Power System Protection and Control (original—Relay), quarterly journal, is a national electro-technical journal. It has been published at home and abroad since 1973 Power System Protection and Control has been indexed in many reputable databases. They are Compendex (EI), Scopus, Chinese Science Citation Database (CSCD), Chinese Core Journal, Chinese Sci-tech Core Journal, Outstanding S & T Journals of China, RCCSE Chinese Authoritative Academic Journal (A+), The Excellent International Impact Academic Journals of China from 2014 to 2016, 2018,and 2017,2019,2020,2021 The Highest International Impact Academic Journals of China, Cambridge Scientific Abstracts (CSA), Ulrich's Periodicals Directory (UPD), Scientific Abstract (SA, INSPEC), JST, and Abstract Journal (AJ, VINITI). Editor-in-chief of Power System Protection and Control is Dr. ZHOU De-Gao, who is an Academician of Chinese Academy of Engineering and professor of Hunan University. Chairmen of Editorial Committee are Qiang LU and Shijie CHENG, who are academicians of Chinese Academy of Sciences. Editorial Committee is composed of 127 members, including 7 academicians and 10 international committee members. All of them hold posts in famous universities or research institutes at home and aboard, acquiring distinguished achievements in the field of power system protection and control. Power System Protection and Control is dedicated to presenting high-quality academic achievements reflecting scientific innovation, advanced technology in the field of Power System Protection and Control by scholars, researchers and engineers at home and abroad, and devotes herself to serving as an academic platform for researches in power industry. Aims: Paying attention to the academic development; Combining theory with practice; Being application-oriented; Serving the front-line research and production. Scope: Power System Protection and Control publishes academic papers with new techniques, new achievements, operation experiences, technical innovations and scientific management, technical regulations, or foreign advanced techniques in the field of power system protection and control including, but are not limited to, power system relay protection, power system analysis and control, power system planning, integrated energy system, new energy generation, smart substation, intelligent power transmission and consumption, influence of large-scale electric vehicles integration on power grid, micro-grid techniques, application of power electronics in power system, power system automation and remote control techniques, power system communication, electricity market, power quality, Risk Management, Risk Management in Engineering Systems, Industrial Engineering, System Engineering, Medical Radiation Engineering, etc. Main Columns: Theoretical analysis, survey, application research, design and development, engineering application, special series on power industry development and hotspot, etc. Open Access: All articles published by Power System Protection and Control are made freely and permanently accessible online immediately upon publication, without subscription charges or registration barriers. Power System Protection and Control will have copyright of the manuscript upon acceptance, including the right to reproduce, publish, transmit, translate and edit the work. The authors can use or share the published article under the Attribution-Non Commercial 4.0 International (CC BY-NC 4.0) license. Power System Protection and Control applies the Creative Commons Attribution-NonCommercial (CC-BY-NC 4.0)(https://creativecommons.org/licenses/by-nc/4.0) which permits unresticted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Power System Protection and Control has followed the OA policy of the journal since 2008, and it conforms to the definition given by DOAJ. Authorship: Researchers, designers, equipment manufacturers from power enterprises and departments. Professors and students in universities with faculties of power system and its related fields; technical staffs from power plants, substations, power transmission and distribution system and power supply companies; personnel engaged in electric power production, construction, and operation & management; individuals from the fields of metallurgy, coal mining, petrifaction, railroad construction, machining, etc.
THE ROLE OF TAX AUDITING IN ENHANCING THE EFFECTIVENESS OF THE SELF-ASSESSMENT METHOD AND ITS IMPACT ON REDUCING TAX EVASION (AN EXPLORATORY STUDY OF THE OPINIONS OF A SAMPLE OF EMPLOYEES OF THE LARGE TAXPAYERS DIRECTORATE, ITS TAXPAYERS, AND CERTIFIED PU
This study aims to clarify the role of tax auditing in the self-assessment method and its impact on reducing tax evasion. This main objective is achieved through several sub-objectives, including identifying the concept and importance of the self-assessment method, explaining the mechanism for its application in the city of Erbil, and presenting the legal framework governing its implementation. The study also analyzes the relationship and measures the effect of tax auditing conducted by the General Commission for Taxes on the self-assessment method, and clarifies the role of the existing determinants of the self-assessment method in calculating taxable income in terms of application procedures in Erbil. The research problem lies in the lack of clarity regarding the extent to which tax auditing is capable of enhancing the effectiveness of the self-assessment method in tax assessment and reducing tax evasion within an organizational and institutional environment facing several challenges related to the legal framework, the level of tax awareness, and the adopted accounting systems and There is a statistically significant relationship and effect between the application of the self-assessment method and the reduction of tax evasion ,and what extent does the application of the self-assessment method have a statistically significant effect in reducing tax evasion. In its theoretical aspect, the study relied on the inductive approach to collect relevant scientific information, while the descriptive–analytical approach was adopted in the field study through distributing a questionnaire to a purposive sample of (368) respondents, consisting of employees of the Large Taxpayers Directorate and its affiliated taxpayers (economic units), as well as certified public accountants holding a legal accounting qualification in the city of Erbil. The data were analyzed using the Statistical Package for the Social Sciences (SPSS). The study reached several results, most notably that tax auditing contributes to achieving tax justice, as the practice of tax auditing on economic units for the purpose of verifying the tax amount places all units in an equal position before tax law. Under the self-assessment method, mutual trust is established between the tax administration on the one hand and taxpayers and auditors on the other. Moreover, it reduces bargaining and administrative corruption by minimizing direct contact between taxpayers and tax administration employees. The existence of strict and well-organized tax auditing increases the level of taxpayer compliance and limits attempts to circumvent the tax system, ultimately leading to a reduction in tax evasion and an enhancement of public revenues. The study recommends developing the skills of tax auditors through specialized training programs in financial data analysis and tax evasion risks, and adopting electronic auditing tools that contribute to accelerating examination and verification processes. It also recommends adopting the self-assessment method more widely, while providing a supportive regulatory environment that includes clear legislation and simplified procedures for submitting electronic tax returns.
Tax auditing, self-assessment method, tax evasion.
Avan Asaad Othman, Assist. Prof. Dr. Arshad Sedeeq Abdullah
A NATIONAL FRAMEWORK FOR EXPLAINABLE AND BIAS-RESISTANT AI IN U.S. HEALTHCARE DECISION SYSTEMS
Artificial intelligence has rapidly transformed healthcare decision-making in the United States, yet concerns about algorithmic bias, transparency, and accountability remain inadequately addressed. This research proposes a comprehensive national framework for implementing explainable and bias-resistant AI systems across U.S. healthcare institutions. The study examines current AI deployment practices, identifies critical vulnerabilities in existing systems, and develops policy recommendations grounded in ethical AI principles. Through analysis of healthcare AI implementations from 2019-2024 and stakeholder surveys involving 280 healthcare administrators, clinicians, and patients, the research reveals that 67% of deployed AI systems lack adequate explainability mechanisms, while 58% show evidence of demographic bias in decision outputs. The proposed framework integrates technical standards for algorithmic transparency, continuous bias monitoring protocols, regulatory oversight mechanisms, and patient rights protections. Findings indicate that structured governance combining federal regulatory standards with institutional accountability measures can substantially improve AI fairness and trustworthiness. This framework addresses the urgent need for systematic approaches to ensure AI-driven healthcare decisions serve all populations equitably while maintaining clinical effectiveness.
Artificial intelligence, healthcare systems, algorithmic bias, explainable AI, health equity, clinical decision support, regulatory framework, algorithmic accountability
Sonia Nashid, Ispita Jahan, Rifat Chowdhury, Tahmina Akter Bhuya Mita
ENVIRONMENTAL TOXICOLOGY AND ITS IMPLICATIONS FOR KENYA'S NATIONAL SECURITY
Kenya’s national security framework is increasingly challenged by internal issues that are not rooted in armed conflict or terrorism, but rather in the deteriorating relationship between human activity and the environment (BGS, 2023; NEMA, 2022). This study is driven by the urgent need to understand how toxic exposures are undermining Kenya's human security and threatening national stability. It adopts an interdisciplinary approach, drawing from environmental science, public health, security studies, and governance frameworks, to present a comprehensive analysis of the toxicological threats facing the country. By identifying the links between pollution and insecurity, the study aims to contribute to policy formulation, risk mitigation strategies, and improved national resilience. Employing a qualitative meta-synthesis grounded in the PRISMA framework, the research delineates pollution on different sectors in the country. By analysing multiple domains such as public health, food and water systems, youth vulnerability, military readiness, and governance, the research demonstrates how environmental toxins compromise Kenya’s ability to ensure the safety, stability, and sustainability of its people and institutions. Ultimately, it argues that environmental toxicology must be integrated into national security planning if Kenya is to safeguard its population, environment, and sovereignty. In doing so, it offers a fresh lens through which to interpret and respond to Kenya’s 21st-century security challenges. This paper fills a gap in understanding how environmental toxicology translates into tangible national security risks in developing contexts like Kenya.
Environmental toxicology, National security, Human security, Pollution, Kenya, PRISMA framework
STATE-OF-THE-ART TRI-BAND MIMO ANTENNA SYSTEMS FOR MULTI-STANDARD SUB-6 GHZ WIRELESS COMMUNICATIONS: DESIGN, PERFORMANCE, AND RESEARCH CHALLENGES
The rapid deployment of fifth-generation (5G) wireless networks has intensified the demand for compact, high-performance antenna systems capable of delivering high data rates, low latency, and reliable connectivity, particularly within the Sub-6 GHz spectrum due to its balanced coverage and penetration characteristics. In this context, Multiple-Input Multiple-Output (MIMO) antennas have emerged as a key enabling technology; however, achieving multi-band operation within compact MIMO architectures remains challenging because of mutual coupling, limited impedance bandwidth, and strict size constraints. This work presents the design, simulation, and experimental validation of a compact tri-band MIMO antenna operating at 2.4–2.5 GHz, 3.4–3.6 GHz, and 4.6–4.8 GHz, specifically targeting Sub-6 GHz applications such as 5G New Radio, Internet of Things (IoT), smart city infrastructure, cloud-based communication, and fixed wireless access systems. The antenna is designed on a low-cost FR4 substrate and optimized using ANSYS HFSS to enhance impedance matching, isolation, and diversity performance. Simulated results demonstrate return loss values below −10 dB, voltage standing wave ratio (VSWR) less than 2, and inter-element isolation exceeding 20 dB across all operating bands. Key MIMO performance metrics, including channel capacity loss, are carefully evaluated, with values maintained below 0.1, indicating excellent diversity efficiency and minimal correlation between antenna elements. A fabricated prototype is experimentally characterized using a vector network analyzer, showing close agreement between simulated and measured results. The proposed tri-band MIMO antenna is expected to offer improved radiation efficiency, robust isolation, and reliable multi-band performance while maintaining compact size and cost-effectiveness, making it a strong candidate for next-generation Sub-6 GHz wireless communication systems and providing a scalable design framework for future enhancements such as reconfigurable structures and intelligent optimization techniques.
Tri-band MIMO antenna, Sub-6 GHz communication, 5G wireless systems, Mutual coupling reduction, Channel capacity loss, HFSS simulation, FR4 substrate, IoT and smart city applications, Antenna isolation, multi-standard wireless networks
Mr. Suresh Shripatrao Kambale, Dr. Anuradha Deshpande
SUPPLY CHAIN RESILIENCE THROUGH INTEGRATED SAP PRODUCTION PLANNING – PROCUREMENT SYNCHRONIZATION
Supply chain disruptions have intensified dramatically in recent years, exposing critical vulnerabilities in traditional procurement and production planning systems. Organizations increasingly recognize that isolated, siloed enterprise resource planning modules cannot adequately respond to volatile demand patterns, supplier uncertainties, and operational complexities. This research examines how integrated synchronization between SAP Production Planning (PP) and Procurement modules enhances supply chain resilience through improved visibility, coordinated decision-making, and adaptive responses to disruptions. Through a mixed-methods study involving implementation analysis at five manufacturing organizations and quantitative assessment of key performance indicators over 18 months, this research demonstrates that integrated SAP PP-Procurement synchronization reduces stockout incidents by 42%, decreases emergency procurement costs by 38%, and improves on-time delivery performance by 31% compared to traditional siloed approaches. The study develops a comprehensive integration framework encompassing master data harmonization, real-time information exchange, collaborative planning processes, and exception management protocols. Findings reveal that successful integration requires not only technical configuration but also organizational change management, cross-functional collaboration, and continuous process refinement. The research identifies critical success factors including executive sponsorship, clear data governance, standardized workflows, and balanced automation with human oversight. This work contributes both a validated integration methodology for SAP PP-Procurement synchronization and empirical evidence of its impact on supply chain resilience, providing practical guidance for organizations seeking to strengthen their operational capabilities against disruptions.
Supply chain resilience, SAP integration, production planning, procurement synchronization, ERP systems, materials management, demand-supply alignment, operational efficiency
INFLUENCE OF INNOVATION AND INFORMATION TECHNOLOGIES ON THE QUALITY OF HIGHER EDUCATION IN BUCARAMANGA
The quality of higher education in Colombia is a determining factor for its socioeconomic development and the reduction of inequalities. Although coverage in higher education has shown a significant increase, reaching 54.90% in 2022, educational quality remains a central concern. This study focuses on Bucaramanga, a representative city in Colombia, aiming to investigate how information and communication technologies (ICT) and innovation can contribute to improving educational quality. A quantitative approach was used, applying statistical models of correlation and multiple regression, which allowed for the identification of significant relationships between the analyzed variables. The findings suggest that improvements in technological infrastructure and teacher training are essential for elevating educational quality, with direct implications for student training and employability.
Higher Education, Information and Communication Technologies, Innovation, Educational Quality, Bucaramanga, Colombia.
Isabel Cristina Rincón Rodríguez, Jorge E. Chaparro Medina, Javier Carreón Guillén, Julio E Crespo, Cruz García Lirios, Albeiro Beltrán Díaz
ARTIFICIAL INTELLIGENCE INTERACTION AND COMPUTER SELF-EFFICACY: A STRUCTURAL EQUATION MODELING APPROACH USING PLS-SEM
The rapid expansion of artificial intelligence technologies has transformed the way individuals interact with digital systems, generating new forms of technological engagement and cognitive adaptation. Understanding the factors that influence user interaction with intelligent systems has therefore become an important area of research in information systems and digital behavior studies. The present study examines the structural relationships between artificial intelligence interaction, technology usage, computer self-efficacy, and user satisfaction through a structural equation modeling approach using Partial Least Squares (PLS-SEM). A quantitative, cross-sectional design was implemented using simulated data from a sample of 400 users with experience interacting with digital technologies and artificial intelligence platforms. The measurement model was specified using reflective indicators measured through a five-point Likert scale. The structural model was estimated using the SmartPLS algorithm and evaluated through reliability, convergent validity, and predictive capacity indicators, including composite reliability, average variance extracted, path coefficients, and coefficients of determination. The results indicate that artificial intelligence significantly influences technology usage, which in turn contributes to the development of computer self-efficacy. Computer self-efficacy emerged as the strongest predictor of user satisfaction, highlighting the central role of perceived technological competence in shaping positive experiences with digital systems. The model also demonstrates that computer self-efficacy acts as a mediating mechanism between technological interaction and satisfaction outcomes. The explanatory power of the model shows moderate to substantial levels of variance explained in the endogenous constructs. These findings suggest that the successful integration of artificial intelligence technologies in digital environments depends not only on technological performance but also on users’ confidence in their ability to interact with intelligent systems. Strengthening technological competence and digital literacy may therefore enhance user satisfaction and facilitate the adoption of AI-based technologies.
Artificial Intelligence; Computer Self-Efficacy; Technology Usage; User Satisfaction; Structural Equation Modeling; PLS-SEM; SmartPLS; Digital Behavior.
Víctor Hugo Meriño Córdoba, Rosaura Sánchez Aguilar, Felipe de Jesús Vilchis Mora, Rosa María Rincón Ornelas, Gilberto Bermúdez Ruíz, Sonia Sujell Velez Baez, Francisco Ruben Sandoval Vázquez, María Luisa Quintero Soto, Josefina Haydee Gutiérrez Hernández
ARTIFICIAL INTELLIGENCE ADOPTION AND QUALITY OF LIFE: A STRUCTURAL EQUATION MODELING APPROACH USING PLS-SEM
Artificial intelligence has become a central component of contemporary digital transformation, influencing social interactions, access to information, and decision-making processes across multiple domains. As intelligent technologies become increasingly integrated into everyday activities, understanding their implications for human well-being and quality of life has become an important research priority. The present study examines the structural relationships between artificial intelligence adoption, perceived benefits of artificial intelligence, life satisfaction, and psychological well-being. A quantitative and cross-sectional research design was implemented using a survey-based data collection procedure. The measurement instrument included reflective indicators measured on a five-point Likert scale. The proposed theoretical model was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) through the SmartPLS software. The analysis followed a two-step procedure consisting of the evaluation of the measurement model and the structural model. Reliability and validity were assessed through indicator loadings, composite reliability, Cronbach’s alpha, and average variance extracted. Structural relationships were examined using path coefficients, coefficients of determination, and bootstrapping procedures to determine statistical significance. The results indicate that artificial intelligence adoption significantly influences perceived technological benefits and life satisfaction. Additionally, perceived benefits of artificial intelligence contribute positively to individuals’ evaluations of their life conditions. Life satisfaction was found to be a strong predictor of overall well-being, highlighting the role of subjective evaluations in shaping psychological outcomes. The structural model demonstrated substantial explanatory power for life satisfaction and well-being, indicating that perceptions related to artificial intelligence technologies are relevant predictors of quality-of-life indicators in digital environments. These findings contribute to the growing interdisciplinary literature connecting technological innovation with human well-being. The study highlights the importance of designing artificial intelligence systems that enhance accessibility, efficiency, and user-centered experiences. Understanding how individuals perceive and interact with intelligent technologies is essential for ensuring that digital transformation contributes positively to human development and social well-being.
Artificial Intelligence; Quality of Life; Life Satisfaction; Well-Being; Structural Equation Modeling; PLS-SEM; Technology Adoption; Digital Transformation.
Minerva Isabel Pérez Ortega, Celia Yaneth Quiroz Campas, Lizeth Armenta Zazueta, Wilfrido isidro Aldana Balderas, Jorge Hernández Valdés, Francisco Rubén Sandoval Vázquez, Mariann Valenzuela Rincón, Nadya Elizabeth Vasquez Segura, Alfredo Barrera Escobar,
ARTIFICIAL INTELLIGENCE AND UNIVERSITY GOVERNANCE: A PLS-SEM MODEL OF TECHNOLOGICAL ADOPTION, ACADEMIC QUALITY, AND INSTITUTIONAL TRUST
The integration of artificial intelligence into higher education institutions has generated new opportunities for improving governance processes, decision-making systems, and academic management. Universities increasingly rely on intelligent technologies to process large volumes of institutional data, support policy development, and enhance educational quality. However, the relationship between artificial intelligence adoption and governance outcomes remains an emerging area of empirical research. The objective of this study is to analyze the structural relationships between artificial intelligence adoption and university governance outcomes through a Structural Equation Modeling approach estimated using Partial Least Squares (PLS-SEM). The proposed model examines the causal relationships among six latent constructs: artificial intelligence adoption, perceived technological benefits, technological challenges, policy development, academic quality, and institutional trust. Data were collected through a structured questionnaire using reflective indicators measured on a seven-point Likert scale. The results indicate that artificial intelligence adoption significantly influences perceived technological benefits, which in turn affect policy development and academic quality. Policy development also contributes positively to academic quality, highlighting the importance of governance frameworks that integrate technological innovation into institutional management. Furthermore, both academic quality and technological challenges influence institutional trust, suggesting that stakeholder confidence depends on the effective and responsible implementation of intelligent systems within university governance structures. The structural model demonstrates satisfactory explanatory power and acceptable model fit indicators, confirming the relevance of artificial intelligence as a driver of institutional transformation in higher education. The findings suggest that universities that strategically integrate artificial intelligence technologies into governance processes are better positioned to enhance academic performance, improve administrative efficiency, and strengthen stakeholder trust. This study contributes to the literature on digital transformation and higher education governance by proposing an empirical model that explains how artificial intelligence adoption influences institutional outcomes through governance mechanisms and academic management processes.
Artificial Intelligence; University Governance; Structural Equation Modeling; PLS-SEM; Academic Quality; Institutional Trust; Digital Transformation; Higher Education Governance.
Juan Guillermo Mansilla Sepúlveda, Celia Yaneth Quiroz Campas, Lizeth Armenta Zazueta, Javier Carreón Guillén, Reyna Amador Velázquez, Paulett Valenzuela Rincón, Gabriel Pérez Crisanto, Francisco Ruben Sandoval Vázquez, Arturo Sánchez Sánchez, Isabel Cris
INTEGRATING AI-BASED SENTIMENT ANALYSIS INTO CORPORATE FX RISK MANAGEMENT: EFFECTS ON HEDGING DECISIONS DURING PERIODS OF HIGH VOLATILITY
Foreign exchange risk management remains one of the most challenging aspects of international business operations, particularly during periods of heightened market volatility. Traditional hedging strategies rely primarily on historical data and technical indicators, often failing to capture the rapidly shifting market sentiment that drives currency movements during volatile periods. This research examines how AI-based sentiment analysis, applied to news feeds, social media, and financial communications, can enhance corporate foreign exchange hedging decisions. Through analysis of hedging outcomes across multiple corporations during recent high-volatility periods including the 2022 inflation surge and 2023 banking crisis, we demonstrate that sentiment-augmented hedging strategies outperform traditional approaches by 18-27% in terms of hedge effectiveness. The study reveals that AI sentiment analysis provides early warning signals of market shifts approximately 6-12 hours before traditional indicators, enabling more timely hedging adjustments. However, implementation challenges including false signals during extreme volatility and integration complexity with existing treasury systems require careful management. This research contributes practical frameworks for incorporating sentiment analysis into FX risk management processes while identifying conditions under which sentiment-based approaches deliver greatest value.
Foreign Exchange Risk, Sentiment Analysis, Artificial Intelligence, Hedging Strategies, Market Volatility, Treasury Management, Corporate Finance
ZERO TRUST ARCHITECTURES: ENHANCING DATA PROTECTION IN REMOTE WORK ENVIRONMENTS
The rapid shift to remote work, accelerated by global events and technological advancement, has fundamentally transformed organizational security landscapes. Traditional perimeter-based security models that assumed trust within corporate networks have become inadequate as employees access sensitive data from diverse locations, devices, and networks. Zero Trust Architecture (ZTA) emerges as a comprehensive security paradigm addressing these challenges through the principle of "never trust, always verify." This research examines how Zero Trust frameworks enhance data protection in remote work environments by eliminating implicit trust, implementing continuous verification, and enforcing least-privilege access controls. Through analysis of implementation strategies across healthcare, financial services, and technology sectors, we demonstrate that organizations adopting Zero Trust experience 68% reduction in security incidents, 73% faster threat detection, and 54% improvement in compliance adherence. The architecture's core components—identity verification, device authentication, micro-segmentation, and continuous monitoring—create defense-in-depth layers protecting data regardless of access location. However, implementation challenges persist including organizational resistance to cultural change, technical complexity requiring 12-18 months for full deployment, user experience friction from frequent authentication, and substantial initial investment averaging $2.8 million for enterprise implementations. The research reveals that successful Zero Trust adoption requires phased implementation beginning with critical assets, executive sponsorship overcoming cultural inertia, comprehensive employee training addressing workflow changes, and automation tools managing policy enforcement at scale. As remote work becomes permanent fixture rather than temporary accommodation, Zero Trust Architecture provides essential framework for maintaining data security without sacrificing productivity or user experience.
Zero Trust Architecture, Remote Work Security, Data Protection, Identity Management, Continuous Verification, Network Segmentation, Access Control, Cybersecurity
COMPARATIVE PERFORMANCE OF MACHINE LEARNING MODELS INCORPORATING MACROECONOMIC INDICATORS FOR CREDIT RISK EARLY WARNING
Credit risk management remains a critical concern for financial institutions, particularly during economic downturns when default rates surge unexpectedly. This research evaluates the comparative performance of six machine learning models—Logistic Regression, Random Forest, Gradient Boosting, Support Vector Machine, Neural Networks, and XGBoost—in predicting credit default by incorporating macroeconomic indicators alongside traditional borrower characteristics. Using a dataset of 45,000 loan accounts from 2015-2023, the study examines how macroeconomic variables including GDP growth, unemployment rate, inflation, and interest rates enhance predictive accuracy of credit risk early warning systems. Results demonstrate that ensemble methods, particularly XGBoost and Gradient Boosting, achieve superior performance with AUC scores of 0.89 and 0.87 respectively when macroeconomic indicators are integrated. The inclusion of macroeconomic factors improves prediction accuracy by 8-12% compared to models using only borrower-specific variables. Neural networks show strong performance in capturing non-linear relationships between economic conditions and default probability. The findings provide practical insights for financial institutions seeking to develop robust early warning systems that anticipate credit risk deterioration during economic stress periods.
Credit risk, machine learning, macroeconomic indicators, early warning systems, default prediction, financial risk management, XGBoost.
STRICT LIABILITY OR FAULT-BASED REGIMES FOR AI-CAUSED HARM? A DOCTRINAL ANALYSIS ACROSS COMMON LAW AND CIVIL LAW SYSTEMS
The rapid deployment of artificial intelligence systems across critical sectors has created unprecedented challenges for traditional tort law frameworks. When autonomous vehicles cause accidents, medical diagnosis algorithms produce errors, or automated decision systems generate discriminatory outcomes, fundamental questions arise about liability attribution. This research examines whether strict liability or fault-based regimes better address AI-caused harm through comparative doctrinal analysis across common law and civil law jurisdictions. The study analyzes existing liability frameworks in the United States, United Kingdom, Germany, France, and Japan, evaluating how each system's principles apply to AI-specific scenarios. Through examination of recent case law, legislative proposals, and doctrinal scholarship, the research identifies critical gaps in current frameworks where AI's autonomous decision-making, opacity, and distributed responsibility challenge traditional causation and fault concepts. Findings reveal that pure fault-based approaches struggle with AI's "black box" problem and difficulty establishing negligence standards, while strict liability faces challenges in defining "defect" for learning systems and determining liable parties in complex AI supply chains. The research proposes a hybrid model incorporating elements from both regimes, with strict liability for high-risk AI applications and modified fault standards for lower-risk contexts. This framework addresses the unique characteristics of AI while maintaining accountability and encouraging responsible innovation across different legal traditions.
Artificial intelligence liability, strict liability, negligence, tort law, AI regulation, product liability, common law, civil law.
AUTONOMOUS THREAT DETECTION: ADVANCED AI-DRIVEN CYBERSECURITY SYSTEMS FOR REAL-TIME RESPONSE
The exponential growth of cyber threats has rendered traditional security approaches increasingly inadequate for protecting modern digital infrastructure. This research examines autonomous threat detection systems powered by artificial intelligence and machine learning technologies that enable real-time identification and response to sophisticated cyberattacks. The study investigates how AI-driven systems can autonomously detect, analyze, and neutralize security threats without human intervention, addressing the critical time gap between threat emergence and organizational response. Through analysis of contemporary cybersecurity frameworks and examination of deployed AI systems across various organizational contexts, this research evaluates the effectiveness, challenges, and future potential of autonomous threat detection mechanisms. Findings demonstrate that AI-driven systems reduce average threat detection time from hours to milliseconds, improve accuracy by 40-60% compared to traditional methods, and significantly enhance organizational security postures. However, implementation challenges including false positive rates, adversarial attacks on AI models, and integration complexities persist. The study concludes with recommendations for developing robust, adaptive autonomous security systems capable of countering evolving cyber threats in increasingly complex digital environments.
: Autonomous threat detection, artificial intelligence, cybersecurity, machine learning, real-time response, intrusion detection, behavioral analytics, threat intelligence
REAL-TIME MONITORING OF PHYSIOLOGICAL CHANGES USING CNT-BASED NANOSENSORS
The integration of carbon nanotube technology into physiological monitoring represents a transformative advancement in healthcare diagnostics and patient care. This research investigates the development and application of CNT-based nanosensors for continuous, real-time monitoring of critical physiological parameters including glucose levels, cardiac biomarkers, neurological signals, and metabolic indicators. Traditional monitoring systems rely on invasive procedures, intermittent sampling, and bulky equipment that limits patient mobility and comfort. CNT nanosensors address these limitations through their exceptional electrical conductivity, biocompatibility, miniaturization potential, and sensitivity to molecular interactions. Our comprehensive analysis examines sensor design principles, fabrication techniques, signal transduction mechanisms, and clinical applications across multiple physiological domains. The research demonstrates that CNT-based sensors achieve detection sensitivities in the nanomolar range while maintaining biocompatibility suitable for extended wearable and implantable applications. Integration with wireless communication systems enables continuous data streaming to healthcare providers, facilitating early intervention and personalized treatment optimization. This work establishes both the technical foundations and practical pathways for translating CNT nanosensor technology from laboratory research into clinical reality, addressing critical gaps in continuous patient monitoring capabilities.
Carbon Nanotubes, Nanosensors, Physiological Monitoring, Real-time Detection, Biocompatibility, Wearable Sensors, Biomarker Detection.
Dr. Brajesh Kumar Mishra, Dr. Darshan B D, Dr. Indu Shukla, Sudipta Dash, Shashank Mishra
IMPLEMENTING A ZERO-TRUST SECURITY FRAMEWORK TO MITIGATE INSIDER THREATS IN CLOUD-BASED INFRASTRUCTURES
Cloud computing has fundamentally transformed how organizations manage data and deliver services, yet this shift introduces significant security vulnerabilities, particularly from insider threats that exploit trusted access privileges. Traditional perimeter-based security models operating on implicit trust assumptions prove inadequate in cloud environments where organizational boundaries blur and users access resources from diverse locations and devices. This research examines the implementation of Zero-Trust security frameworks specifically designed to mitigate insider threats within cloud infrastructures. We analyze the core Zero-Trust principles of "never trust, always verify" through continuous authentication, micro-segmentation, least privilege access, and comprehensive monitoring. A practical implementation framework is developed incorporating identity and access management, network segmentation, data encryption, behavioral analytics, and continuous verification mechanisms. Evaluation across three case studies spanning financial services, healthcare, and technology sectors demonstrates that Zero-Trust implementations reduce insider threat incidents by 67-73% while decreasing mean time to detect anomalous behavior from 197 days to 12 days. However, challenges persist including implementation complexity requiring 8-14 months for complete deployment, user experience friction from frequent authentication demands, and operational overhead increasing security team workload by 35-40% initially. The research reveals that successful Zero-Trust adoption requires phased implementation prioritizing critical assets, comprehensive employee training addressing cultural resistance, and automation tools managing policy enforcement and monitoring at scale. Despite implementation challenges, Zero-Trust architectures provide demonstrably superior protection against insider threats compared to traditional models, particularly critical for cloud environments where implicit trust creates unacceptable risk exposure.
Zero-Trust Security, Insider Threats, Cloud Security, Access Control, Continuous Verification, Micro-segmentation, Identity Management, Cloud Infrastructure.
MACHINE LEARNING TECHNIQUES APPLIED TO INTRUSION DETECTION SYSTEMS
Cybersecurity threats have evolved dramatically in sophistication and frequency, rendering traditional signature-based intrusion detection systems increasingly ineffective against novel and zero-day attacks. Machine learning techniques offer promising solutions by enabling systems to learn normal network behavior patterns and identify anomalous activities that may indicate intrusions. This research comprehensively examines the application of machine learning algorithms to intrusion detection systems, analyzing supervised, unsupervised, and hybrid approaches across various network environments. We evaluate classical algorithms including decision trees, support vector machines, and naive Bayes alongside advanced techniques such as deep learning, ensemble methods, and reinforcement learning. Performance analysis using standard datasets including NSL-KDD, CICIDS2017, and UNSW-NB15 reveals that ensemble methods combining multiple algorithms achieve superior detection rates of 96-98% with false positive rates below 2%, outperforming individual classifiers by 8-12%. Deep learning approaches, particularly convolutional and recurrent neural networks, demonstrate exceptional capability in detecting complex attack patterns with 97.3% accuracy, though requiring substantial computational resources and training time. However, significant challenges persist including imbalanced datasets where attack samples comprise only 0.1-5% of traffic, concept drift as attack methodologies evolve, adversarial attacks targeting machine learning models themselves, and interpretability concerns where black-box models provide limited insight into detection reasoning. The research reveals that no single machine learning technique dominates across all metrics—supervised methods excel at detecting known attack types, unsupervised approaches identify novel threats, and hybrid systems balance both capabilities. Practical deployment considerations including real-time processing requirements, computational constraints, and integration with existing security infrastructure significantly influence algorithm selection. This work provides evidence-based guidance for security practitioners selecting appropriate machine learning techniques for intrusion detection based on specific operational requirements, threat landscapes, and resource availability.
Intrusion Detection Systems, Machine Learning, Cybersecurity, Anomaly Detection, Deep Learning, Network Security, Ensemble Methods, Attack Classification
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NEUROFUSION: A UNIFIED AI MODEL FOR MULTI-MODAL HEALTHCARE DATA ANALYSIS
Modern healthcare generates vast quantities of heterogeneous data from multiple sources including medical imaging, electronic health records, genomic sequences, and wearable sensor streams. However, most artificial intelligence systems analyze these modalities in isolation, missing critical cross-modal relationships that could improve diagnostic accuracy and treatment planning. This research presents NeuroFusion, a unified deep learning architecture that integrates and analyzes multi-modal healthcare data through shared latent representations and cross-attention mechanisms. The model employs modality-specific encoders processing medical images, clinical text, time-series physiological signals, and genomic data, which feed into a fusion layer learning complementary information across modalities. Evaluated on three clinical datasets encompassing cardiovascular disease diagnosis, cancer prognosis, and ICU mortality prediction, NeuroFusion achieves 94.2% accuracy in cardiovascular classification, 89.7% in cancer outcome prediction, and 91.3% in mortality forecasting—representing 6-8% improvements over single-modality baselines and 3-5% gains compared to simple concatenation approaches. The model's attention mechanisms provide interpretable insights into which data modalities contribute most to specific predictions, addressing the black-box criticism of deep learning in clinical contexts. However, challenges persist including computational requirements demanding 32GB GPU memory for training, data alignment complexity across modalities with different temporal resolutions, and limited generalization when deployed on institutions with different data collection protocols. Privacy considerations necessitate federated learning approaches when training across multiple hospitals. Despite these limitations, NeuroFusion demonstrates that unified multi-modal architectures can unlock synergistic information from diverse healthcare data sources, paving the way toward more comprehensive AI-assisted clinical decision support systems.
Multi-modal Learning, Healthcare AI, Deep Learning, Medical Data Fusion, Clinical Decision Support, Attention Mechanisms, Diagnostic Systems
FINANCIAL PERFORMANCE OPTIMIZATION THROUGH OPERATIONAL CONTROLS
Organizations increasingly recognize that sustainable financial performance depends not just on strategic planning but on robust operational controls that translate strategy into consistent execution. This research examines how operational control systems directly influence financial outcomes across organizational levels. The study addresses a critical gap in understanding the specific mechanisms through which operational controls—including process standardization, quality management, resource allocation controls, and performance monitoring—create measurable financial value. Through comprehensive analysis of control frameworks and their financial impacts, we develop an integrated model linking operational discipline to profitability, cash flow optimization, and shareholder value creation. Our findings demonstrate that organizations with mature operational control systems achieve 23-35% higher returns on invested capital compared to peers with weak controls. The research reveals that operational controls work through multiple pathways: reducing waste and inefficiency, improving asset utilization, accelerating cash conversion cycles, and enabling more accurate financial forecasting. This work contributes practical frameworks for implementing operational controls that deliver measurable financial improvements while maintaining organizational agility.
Operational Controls, Financial Performance, Process Optimization, Performance Management, Cost Control, Operational Efficiency, Return on Investment
THE ROLE OF TECHNOLOGICAL MEDIATIONS IN TEACHING AND ASSESSMENT IN EDUCATION: A THEORETICAL AND EMPIRICAL ANALYSIS
This article analyses the impact of technological mediations on teaching and assessment in education, focusing on how digital innovations are reshaping pedagogical practices and evaluative processes. Employing a mixed-method approach that integrates qualitative insights and quantitative data, the study examines the extent to which digital tools contribute to transforming traditional teaching models. Technology enables greater personalization of learning, offering students opportunities to progress at their own pace and engage with adaptive content. Furthermore, digital platforms enhance the quality and immediacy of feedback, fostering continuous assessment and reflective learning. However, the findings also reveal significant challenges associated with these transformations. Teachers often face resistance to pedagogical change, lack of institutional support, and disparities in technological access that reinforce educational inequalities. These limitations highlight that the effectiveness of technology in education depends not merely on its availability but on how it is integrated into pedagogical design. The article emphasizes the need for ongoing teacher training and critical reflection to ensure that technology serves as a means to promote equity, creativity, and meaningful learning. Ultimately, it argues that technological mediation should strengthen—not replace—the human dimension of teaching and assessment.
Technological Mediations, Teaching, Assessment, Education, Digital Technologies, Personalized Learning.
Jorge E. Chaparro Medina, Isabel Cristina Rincón Rodríguez, Javier Carreón Guillén, Julio E Crespo, Cruz García Lirios, Albeiro Beltrán Díaz
INVESTIGATING THE IMPACT OF CHANGES IN CONSUMER BEHAVIOR ON SUSTAINABLE GREEN ECONOMY PRACTICES BASED ON THE ROLE OF ELECTRONIC PAYMENT USE
This study was conducted to investigate the impact of changes in consumer behavior on sustainable green economy practices based on the role of electronic payment use. The statistical population included customers using electronic payment of banks in Tehran province. The research questionnaire was distributed and collected among 384 members of the statistical population as a sample. The information collected by the questionnaires was analyzed using SPSS24 and AMOS24 software using structural equation modeling. The results showed that changes in consumer behavior have a positive and significant impact on sustainable green economy practices. Changes in consumer behavior have a positive and significant impact on the use of electronic payment. The use of electronic payment has a positive and significant impact on sustainable green economy practices. The use of electronic payment has a mediating role in the relationship between changes in consumer behavior and sustainable green economy practices. Changes in consumer behavior strengthen sustainable practices in the green economy. Also, the use of electronic payment as a key factor positively and significantly affects this relationship. Finally, electronic payment plays an effective mediating role between consumer behavior and the development of sustainable green economy practices. Investigating the impact of changes in consumer behavior on sustainable green economy practices based on the role of electronic payment usage
Green economy, electronic payment, changes in consumer behavior.
A VERIFIABLE ARCHITECTURE FOR TRUSTWORTHY AI IN AUTOMATED CLINICAL AND HEALTHCARE ADMINISTRATIVE DECISION SYSTEMS
Healthcare systems increasingly deploy artificial intelligence for clinical decision support and administrative automation, yet the opacity of AI models raises critical concerns about patient safety, regulatory compliance, and accountability. This research develops a comprehensive verifiable architecture that ensures trustworthiness, transparency, and auditability in AI-driven healthcare decision systems. Through systematic analysis of regulatory requirements, clinical workflows, and existing AI deployment challenges, we identify fundamental gaps in current approaches that treat AI models as black boxes without verification mechanisms. Our architecture introduces layered verification including pre-deployment validation, runtime monitoring, decision provenance tracking, and continuous compliance checking. The framework implements explainability requirements specific to healthcare contexts, ensuring clinicians understand AI reasoning in terms meaningful for patient care rather than abstract technical metrics. Validation across three healthcare organizations deploying AI for diagnosis support, treatment recommendations, and claims processing demonstrates that our architecture enables 94% decision traceability, reduces unsafe AI recommendations by 78%, and achieves full regulatory compliance verification. The research contributes both theoretical foundations for verifiable healthcare AI and practical implementation patterns enabling safe, trustworthy deployment of AI systems affecting patient health and healthcare operations.
Trustworthy AI, Healthcare AI, Clinical Decision Support, AI Verification, Explainable AI, Medical AI Safety, Healthcare Automation
AI-BASED DISTRIBUTED SYSTEMS OBSERVATION SYSTEM: REAL-TIME MONITORING AND INTELLIGENT ANOMALY DETECTION FOR CLOUD INFRASTRUCTURE
Distributed systems have become the foundation of modern computing infrastructure, powering cloud services, microservices architectures, and large-scale applications serving billions of users worldwide. However, the complexity, scale, and dynamic nature of these systems present significant challenges for effective monitoring, fault detection, and performance optimization. This research presents an AI-based distributed systems observation system that employs machine learning algorithms, anomaly detection techniques, and predictive analytics to provide comprehensive real-time monitoring and intelligent diagnostics. The system integrates data from multiple sources including application logs, system metrics, network traffic, and trace data to build holistic understanding of distributed system behavior. Through implementation of unsupervised learning for anomaly detection, deep learning for pattern recognition, and causal analysis for root cause identification, the system achieved 93.8% accuracy in detecting system anomalies, 89.4% accuracy in predicting performance degradation before user impact, and reduced mean time to resolution by 67% compared to traditional monitoring approaches. Validation across three production distributed systems processing over 50 million transactions daily demonstrated the system's effectiveness in identifying issues ranging from resource exhaustion and network partitions to configuration errors and cascading failures. This research contributes practical methodologies for applying AI technologies to distributed systems management and demonstrates significant improvements in system reliability, operational efficiency, and user experience.
Distributed systems, artificial intelligence, anomaly detection, cloud computing, observability, predictive maintenance, system monitoring.
OPERATIONALIZING INTELLIGENT PLATFORMS AT SCALE: LESSONS FROM SOFTWARE PRODUCT DEVELOPMENT IN U.S. HEALTHCARE SYSTEMS
U.S. healthcare systems increasingly develop intelligent software platforms incorporating AI, machine learning, and advanced analytics to improve clinical outcomes, operational efficiency, and patient experience. However, transitioning these platforms from promising prototypes to scaled operational deployment presents substantial challenges unique to healthcare's complex regulatory, clinical, and organizational environments. This research examines lessons learned from software product development efforts across twelve major U.S. healthcare systems, identifying critical success factors and common failure modes in operationalizing intelligent platforms at scale. Through systematic analysis of 47 platform initiatives spanning clinical decision support, population health management, operational optimization, and patient engagement, we identify recurring patterns around technical architecture, organizational change management, clinical workflow integration, regulatory compliance, and financial sustainability. Our findings reveal that successful scaled deployments share common characteristics including executive sponsorship extending beyond initial funding, deep clinical co-design throughout development, modular architectures enabling incremental deployment, and explicit strategies for managing organizational resistance. Conversely, failed initiatives typically suffered from technology-first approaches disconnected from clinical workflows, underestimated change management requirements, and inability to demonstrate clear value propositions justifying ongoing investment. The research contributes practical frameworks for healthcare IT leaders planning intelligent platform deployments, emphasizing that successful operationalization requires balancing technical excellence with organizational readiness, regulatory compliance, and sustainable business models.
Healthcare IT, Intelligent Platforms, Software Product Development, Clinical Deployment, Change Management, Healthcare AI, Platform Operationalization
VERIFIABLE INTEROPERABILITY ACROSS HEALTH DATA STANDARDS: A PROVENANCE-AWARE FRAMEWORK FOR AUDITABILITY
Healthcare data interoperability remains fragmented across incompatible standards including HL7 FHIR, CDA, v2 messaging, DICOM, and proprietary formats, creating critical gaps in patient care continuity and clinical decision-making. While technical bridges enable syntactic data exchange, semantic fidelity and transformation auditability remain largely unverified, leading to silent data corruption, information loss, and compliance risks. This research develops a provenance-aware framework that ensures verifiable interoperability across health data standards through comprehensive tracking of data transformations, semantic mappings, and quality assertions. Our framework introduces cryptographic attestation of data provenance, enabling stakeholders to verify transformation integrity and trace data lineage across standard conversions. Through systematic analysis of interoperability failures in healthcare exchanges and synthesis of provenance tracking requirements, we design an architecture that captures complete transformation histories including mapping decisions, data quality assessments, and human interventions. Implementation across three health information exchanges processing 2.4 million patient records demonstrates 99.2% transformation verification coverage, detection of 847 previously unidentified data quality issues, and full compliance auditability for regulatory requirements. The research contributes both theoretical foundations for verifiable health data interoperability and practical implementation patterns enabling trustworthy data exchange across healthcare ecosystems.
Health Data Interoperability, Data Provenance, FHIR, HL7, Healthcare Standards, Data Quality, Auditability, Transformation Verification
SYSTEM FOR HYBRID MIDDLEWARE INTEGRATION WITH SERVICE BUS MEDIATION, SECURE API GATEWAY MANAGEMENT, CLOUD AI-ASSISTED OBSERVABILITY, AND JAVA RUNTIME
Modern enterprise architectures increasingly require integration across heterogeneous systems spanning on-premises legacy applications, cloud-native services, and third-party APIs. Traditional middleware solutions struggle to address the complexity of hybrid environments while maintaining security, observability, and performance at scale. This research designs and evaluates a comprehensive hybrid middleware integration system that combines service bus mediation for message routing and transformation, secure API gateway management for controlled access and protocol translation, cloud AI-assisted observability for intelligent monitoring and anomaly detection, and optimized Java runtime for high-performance execution. Through implementation and testing across three enterprise deployment scenarios representing financial services, healthcare, and retail sectors, the system demonstrates 47% reduction in integration development time, 62% improvement in message throughput compared to legacy middleware, and 78% faster anomaly detection through AI-assisted observability. The architecture achieves 99.97% uptime across distributed deployments while maintaining sub-50ms latency for 95th percentile API transactions. Security analysis reveals the gateway management component successfully prevents 99.8% of simulated attack vectors while maintaining seamless user experience. The AI-assisted observability module accurately predicts 84% of system failures 15-45 minutes before occurrence, enabling proactive intervention. This research contributes a validated architectural framework for hybrid middleware integration addressing contemporary enterprise needs for scalability, security, observability, and performance, with practical implementation guidance for organizations navigating digital transformation challenges.
Hybrid middleware, enterprise integration, service bus, API gateway, cloud observability, artificial intelligence, Java runtime, microservices architecture, system security, distributed systems
A NOVEL SWARM INTELLIGENCE-BASED COMPRESSIVE SLEEPING APPROACH TO IMPROVE THE LIFETIME OF WIRELESS SENSOR NETWORKS
Wireless Sensor Networks face serious energy limitations that directly constrain their operational lifetime. To address this, we propose a hybrid optimization framework that integrates compressive sensing, genetic algorithm, and ant colony optimization to decrease energy consumption while preserving data accuracy. The proposed method considers both spatial and temporal correlations but unlike existing approaches that rely on random node activation or single-hop routing, our method centrally selects active nodes using genetic algorithm and constructs a multi-hop data aggregation tree using ant colony optimization for energy-efficient transmission. This design offers a significant improvement over compared methods by jointly optimizing signal reconstruction and routing. Simulation results demonstrate that our method reduces signal reconstruction error by over 48% and extends network lifetime by more than 18% compared to leading baselines.
Accuracy, Compressive Sensing, Data Aggregation, Energy Consumption, Lifetime, Sleep/Wake Algorithm, Wireless Sensor Network.
Mohammad Hassan Katebi Jahromi, Karamollah Bagherifard, Razieh MalekHoseini, Saeed Mehrjoo
ARTIFICIAL INTELLIGENCE USE AND INTELLECTUAL CAPITAL FORMATION: AN EMPIRICAL STRUCTURAL EQUATION MODELING APPROACH
This study examines the determinants of artificial intelligence (AI) use and its role in the formation of intellectual capital through the empirical testing of a structural equation model. The research adopts a quantitative, explanatory, and cross-sectional design based on data collected from university students and academic staff exposed to AI-based educational and knowledge management systems. The proposed model integrates institutional and individual determinants—governance quality, institutional support, digital skills, trust in AI, and user satisfaction—and analyzes their effects on AI use as a mediating variable influencing intellectual capital formation. The results demonstrate that AI use plays a central mediating role, translating favorable governance structures, user competencies, and experiential conditions into enhanced human and structural capital. Digital skills and user satisfaction emerge as the strongest predictors of AI use, while governance and institutional support provide essential enabling conditions. Overall, the findings highlight that the contribution of AI to intellectual capital is not automatic but contingent upon coherent socio-technical and governance arrangements. The study provides empirical evidence to support strategic decision-making in educational and organizational contexts seeking to leverage AI for sustainable knowledge development.
Artificial intelligence; Intellectual capital; Structural equation modeling; AI use; Governance; Digital skills; Higher education.
Sonia Sujell Velez Baez, Agustin Maya Miranda, Joel Martínez Bello, Adriana Vanessa Blaness Ugarte, José Alfonso Aguilar Fuentes, Wilfrido Isidro Aldana Balderas, Felipe de Jesús Vilchis Mora, Josefina Haydee Gutiérrez Hernández, Reyna Amador Velázquez
CONTEXT-AWARE ANOMALY DETECTION IN HEALTHCARE CLAIMS USING MULTI-STAGE DECISION PIPELINES
Healthcare fraud, waste, and abuse represent a critical challenge costing the U.S. healthcare system an estimated $68-230 billion annually, with fraudulent claims comprising 3-10% of total healthcare expenditures. Traditional rule-based fraud detection systems generate excessive false positives by ignoring clinical context, patient histories, and temporal claim patterns, creating operational burdens for legitimate providers while missing sophisticated fraud schemes. This research develops a context-aware anomaly detection framework employing multi-stage decision pipelines that integrate clinical knowledge, temporal sequence analysis, and provider behavior profiling to identify fraudulent healthcare claims with high precision. The proposed system implements a four-stage architecture: (1) Clinical Context Validation assessing medical appropriateness using diagnosis-procedure compatibility matrices and clinical guidelines; (2) Temporal Pattern Analysis detecting unusual claim sequences through Hidden Markov Models and time-series anomaly detection; (3) Provider Behavior Profiling identifying outlier billing patterns using Isolation Forest and statistical process control; and (4) Ensemble Decision Fusion combining stage outputs through weighted voting and confidence scoring. Evaluation on a dataset of 2.4 million Medicare claims (2020-2023) containing 87,420 confirmed fraud cases demonstrates 94.6% detection accuracy with 3.2% false positive rate, substantially outperforming traditional approaches (rule-based: 78.3% accuracy, 18.7% FPR; standard ML: 88.4% accuracy, 8.1% FPR). The multi-stage pipeline reduces false positives by 61% compared to single-stage models while maintaining 96.8% recall for confirmed fraud. Average case processing time of 2.3 seconds enables real-time claim adjudication. Implementation across three healthcare payers resulted in $127 million in prevented fraudulent payments over 18 months with 68% reduction in provider audit burden. This research contributes novel integration of clinical domain knowledge with machine learning, temporal sequence analysis techniques tailored for healthcare claims, and practical multi-stage architecture enabling graduated responses based on fraud likelihood.
Healthcare Fraud Detection, Anomaly Detection, Clinical Context, Claims Analytics, Multi-Stage Pipeline, Temporal Analysis, Machine Learning, Medicare Fraud, Billing Abuse.
SYSTEM FOR MIDDLEWARE INTEGRATION AND SERVICE BUS ORCHESTRATION WITH API GATEWAY SECURITY, CLOUD AI-BASED MONITORING, AND JAVA-BASED EXECUTION
Enterprise application integration (EAI) has become increasingly complex with the proliferation of microservices architectures, cloud-native applications, and distributed systems requiring seamless communication across heterogeneous platforms. This research presents a comprehensive middleware integration system that combines service bus orchestration, API gateway security, cloud-based AI monitoring, and Java-based execution to address modern enterprise integration challenges. The proposed architecture implements an Enterprise Service Bus (ESB) pattern enhanced with intelligent routing, real-time monitoring, and adaptive security mechanisms. Our system employs a multi-layered approach: (1) API Gateway layer providing authentication, authorization, rate limiting, and threat detection; (2) Service Bus orchestration layer managing message routing, transformation, and protocol mediation; (3) AI-based monitoring layer utilizing machine learning algorithms for anomaly detection, performance optimization, and predictive maintenance; and (4) Java-based execution engine ensuring platform independence and robust transaction management. Implementation across three enterprise case studies—financial services, healthcare, and e-commerce—demonstrates 43% reduction in integration latency, 67% improvement in fault detection time, and 82% decrease in security incidents compared to traditional integration approaches. The AI monitoring component achieves 94% accuracy in predicting service failures 15 minutes before occurrence, enabling proactive intervention. Performance benchmarks show the system handling 50,000 transactions per second with 99.97% availability. This research contributes both architectural patterns for modern middleware integration and practical implementation guidance for enterprises navigating digital transformation initiatives requiring robust, secure, and intelligent integration infrastructure.
Middleware Integration, Enterprise Service Bus, API Gateway, Cloud Computing, Artificial Intelligence, Microservices, Java, Service Orchestration, Security, Monitoring.