% reduction in 95th percentile latency compared to static orchestration approaches Adapted to new optimal configurations within 30 seconds following topology changes Incurred less than 1.2 ms decision-loop overhead per cycle This work provides a comprehensive explanation of system architecture, agent design, training methodology, performance evaluation, and addresses integration challenges as well as future directions for autonomous network operations.
subject: Research Article
  topic: section
subject: Autonomous Agents, Network Orchestration, SLA Compliance, Reinforcement Learning, QoS Monitoring, Software-Defined Networking, Multi-Cloud
  topic: keywords
creator: Amar Gurajapu
creator: Swapna Anumolu
creator: Vardhan Garimella
creator: Venkata Manikanta Sai Ramakrishna Chundi
creator: Venkata Sita Anand Prakash Gubbala
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2025-01-20
publication date: 2025-01-20
starting page: 78
ending page: 83
doi: 10.32996/jcsts.2025.4.1.6

Unified OSS–BSS Convergence: Orchestrating Network Performance and Customer Experience in Telecom
type: item
description: The traditional separation of Operations Support Systems (OSS) and Business Support Systems (BSS) in telecom has led to siloed workflows, delayed service activation, and disjointed fault resolution, all of which degrade both network performance and customer experience. This research presents a comprehensive framework, ConvergedON, for unifying OSS and BSS functions to streamline the end-to-end customer-to-network lifecycle. We detail the system design incorporating real-time telemetry ingestion, order processing, policy-driven orchestration, closed-loop analytics, and feedback into a single converged platform. A pilot exercise demonstrated: 55 % reduction in mean service activation time (from 24 hours to 11 hours) 48 % faster fault-detection-to-customer-notification cycle (from 30 min to 15 min) 22 % improvement in Net Promoter Score (NPS) post-deployment 35 % lower manual intervention in order provisioning and incident handling We explore architectural considerations, data flows, implementation methodology, quantitative findings, and discuss challenges and best practices for large-scale OSS–BSS convergence.
subject: Research Article
  topic: section
subject: OSS–BSS Convergence, Customer-to-Network Lifecycle, Network Performance Management, Customer Experience Management, Automation, Real–Time Analytics, Service Orchestration
  topic: keywords
creator: Amar Gurajapu
creator: Swapna Anumolu
creator: Vardhan Garimella
creator: Venkata Manikanta Sai Ramakrishna Chundi
creator: Venkata Sita Anand Prakash Gubbala
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2025-06-19
publication date: 2025-06-19
starting page: 44
ending page: 48
doi: 10.32996/fcsai.2025.4.1.4X

Observability for LLM apps: what to log, privacy-safe telemetry, KPIs
type: item
description: Large Language Model (LLM) applications increasingly form an integral part of enterprise software architecture, enabling conversational interfaces, intelligent assistant applications, and autonomous decision-support systems. While these applications provide tremendous flexibility and capability, their probabilistic nature, prompt dependency, and complex orchestration pipelines create new challenges for monitoring and reliability engineering. The traditional approach to observability, relying on logs, metrics, and traces, is found to be inadequate to measure semantic correctness, behavioral consistency, and governance risks associated with LLM applications. This study explores the concept of observability in large language model (LLM) applications from three different viewpoints: auditable data selection, privacy-preserving telemetry construction, and meaningful operational key performance indicator (KPI) definition. Following the best practices of software observability and MLOps, the study proposes a conceptual framework for model-agnostic observability in LLMs that covers the interaction layer, execution layer, performance layer, and safety layer. In particular, the study focuses on the application of privacy by design, including metadata-centric logging, selective redaction, and controlled access to telemetry data. Furthermore, this paper introduces a well-defined set of operational key performance indicators (KPIs) specific to large language model (LLM) applications, including reliability, performance efficiency, measures of output quality, and safety compliance. The above-mentioned parts of the framework enable the development of a well-structured framework for detecting faults, managing costs, as well as ensuring the reliability of LLMs. The above-mentioned framework makes it easier to implement LLMs at the enterprise level.
subject: Research Article
  topic: section
subject: Large Language Models (LLMs), Observability, Privacy-Safe Telemetry, Operational Key Performance Indicators (KPIs), AI System Governance
  topic: keywords
creator: Prasad Maderamitla
creator: Subba Rao Katragadda
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-14
publication date: 2026-02-14
starting page: 10
ending page: 14
doi: 10.32996/jcsts.2026.5.4.2