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
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
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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
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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
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Governance Gaps AI-Driven Fraud Detection: Machine Learning Strategies for Countering Generative Fraud in the U.S. Financial System
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 crossroads of financial technology, AI governance, and the policy of cyber security by offering both empirical and policy-based suggestions.
AI-Driven Fraud Detection, Generative Fraud, Machine Learning, Financial Governance, Explainable Artificial Intelligence (XAI) and Model Risk Management
Artificial Intelligence in Sentencing: Evaluating Machine Learning Models for Sentencing Recommendations in the U.S.
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.
Copyright (c) 2026 Frontiers in Computer Science and Artificial Intelligence
An Explainable Machine Learning Framework for Mortality Risk Prediction of Liver Cirrhosis Patients in the U.S. Healthcare System
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.
Liver Cirrhosis, Explainable Artificial Intelligence, Machine Learning, Mortality Risk Prediction, SHAP Explainability, Clinical Decision Support, U.S. Healthcare System
GitOps and Continuous Delivery in Financial Software: Best Practices for Efficient DevOps Pipelines
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.
GitOps; Continuous Delivery; DevSecOps; Financial Software Engineering; FinTech; CI/CD Pipelines; Cloud-Native Architecture; Regulatory Compliance; Microservices Modernization; Secure Software Supply Chain
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Designing High-Performance OLAP Cubes for Advanced Analytical Decision-Making
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.
OLAP Cubes, Data Warehousing, Decision Support Systems, High-performance Analytics, Multidimensional Modeling, Big Data Processing
Ethical and Trustworthy Autonomous Agents in Network SecOps: Transparency, Auditing, and Human-in-the-Loop Overrides
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.
Autonomous Agents, Network Orchestration, SLA Compliance, Reinforcement Learning, QoS Monitoring, Software-Defined Networking, Multi-Cloud, SecOps, Cybersecurity
Venkata Manikanta Sai Ramakrishna Chundi
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Goal-Driven Autonomous Agents for SLA-Aware Network Orchestration
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.
Autonomous Agents, Network Orchestration, SLA Compliance, Reinforcement Learning, QoS Monitoring, Software-Defined Networking, Multi-Cloud
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Unified OSS–BSS Convergence: Orchestrating Network Performance and Customer Experience in Telecom
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.
OSS–BSS Convergence, Customer-to-Network Lifecycle, Network Performance Management, Customer Experience Management, Automation, Real–Time Analytics, Service Orchestration
Observability for LLM apps: what to log, privacy-safe telemetry, KPIs
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.
Large Language Models (LLMs), Observability, Privacy-Safe Telemetry, Operational Key Performance Indicators (KPIs), AI System Governance
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Predictive Financial Technologies for Strengthening Liquidity and Cash-Flow Management in U.S. Small Enterprises
The fast-growing innovation in financial technologies has altered the way small businesses are run in their financial affairs, in such aspects as liquidity and cash-flow sustainability. This research paper discusses how predictive financial technologies enhance the strength of liquidity and cash-flow management in small businesses in the United States. Artificial intelligence powered predictive tools and machine learning as well as advanced data analytics allow businesses to predict streams of revenue, financial risks, working capital optimization, and data-driven financial decisions. The article discusses the main fintech solutions, including automated cash-flow forecasting systems, intelligent accounting platforms, and digital lending technologies, which can help planning finances proactively. The qualitative research method was taken by reading the previous literature and industry reports to assess the effect of predictive technologies on financial resilience among small businesses. The results show that companies that use predictive financial tools are characterized by better financial visibility, less uncertainty in cash working capital, greater access to credit, and better operational sustainability. Despite these advantages, technological adoption barriers, cybersecurity issues, expensive implementation, and financial illiteracy are still major limitations to several small businesses. The paper concludes by stating that predictive financial technologies have been critical success factors of financial stability and competitive advantage of small businesses. It suggests greater financial infrastructure investment and specially developed training and enabling regulatory measures to promote further implementation. The combination of predictive fintech solutions can help small businesses to endure the changes of the economy and establish themselves from a long-term perspective.
Predictive financial technology, liquidity management, cash-flow forecasting, financial technology (FinTech), small enterprises, artificial intelligence, machine learning, financial resilience, digital finance.
A Scalable Machine Learning Strategy for Chronic Kidney Disease Screening Across U.S. Healthcare Systems
Chronic Kidney Disease (CKD) is a progressive and frequently underdiagnosed condition that poses a significant public health burden within the U.S. healthcare system. Late-stage detection often results in increased mortality and higher treatment costs, highlighting the need for effective early screening solutions. This study investigates the application of machine learning techniques for early-stage CKD prediction and classification using a reliable clinical dataset obtained from the UCI Machine Learning Repository. Four supervised learning algorithms Support Vector Machine (SVM), AdaBoost (AB), Linear Discriminant Analysis (LDA), and Gradient Boosting (GB) are implemented and evaluated to identify the most accurate predictive model. Model performance is assessed using multiple evaluation metrics, including accuracy, precision, recall, and F1-score. Experimental results demonstrate that the Gradient Boosting classifier achieves the highest predictive accuracy of 99.80%, outperforming the other approaches. The findings suggest that optimized machine learning models can support early CKD detection and enhance clinical decision-making in U.S. healthcare settings.
Chronic Kidney Disease (CKD), Machine Learning, Early Disease Detection, Gradient Boosting, Predictive Modeling, Clinical Decision Support, U.S. Healthcare System
Artificial Intelligence–Enabled E-Commerce Systems and Automated Warehousing: Economic Effects from Amazon FBA in the U.S. Market
The rapid integration of artificial intelligence (AI) into e-commerce platforms has fundamentally transformed digital trade, supply chain management, and business scalability in the United States. This study examines the economic implications of AI-enabled e-commerce platforms and automated warehousing systems, using Amazon Fulfillment by Amazon (FBA) as a representative case to assess impacts on productivity, small business participation, labor dynamics, and overall economic performance. The analysis explores how AI-driven capabilities such as demand forecasting, dynamic pricing, robotic fulfillment, automated inventory management, and algorithmic advertising enhance operational efficiency, reduce transaction costs, and improve market responsiveness. The findings indicate that AI adoption within the Amazon FBA ecosystem lowers entry barriers for small and medium-sized enterprises, strengthens supply chain resilience, and contributes to higher levels of productivity across the U.S. economy. At the macroeconomic level, AI-enabled e-commerce and warehouse automation support GDP growth, regional economic development, and tax revenue expansion, while reshaping employment patterns through job transformation rather than widespread displacement. Despite these benefits, the study highlights critical challenges related to market concentration, data governance, workforce reskilling, and automation governance. Overall, the paper argues that AI-enabled e-commerce platforms and automated fulfillment infrastructures function as economic multipliers, reinforcing U.S. competitiveness in the digital economy when accompanied by balanced regulatory and policy frameworks.
E-Commerce Platforms, Amazon FBA, U.S. Economy; Small Business Growth, Supply Chain Automation, Digital Transformation, Smart Warehousing
Are Arabic YouTube Videos Narrated by Artificial Intelligence Suitable for Training Foreign Students in Listening Skills?
This study aims to find out if AI-narrated Arabic YouTube Videos can be used for listening practice by students learning Arabic as a foreign language (AFL). A sample of Arabic videos narrated by Artificial Intelligence (AI) were collected from YouTube and anlyzed to find out which Arabic words are mispronounced by AI narrators. Whether the videos are translated from English or originally produced in Arabic and whether they are stories, medical or commercial videos, it was noted that AI narrators speak with a natural voice, good expression and intonation. They make no grammatical or syntactic errors. But they make pronunciation errors, especially in diacritics and homographs (words that are spelled the same but have different pronunciations and meanings depending on the short vowel diacritics which are not usually shown on written words). This means that AI has difficulty matching the pronunciation of a homophone with the context of the text in which it occurs. They confuse vowels on تsuffix /ta/, /ti/ /tu/ when it refers to first, second, and third person, changing masculine to feminine, singular to plural and vice versa (كَتَبَ كُتِب كُتب كَتّبَ كُتُب كتبتَ كتبتُ كتبتِ كُتِبَتْ). It also confuses first This affects comprehension in L2 learners and causes cacophony and distortion for Arabic native speakers and non-native speakers of Arabic. The presentation will explain how AI reads Arabic aloud, give reasons for AI pronunciation errors in Arabic videos, suggestions for improvement and recommendation for students learning Arabic as a foreign language.
AI narrated Arabic videos, AI-generated stories, pronunciation errors, Arabic diacritics, homophones, text-to-speech, language processing, AI narrators
Performance Optimization Techniques for High-Frequency Trading and Financial Platforms
High-frequency trading (HFT) has revolutionized financial markets by enabling rapid trade execution and high transaction volumes. This transformation is driven by the need to respond quickly to market fluctuations and execute trades within microseconds. However, the performance of HFT systems is heavily influenced by computational efficiency, network latency, and algorithmic optimization. Ensuring optimal performance minimizes delays, maximizes throughput, and maintains system reliability. This research paper explores the primary performance bottlenecks that obstruct HFT efficiency and explores various optimization strategies. Key focus areas include hardware acceleration techniques such as FPGA and GPU integration, software enhancements for improved code execution speed, and network solutions like direct market access and colocation. The paper also investigates machine learning applications that can predict market movements and refine trading strategies. The paper looks at a lot of existing research and points out the significant technological steps and the hurdles that still need to be tackled to improve high-frequency trading systems. The impact of these improvements is substantial, managing greater efficiency in financial trading platforms and fostering market stability. This study offers valuable information for banks, investors, and tech companies looking to improve their trading results and stay ahead in today’s fast-paced markets.
High-Frequency Trading, HFT, Performance Optimization
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A Real-World Deployment of an Enterprise Conversational AI Platform for Demand Generation and Lead Generation Using Guided Workflows with a Rasa-Based Chatbot
Enterprise organizations increasingly employ conversational AI platforms to support marketing and sales operations, particularly in demand generation and lead qualification workflows. While research on conversational systems has advanced rapidly [1], [2], comparatively little work has documented how such systems are architected and evaluated in real-world enterprise deployments that directly influence revenue-generating business processes. This paper presents a comprehensive technical and operational analysis of an enterprise conversational AI platform built on the Rasa framework and deployed to support demand generation and lead generation through guided conversational workflows. The system is designed to capture user interest in products, answer product-related questions, qualify prospects, and schedule sales demonstrations. Rather than reporting proprietary performance figures, the paper defines a rigorous metrics framework for evaluating business impact, including engagement effectiveness, lead quality indicators, conversion funnel movement, and return-on-investment (ROI) considerations grounded in established marketing analytics and MLOps practices [4], [7]. The paper focuses on system architecture, workflow orchestration, measurement methodology, and operational challenges encountered in production. The goal is to provide a reproducible technical reference for organizations designing revenue-oriented conversational AI systems while avoiding unverifiable empirical claims.
Real-World Deployment; Enterprise Conversational AI; Lead Generation; Rasa-Based Chatbot
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AI model that predicts antibiotic resistance from bacterial genomes (AMR) using open sequencing data
Antimicrobial resistance (AMR) prediction from bacterial whole genome sequencing can shorten time to effective therapy, strengthen surveillance, and reduce reliance on slow culture based susceptibility testing. However, many genomic AMR workflows still depend on curated gene and mutation catalogs, which can miss emerging mechanisms, vary in coverage across species, and rarely provide calibrated uncertainty. We present a phenotype first framework that trains models to predict resistant, intermediate, or susceptible outcomes directly from genome sequences using open repositories. Genomes and linked phenotypes are assembled from NCBI SRA and GenBank records and harmonized with BV BRC (formerly PATRIC) antibiotic panels. We compare three modeling families: catalog based baselines (AMRFinderPlus and ResFinder), k mer linear models with stability selection, and sequence transformers fine tuned on long k mer tokens. To support clinical decision making, predicted probabilities are calibrated with temperature scaling and wrapped with conformal prediction to yield distribution free confidence sets. Interpretability is addressed by mapping influential k mers and attention based attributions to genes and known resistance determinants in CARD and MEGARes, producing mutation importance summaries that can be reviewed by microbiologists. We define evaluation protocols that avoid leakage through lineage structure, including temporal splits and site stratified cross validation, and report discrimination, calibration, and abstention metrics. The result is a reproducible template for AMR phenotype modeling that complements rule based tools while providing transparent evidence and quantified uncertainty. The manuscript targets publication in November 2025 and emphasizes open science, auditability, and transferability across pathogens and antibiotics in public health practice.
Antimicrobial resistance; AMR prediction; bacterial whole-genome sequencing; genotype–phenotype modeling; antibiotic susceptibility; k-mer embeddings; sequence transformers; genome foundation models; BV-BRC (PATRIC); NCBI SRA; GenBank; AMRFinderPlus; ResFinder; CARD; MEGARes; interpretable machine learning; mutation importance; calibrated uncertainty; temperature scaling; conformal prediction; deep ensembles; lineage-aware validation; temporal split; public health surveillance.
Copyright (c) 2025 Frontiers in Computer Science and Artificial Intelligence
Authoritative Data Stores for Treasury Deposits: Sourcing, Certification, and Provisioning Standards for Enterprise Downstreams
The rapid growth of enterprise data volumes and the increasing demand for timely, accurate, and auditable financial reporting have compelled large organizations to rethink traditional extract–transform–load (ETL) architectures supporting treasury deposit systems. Between 2018 and 2019, enterprises—particularly in banking and financial services—faced mounting limitations with legacy SAS, mainframe COBOL, Informatica, and Oracle PL/SQL-based batch processing platforms. These systems were costly, vertically scaled, and constrained by overnight batch windows that could no longer meet modern regulatory, operational, and analytical requirements. This paper presents a comprehensive architectural and methodological framework for building Authoritative Data Stores (ADS) for treasury deposits using Apache Spark as the core distributed processing engine. The framework addresses data sourcing, certification, governance, and standardized provisioning to downstream consumers such as regulatory reporting, risk analytics, finance, and real-time dashboards. It elaborates on enterprise migration drivers, architectural comparisons between legacy ETL systems and Spark-based ecosystems, and the key challenges encountered during large-scale modernization initiatives. A structured migration methodology is proposed, encompassing baseline legacy assessment, code translation, data quality reconciliation, performance optimization, orchestration redesign, and phased cutover strategies. The paper further illustrates best practices through representative case studies demonstrating significant performance gains, cost reduction, and improved scalability. The findings highlight Spark’s role as a unifying compute platform capable of supporting batch, streaming, and advanced analytics workloads while maintaining the integrity and auditability required for treasury deposit data. The study concludes that Spark-enabled Authoritative Data Stores provide a resilient foundation for enterprise financial data modernization, enabling near real-time insights, improved governance, and long-term adaptability in evolving regulatory and business environments.
Authoritative Data Store, Treasury Deposits, Apache Spark, Legacy ETL Migration, Data Certification, Enterprise Data Architecture, Distributed Processing
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AI-Enhanced Sustainable Energy Management and Policy Recommendations for the U.S. Power Sector
The transition to a sustainable U.S. power sector is faced with the challenge of balancing reliability, decarbonization, and affordability in more variable demand conditions. There is tremendous potential for artificial intelligence (AI) to make huge contributions to forecasting accuracy, optimizing resource allocation, and in developing policy and evidence-based policy. This paper presents a new model of hybrid artificial intelligence for sustainable energy management applications that consists of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Transformer networks combined with CatBoost meta-learner and then optimized with residual BiGRU-attention and α-blender. The proposed model was tested using the public dataset of hourly electricity generation and demand data from the United States of America with temporal and operational characteristics significant to grid planning. Experimental results indicate a very high performance of the hybrid model compared with baseline methods for all performance measures. Specifically, it had a minimum MAE (0.007503), minimum MSE (0.000102) and RMSE (0.010082) and highest R2 value (0.995954) than single Lstm, GRU, Catboost and Xgboost. Aside from technical performance, the study accomplishes three contributions: efficiency-enhancing energy management using AI-driven energy forecasting and optimization, American context - use of demand forecasting for renewable integration and grid balancing, and extending technical understanding to the policy advice for future adaptive regulations. By integrating AI analytics, policy design, and sustainability policy, this research makes a methodological contribution as well as a governance-centric framework with a focus on setting up AI as an accelerator for the sustainable transformation of America's power grid.
Artificial intelligence, Hybrid deep learning, Energy demand forecasting, Sustainable energy management, Policy recommendations, U.S. power sector.
Shift-Left Security Validation of Containers via Kubernetes Admission Webhook
We propose a unified “shift-left” security validation framework that integrates static vulnerability scanning, SBOM generation, image signature verification, policy-as-code enforcement, and best-practice scoring into a single Kubernetes admission-control webhook invoked within the CI/CD pipeline. By atomically intercepting container admission requests, the system produces a Software Bill of Materials, cross-references CVE feeds, validates digital signatures, applies dynamically loaded JSON/YAML policies, and computes a weighted KubeScore for Pod specifications prior to deployment. Evaluation on realistic workloads demonstrates end-to-end processing latency below 200 ms and detection rates exceeding 95 % for critical vulnerabilities and misconfigurations. This consolidated approach eliminates post-deployment scans, accelerates feedback loops, strengthens compliance auditability with immutable logs, and lays the foundation for future AI-driven remediation and multi-cluster policy synchronization. Index Terms – Admission-Control Webhook, SBOM (Software Bill of Materials), Static Vulnerability Analysis, CVE (Common Vulnerabilities and Exposures), Image Signature Verification, Cosign, Notary v2, Policy-as-Code, Open Policy Agent, Kyverno, KubeScore, kube-bench, Dynamic Policy Loading, Hot-Reload, Automated Remediation, Self-Healing, Immutable Audit Logs, CI/CD Pipeline Integration, Kubernetes Best Practices, SBOM Formats: CycloneDX, SPDX, Threat Modeling, Compliance Automation.
Shift-Left Security Validation; Containers; Kubernetes Admission Webhook
Why Developers Fail When Building First-Generation Agents- Common Pitfalls and Cryptographically Grounded Design Solutions
The development of autonomous software agents, built upon large language models (LLMs) and integrated with external tools, marks a significant paradigm shift in computation. However, first-generation agent deployments often exhibit critical failure modes that undermine reliability and security. This article, grounded in established cryptographic and access control theory, analyzes five primary development pitfalls: reasoning failures, runaway loops, missing context, transient state loss, and faulty planning logic. We argue that these failures stem from an inadequate foundational separation between the agent's generative capability (the LLM) and its operational integrity (state, authorization, and execution). We propose a theoretical Verifiable Context-Aware Access Control (VCAAC) model. This model enforces trust not just on the agent's identity, but on the verifiable proof of its state and the computational path taken to reach a decision, utilizing Zero-Knowledge Proofs (ZKPs) and distributed capabilities to mitigate security risks inherent in autonomous execution. The discussion includes a realistic threat model and highlights scalability limitations, focusing exclusively on hypothetical benefits without recourse to fabricated performance data.
Autonomous Agents, Zero Trust, Cryptography, Access Control, Verifiable Computation, LLM Security, Agent Alignment
AI-Driven Big Data Analytics for Precision Medicine and Healthcare Intelligence: A Unified Framework for Cancer, Chronic Disease, and Clinical Decision Optimization
The explosion of various healthcare data from genomics to medical imaging to electronic health records, wearable sensors, and real-time clinical systems opens new opportunities for precision medicine. Yet traditional statistical and rule-based decision tools are stretched to the limits of analyzing the high-dimensional and multimodal and constantly changing data. Recent research has identified that AI-driven big data analytics can be used to scale up and provide better early detection of diseases, personalized treatment planning and clinical decision making in oncology and chronic diseases (Ahmed et al., 2025; Manik et al., 2025a). This paper synthesizes AI-enabled big data analytics for precision medicine, drawing on evidence from cancer diagnostics, prediction of chronic diseases, genomics-based drug discovery, and explainable clinical intelligence. It brings together the domains of deep learning, hybrid machine learning architectures, multimodal data fusion, and explainable AI (XAI) techniques that are subsumed into consolidated deep learning models into one unified framework that aims to enhance the performance of diagnostic accuracy, interpretability, and translational readiness (Forhad Hossain, 2025; Islam et al., 2025). The framework focuses on ethical data governance and scalable analytics pipelines, and clinician-centric transparency to bridge the gap between algorithmic innovation and real-world healthcare implementation. Synthesized results of findings show the consistent superiority of certain AI-relying big data analytics over traditional methods for disease classification, risk stratification, and therapeutic decision support and for certain other new challenges concerning model explainability, data privacy, and healthcare equity (Rahman et al., 2025; Samiun et al., 2025). By combining cross-domain evidence and proposing a unified analytical architecture, this work is designed to provide researchers, practitioners, and authorities with practical and useful knowledge by responding to the demand to operationalize at scale AI-powered precision medicine.
AI-Driven Precision Medicine; Big Data Analytics; Multimodal Healthcare Data; Explainable Artificial Intelligence; Predictive Clinical Modeling; Personalized Healthcare Systems
Artificial Intelligence for Revenue Forecasting, Risk Management, and Financial & Business Planning in International Sports Tourism: The FIFA World Cup USA 2026 Case
The upcoming FIFA World Cup USA 2026 represents one of the largest international sports tourism events in history, generating complex financial flows, multidimensional risks, and unprecedented planning challenges for host nations, cities, and private stakeholders. This study examines the role of Artificial Intelligence (AI) in enhancing revenue forecasting, risk management, and financial and business planning within the context of international sports tourism, using the FIFA World Cup USA 2026 as a case study. Drawing on predictive analytics, machine learning models, and big data integration, the paper demonstrates how AI-driven systems can improve the accuracy of demand forecasting, optimize pricing and taxation strategies, and support dynamic budget allocation across public and private sectors. The study further explores AI applications in risk identification and mitigation, including economic volatility, infrastructure cost overruns, cybersecurity threats, crowd management risks, and uncertainty in tourism inflows. By integrating real-time data from ticketing platforms, hospitality services, transportation systems, and macroeconomic indicators, AI enables scenario-based financial planning and evidence-based decision-making. The findings highlight that AI-supported financial frameworks not only enhance fiscal sustainability and investor confidence but also contribute to resilient and inclusive economic outcomes for host regions. This research provides practical insights for policymakers, financial planners, tourism authorities, and sport’s governing bodies seeking to leverage AI for strategic planning and long-term value creation in mega sporting events.
Revenue Forecasting, Risk Management, Financial Planning, Business Analytics, Sports Tourism, FIFA World Cup 2026, Predictive Analytics, Public Finance, Tourism Economics
Parallel Deep Learning for Cybersecurity-Oriented Multi-Platform Social Media Bot Detection
Social media is a key unit of today's cyber communication society; however, being widely available, social media networks also contribute to the diffusion of social bots, which are then abused for forging global digital weapons, such as spreading fake news, organized manipulation and infiltration, or malicious propaganda actions. However, identifying these bots in a multi-platform setting is difficult due to the heterogeneity of data structures and behaviours, as well as platform-specific issues. In this paper, we have presented a parallel DNN-based social media bot detection model for various platforms. The proposed architecture has a multi-head parallel structure sharing the latent representations and simultaneously learning the shared latent features and platform-specific independent detections. What it allows, however, is for the framework to handle both generic bot prototype behaviours and platform-specific functionalities in a single solution that scales well. The proposed approach is evaluated on two real-world datasets taken from Twitter and TikTok, including behavioural, engagement, and account-level features. Experiments show strong and consistent detection performance across the tested platforms. The detection heads on Twitter reach 94.47% accuracy, 94.42% F1-score, and a ROC-AUC of 94.77%, while those on TikTok obtain an accuracy of 91.29%, an F1-score of 92.92% and a ROC-AUC score of 96.30%. High precision–recall performance also confirms the effectiveness of the proposed system in detecting automated harmful accounts with low false positive rates.
Cyber Security, Bot Detection, Artificial Intelligence, Social Media
Socio-Technical Integration of AI Decision Support in Autism Care: Implications for Caregiver Workflows, Trust, and Workforce Sustainability
The integration of artificial intelligence into autism spectrum disorder care promises improved behavioral monitoring, early escalation detection, and enhanced decision support for caregivers. However, the success of such systems depends not only on technical accuracy but also on their alignment with human workflows, trust dynamics, and workforce sustainability. This study examines the socio-technical dimensions of AI deployment in autism care, focusing on how AI decision support systems interact with caregiver roles, responsibilities, and organizational contexts. We propose a human-centered socio-technical framework that integrates AI capabilities with caregiver workflows, trust calibration, and governance mechanisms. Using simulated care environments informed by empirical studies, the framework is evaluated in terms of caregiver workload, decision confidence, trust, and system acceptance. Results suggest that socio-technical alignment significantly improves caregiver engagement and long-term system sustainability. This research highlights the necessity of designing AI systems that support—not disrupt—the human foundations of autism care.
Autism spectrum disorder; Socio-technical systems; Caregiver decision support; Human-centered AI; Workforce sustainability; Trustworthy AI; Clinical workflows
A Trust-Calibrated Federated Learning Framework for Predicting Behavioral Escalation in Children with Autism Using Edge IoT Systems
Behavioral escalation episodes in children with autism spectrum disorder present complex clinical, ethical, and technological challenges due to their unpredictability, individual variability, and the sensitivity of behavioral data. Artificial intelligence and Internet of Things technologies have demonstrated potential for early escalation detection; however, centralized learning architectures introduce privacy risks, governance challenges, and caregiver mistrust. This study proposes a trust-calibrated federated learning framework deployed on edge IoT systems to predict behavioral escalation while preserving data locality and embedding human trust directly into the learning process. The framework integrates reinforcement learning for temporal behavior modeling, confidence-weighted federated aggregation, and caregiver-driven trust calibration aligned with AI risk management principles. Experimental evaluation using simulated autism care scenarios demonstrates improved prediction accuracy, reduced false alerts, and measurable gains in caregiver trust compared to centralized and non-trust-aware baselines. The proposed approach advances privacy-preserving, human-centered artificial intelligence for autism care and provides a scalable pathway for ethically aligned deployment in real-world environments.
Autism spectrum disorder; Federated learning; Edge IoT; Behavioral escalation prediction; Trustworthy AI; Reinforcement learning; Human-centered AI
Multimodal Behavioral Signal Fusion Using Wearable IoT and Context-Aware Machine Learning for Early Autism Escalation Detection
Behavioral escalation in children with autism spectrum disorder is a multifactorial phenomenon influenced by physiological, behavioral, and environmental factors that interact dynamically over time. Early detection of escalation remains challenging because many existing systems rely on single-modality signals or static thresholds that fail to capture individualized and context-dependent patterns. This study proposes a multimodal behavioral signal fusion framework that integrates wearable Internet of Things sensing with context-aware machine learning to enable early and reliable escalation detection. The framework combines physiological indicators, motion dynamics, and environmental context using adaptive feature weighting and temporal modeling to capture personalized escalation signatures. Experimental evaluation using simulated autism care scenarios informed by empirical studies demonstrates that multimodal fusion significantly improves early detection accuracy and robustness compared to unimodal approaches while reducing false alerts. The proposed architecture advances personalized, privacy-aware autism care by leveraging context-aware intelligence for proactive intervention.
Autism spectrum disorder; Multimodal data fusion; Wearable IoT; Context-aware machine learning; Behavioral escalation detection; Personalized monitoring; Smart healthcare
Operationalizing the NIST AI Risk Management Framework for AI-Driven Autism Care Systems: A Governance-Centric Technical Architecture
Artificial intelligence–driven systems are increasingly deployed in autism spectrum disorder care to support behavioral monitoring, escalation prediction, and caregiver decision-making. While these systems offer substantial clinical and operational benefits, they also introduce risks related to safety, privacy, bias, accountability, and trust—particularly when applied to vulnerable pediatric populations. The NIST Artificial Intelligence Risk Management Framework provides high-level guidance for managing AI risks, yet practical operationalization within real-world autism care systems remains limited. This study proposes a governance-centric technical architecture that embeds NIST AI Risk Management Framework principles directly into the design, deployment, and lifecycle management of AI-driven autism care systems. The framework translates abstract governance functions into concrete technical controls, audit artifacts, and operational workflows aligned with reinforcement learning, IoT-based behavioral monitoring, and caregiver decision support. Through a structured risk taxonomy, control catalog, and implementation blueprint, this research demonstrates how trustworthy AI principles can be transformed into deployable, auditable, and scalable autism care solutions.
Autism spectrum disorder; AI governance; NIST AI Risk Management Framework; Trustworthy AI; Clinical decision support; AI safety; Risk-aware system architecture