International Journal of AI, Engineering and Management Studies (IJAIEMS)
International Journal of AI, Engineering and Management Studies (IJAIEMS) is a peer-reviewed, open-access scholarly journal dedicated to publishing high-quality research in the interdisciplinary fields of Artificial Intelligence, Engineering, and Management. The journal aims to provide a global platform for researchers, academicians, industry experts, and practitioners to share innovative ideas, research findings, and practical insights that contribute to technological advancement and organizational development.
Essay Publication Research and Consultancy
Ethical Deployment of Large Language Models in Higher Education
Ethical deployment of Large Language Models (LLMs) in higher education refers to the responsible design, implementation, and governance of AI systems used for teaching, learning, assessment, and academic support. As universities increasingly integrate LLMs into educational workflows, concerns around bias, academic integrity, data privacy, transparency, and accountability have become central. Ethical deployment requires aligning AI outputs with curriculum standards while ensuring fairness, explainability, and human oversight. By integrating Explainable Artificial Intelligence (XAI), Retrieval-Augmented Generation (RAG), curriculum-aware AI, Human-in-the-Loop (HITL) validation, and governance frameworks, institutions can mitigate risks while maximizing educational benefits. This study explores ethical deployment of LLMs in higher education through a qualitative literature review. Findings indicate that ethical frameworks improve trust, academic integrity, learning outcomes, and institutional accountability. Despite challenges such as regulatory uncertainty, model bias, and infrastructure limitations, ethical deployment remains essential for sustainable AI integration in universities.
Large Language Models, higher education, AI ethics, curriculum-aware AI, academic integrity, Explainable AI, governance frameworks
Benjamin Sterling, Victoria Hayes, Samuel Redford
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0
AI Supported Learning Analytics for Student Performance Prediction
Artificial Intelligence (AI)-supported learning analytics has emerged as a transformative approach for predicting student performance and improving educational decision-making in higher education. By combining learning analytics with advanced Artificial Intelligence techniques such as Large Language Models (LLMs), machine learning, and predictive modeling, educational institutions can identify learning patterns, forecast academic outcomes, and provide timely interventions for students at risk of poor performance. Traditional learning analytics systems often rely on descriptive statistics and historical academic records, limiting their ability to deliver personalized and real-time educational support. AI-supported learning analytics overcomes these limitations by integrating curriculum-aware artificial intelligence, Retrieval-Augmented Generation (RAG), Explainable Artificial Intelligence (XAI), Human-in-the-Loop (HITL) verification, and institutional governance frameworks to improve prediction accuracy and educational transparency. This study investigates AI-supported learning analytics for student performance prediction through a qualitative review of recent scholarly literature. The findings indicate that AI-enhanced learning analytics significantly improve prediction accuracy, learner engagement, personalized learning, curriculum alignment, and institutional decision-making while reducing dropout risks and improving academic success. Although implementation requires technological infrastructure, continuous curriculum updates, data governance, and faculty development, AI-supported learning analytics provides a sustainable framework for intelligent, ethical, and learner-centered educational environments.
Learning analytics, student performance prediction, Artificial Intelligence, curriculum-aware artificial intelligence, Explainable Artificial Intelligence, Retrieval-Augmented Generation, higher education
Lucas Fairchild, Grace Holloway, Henry Blackwood
The Global Impact of Foreign Currency Borrowing on Corporate Valuation: Evidence From Emerging Markets
This paper analyses the effects of foreign currency borrowing on corporate valuation in the emerging markets based on the panel data and econometric models. The value is determined through the Tobin Q and market capitalization, in which foreign currency debt was the most important independent variable. They can be controlled variables, which are exchange rate volatility, interest rate differentials, firm size, profitability, leverage, and GDP growth, as well as inflation. Findings indicate that although foreign debt has the potential to improve valuation by providing accessible and inexpensive financing as well as access to international markets, currency mismatches and currency volatility risks might negatively affect firm value. One of the most effective ways to maximize the benefits and reduce foreign currency borrowing risks is to have good hedging, profitability, and regulatory support.
Foreign Currency Borrowing, Corporate Valuation, Exchange Rate Volatility, Emerging Markets, Financial Risk Management
Dr. Sumaira Gulzar, Dr. Ajaz Ahmad Mistree, Asif Nabi, Irfan Subhan Wani
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc/4.0
Intelligent Academic Support Systems Based on Retrieval-Augmented Generation
Intelligent academic support systems based on Retrieval-Augmented Generation (RAG) represent a significant advancement in educational artificial intelligence by combining the reasoning capabilities of Large Language Models (LLMs) with external knowledge retrieval mechanisms. Traditional academic support tools often rely solely on pre-trained language models, which may produce hallucinations, outdated explanations, or curriculum-inconsistent responses. RAG-based systems address these limitations by retrieving relevant and verified academic content from curriculum repositories, institutional databases, digital libraries, and structured knowledge graphs before generating responses. When integrated with curriculum-aware artificial intelligence, Explainable Artificial Intelligence (XAI), Human-in-the-Loop (HITL) validation, and educational governance frameworks, RAG-based academic support systems significantly enhance instructional accuracy, learner engagement, personalization, and decision-making quality. This study explores intelligent academic support systems based on RAG through a qualitative review of recent scholarly literature. Findings indicate that RAG-driven systems improve academic performance, curriculum alignment, transparency, and learner trust while reducing misinformation and hallucinations. Despite challenges such as infrastructure requirements, knowledge base maintenance, and governance complexity, RAG-based academic support systems provide a scalable and reliable foundation for modern educational environments.
Retrieval-Augmented Generation, academic support systems, educational AI, curriculum-aware AI, knowledge graphs, Explainable AI, personalized learning
Julian Hartley, Amelia Brooks, Oliver Kensington
Trustworthy Retrieval Augmented Learning Systems
Trustworthy Retrieval-Augmented Learning Systems (RALS) have emerged as an important innovation in educational artificial intelligence by combining the reasoning capabilities of Large Language Models (LLMs) with verified knowledge retrieval to deliver accurate, transparent, and curriculum-aligned learning experiences. Traditional language models often generate hallucinated or outdated information because they rely primarily on pre-trained parameters rather than current institutional knowledge. Retrieval-Augmented Generation (RAG) addresses these limitations by retrieving information from trusted educational repositories before producing responses. When integrated with curriculum-aware artificial intelligence, explainable artificial intelligence (XAI), human-in-the-loop validation, and ethical governance, retrieval-augmented learning systems become more reliable, accountable, and suitable for higher education. This study investigates trustworthy retrieval-augmented learning systems through a qualitative review of recent scholarly literature. The findings indicate that these systems significantly improve instructional accuracy, learner engagement, curriculum alignment, and academic decision support while reducing misinformation and hallucinations. Furthermore, explainability, transparency, and institutional governance strengthen learner confidence and educational accountability. Although implementation requires continuous knowledge management, technical infrastructure, and faculty participation, trustworthy retrieval-augmented learning systems provide a sustainable foundation for responsible artificial intelligence in modern higher education.
Retrieval-Augmented Generation, trustworthy artificial intelligence, curriculum-aware artificial intelligence, intelligent tutoring systems, explainable artificial intelligence, educational technology, higher education
Jonathan Mercer, Emily Ashbourne, Christopher Bennett
Labor Market Segmentation and Employment Inequality in the Era of Digital Transformation
The rapid expansion of digital technologies has fundamentally transformed labor markets across the world, creating new opportunities for productivity growth, innovation, and economic development. At the same time, digital transformation has intensified existing forms of labor market segmentation and contributed to widening employment inequalities among different groups of workers. The integration of automation, artificial intelligence, digital platforms, and information technologies has altered the nature of work, reshaped occupational structures, and changed the demand for skills. While highly skilled workers often benefit from increased access to quality employment, higher wages, and greater career mobility, low-skilled workers frequently face job displacement, precarious employment conditions, and limited opportunities for advancement. These developments have generated concerns regarding the equitable distribution of economic gains associated with technological progress. The analysis demonstrates that digital transformation simultaneously creates opportunities and risks. While digitalization contributes to productivity enhancement and the creation of new occupations, it also reinforces structural inequalities by concentrating benefits among workers with advanced skills and digital competencies. The findings highlight the importance of inclusive labor market policies, continuous workforce training, digital literacy development, and social protection mechanisms in addressing emerging forms of employment inequality. The article concludes that achieving equitable labor market outcomes in the digital age requires coordinated efforts from governments, employers, educational institutions, and other stakeholders to ensure that technological progress promotes inclusive and sustainable economic development.
Labor Market Segmentation, Employment Inequality, Digital Transformation, Digital Economy, Automation, Artificial Intelligence, Gig Economy, Wage Inequality, Skill Polarization, Workforce Development, Employment Opportunities, Digitalization
The Impact of Digital Economy Development on Sustainable Urban and Rural Growth in China
The rapid expansion of the digital economy has emerged as a transformative force shaping economic growth, social development, and environmental sustainability across China. As digital technologies such as artificial intelligence, big data, cloud computing, e-commerce, fintech, and digital platforms become increasingly integrated into economic activities, they are creating new opportunities for balanced urban and rural development. This article examines the impact of digital economy development on sustainable urban and rural growth in China, focusing on its contributions to economic productivity, employment creation, industrial upgrading, social inclusion, environmental governance, and regional development. The study explores how digital infrastructure and technological innovation have enhanced resource allocation, improved public service delivery, promoted rural revitalization, and strengthened connections between urban and rural economies. At the same time, it highlights challenges associated with digital inequality, labor market polarization, skill disparities, and uneven access to digital resources that may hinder inclusive and sustainable growth. The analysis reveals that the digital economy serves as a critical driver of modernization by fostering innovation, increasing efficiency, and supporting environmentally responsible development practices. Furthermore, the integration of digital technologies into agriculture, manufacturing, education, and public administration has accelerated economic transformation while improving the quality of life for both urban and rural populations. The findings suggest that sustained investments in digital infrastructure, human capital development, digital literacy, and inclusive policy frameworks are essential for maximizing the benefits of digitalization and ensuring balanced regional development. The article concludes that the digital economy has significant potential to support China's long-term sustainability objectives by promoting coordinated urban-rural development, enhancing economic resilience, and facilitating inclusive growth in the digital era
Digital economy, sustainable development, urban growth, rural revitalization, China, digital transformation, economic development, digital infrastructure, regional development, inclusive growth.
AI-Driven Big Data Policy Systems and Agentic Governance: Evidence from Regional Economic Development in China
This study examines how AI-driven big data policy systems and agentic governance frameworks influence regional economic development in China. It explores how data governance, digital infrastructure, and AI agent architectures collectively shape productivity, innovation, and spatial economic disparities. Positioning big data policy as part of China’s digital transformation strategy, the study evaluates its role in industrial upgrading, resource allocation efficiency, and the development of regional innovation ecosystems. It further considers how AI governance mechanisms and agent-based decision systems improve policy execution, transparency, and trust in digital public infrastructure. At the same time, the analysis addresses structural challenges such as uneven technological adoption, labor market segmentation, and regional digital inequality. By integrating perspectives from the digital economy, AI agent architectures, and trustworthy AI governance, the study provides a unified framework for understanding how intelligent policy systems reshape regional development in China
Big data policy; regional economic development; digital economy; China; data governance; industrial upgrading; economic disparity; digital transformation
Artificial Intelligence Adoption in Higher Education: Opportunities, Risks, and Student Outcomes
Artificial Intelligence (AI) is rapidly transforming higher education by reshaping teaching, learning, assessment, and institutional management practices. The increasing adoption of AI-driven technologies offers significant opportunities to enhance educational accessibility, personalize learning experiences, improve administrative efficiency, and support data-informed decision-making. AI-powered tools can assist students in developing critical skills, accessing educational resources, and receiving tailored academic support that aligns with their individual learning needs. At the same time, the widespread integration of AI in higher education raises important concerns related to academic integrity, data privacy, algorithmic bias, ethical governance, and the potential overreliance on automated systems. These challenges require institutions to establish appropriate policies and frameworks that ensure the responsible and equitable use of AI technologies. Furthermore, AI adoption has significant implications for student outcomes, influencing academic performance, engagement, learning satisfaction, digital literacy, and future employability. This article examines the opportunities, risks, and student outcomes associated with AI adoption in higher education. It explores how educational institutions can maximize the benefits of AI while addressing emerging challenges to create inclusive, effective, and sustainable learning environments. The discussion highlights the importance of balancing technological innovation with ethical considerations to ensure that AI contributes positively to the future of higher education.
Artificial Intelligence, Higher Education, Educational Technology, Student Outcomes, Personalized Learning, Academic Integrity, Digital Transformation, AI Adoption, Learning Analytics, Educational Innovation.
AI-Powered Digital Transformation in Next-Generation Banking Systems
The rapid evolution of financial technologies has significantly transformed traditional banking systems into intelligent, data-driven ecosystems. This research explores the role of Artificial Intelligence (AI) in enabling digital transformation within next-generation banking systems. The study focuses on how AI technologies such as machine learning, natural language processing, predictive analytics, and intelligent automation enhance operational efficiency, customer experience, fraud detection, and risk management in modern banking environments. The objective of this paper is to analyze the integration of AI within digital banking infrastructures and to propose a conceptual framework that supports scalable, secure, and real-time financial services. A qualitative research approach is adopted, supported by secondary data from recent literature, industry reports, and case studies of leading financial institutions adopting AI-driven solutions. The findings indicate that AI-powered banking systems significantly improve decision-making accuracy, reduce operational costs, and enhance personalized financial services through real-time data analytics. Additionally, AI strengthens cybersecurity mechanisms by detecting anomalous transactions and preventing fraudulent activities more effectively than traditional rule-based systems. However, challenges such as data privacy concerns, algorithmic bias, regulatory compliance, and high implementation costs remain critical barriers to widespread adoption. The study concludes that AI is a fundamental enabler of next-generation banking transformation, offering scalable solutions for intelligent automation and customer-centric services. Future developments are expected to integrate AI with blockchain and cloud computing to create more secure, transparent, and autonomous banking ecosystems.
AI in Banking, Digital Transformation, FinTech, Machine Learning, Blockchain, Risk Management, Intelligent Banking Systems
Zero Trust Security Model for API-Driven Financial Microservices
Financial microservices architecture has become a core component of modern digital banking and fintech systems due to its scalability, flexibility, and ability to support API-driven services. APIs enable seamless communication between distributed financial services such as payments, authentication, fraud detection, and account management. However, this API-centric environment introduces significant security challenges, including unauthorized access, token misuse, data leakage, and increased attack surfaces across microservices. To address these issues, this study proposes a Zero Trust Security Model (ZTSM) for API-driven financial microservices. The model follows a “never trust, always verify” approach, where every API request is continuously authenticated, authorized, and validated regardless of its origin. It integrates identity-based access control, multi-factor authentication, API gateway enforcement, and continuous monitoring to strengthen security across service interactions. The proposed approach enhances security by ensuring strict verification at every communication layer, thereby reducing the risk of internal and external attacks. Key benefits include improved access control, minimized unauthorized API usage, enhanced visibility of system activity, and strengthened protection of sensitive financial data. Overall, the Zero Trust-based approach significantly improves the resilience of financial microservices against modern cyber threats and provides a robust framework for secure API-driven financial ecosystems.
Zero Trust, Microservices, API Security, Financial Systems, Authentication, Cybersecurity
National Economic Impact Measurement of Cybersecurity Failures in U.S. Financial Technology Platforms
The rapid proliferation of Financial Technology (FinTech) platforms in the United States has fundamentally reshaped domestic financial services, offering unprecedented efficiency and accessibility. However, this digital transformation has concurrently expanded the national cyber threat surface, making systemic cybersecurity failures a critical source of macroeconomic vulnerability. This paper addresses a significant gap in existing literature: the absence of a standardized, scalable framework for measuring the national economic consequences of cyber failures specifically within the interconnected FinTech ecosystem. While micro-level breach costs (e.g., remediation, fines) are well-documented, the cascading, economy-wide effects-including liquidity freezes, consumer confidence shocks, and cross-platform contagion-remain largely unquantified. This study proposes a hybrid measurement method integrating econometric input-output modeling with agent-based simulation of the U.S. payment and lending infrastructure. Using publicly reported cybersecurity incidents from 2019–2024 across major U.S. FinTech subsectors (digital wallets, neobanks, and alternative lending platforms), we construct a national “cyber-failure shock” variable. Our model quantifies direct losses (theft, system downtime, regulatory penalties) and, crucially, indirect losses: supply chain disruptions to traditional banks, velocity of money reductions, and depreciation of digital transactional trust. Preliminary findings suggest that a severe, multi-day operational failure affecting just three dominant paymentsFinTechs could generate indirect national losses exceeding direct costs by a factor of 4:1, primarily through halted small business cash flows and transient liquidity hoarding. Furthermore, we name a non-linear relationship between failure frequency, and long-term productivity-persistent but minor failures impose hidden drag via increased compliance friction and risk-premium inflation on FinTech equities. The study also critiques current disclosure regimes (e.g., SEC 8-K filings) for lacking granularity necessary for macroeconomic time-series analysis. The paper concludes by proposing a National Cyber-Economic Impact Index (NCEI) for FinTech platforms, recommending mandatory reporting of operational duration and transaction volume loss. Finally, we discuss policy implications for the Financial Stability Oversight Council (FSOC), emphasizing that cybersecurity failures should be recategorized not merely as technical or firm-specific risks, but as measurable systemic risks to national economic output.
Artificial Intelligence–Driven Financial Risk Assessment and Investment Decision Support Systems in Modern Banking
Artificial Intelligence (AI) has emerged as a revolutionary technology in the banking and financial services sector, enabling organizations to enhance operational efficiency, risk management, and investment decision-making processes. Modern financial institutions increasingly utilize AI-driven techniques such as machine learning, predictive analytics, and data mining to assess financial risks, detect fraudulent activities, evaluate customer creditworthiness, and optimize investment strategies. This study explores the applications of AI in financial risk assessment and investment decision support systems within the banking industry. The research examines how AI-based models improve the accuracy and speed of financial analysis by processing large volumes of structured and unstructured data. Furthermore, the study discusses the benefits of AI adoption, including enhanced decision-making, reduced operational risks, improved customer services, and cost efficiency. It also highlights key challenges such as data privacy concerns, algorithmic bias, regulatory compliance, and implementation costs. The findings indicate that AI-driven systems significantly contribute to more reliable risk assessment and informed investment decisions, thereby strengthening the competitiveness and sustainability of modern banks. The study concludes that the continued integration of AI technologies will play a crucial role in shaping the future of intelligent banking and financial management
A Risk-Driven Framework for Integrating Risk Management into the Software Development Lifecycle
Software systems are increasingly complex, distributed, and exposed to evolving security, operational, and requirement-related uncertainties. Traditional software development methodologies often treat risk management as a supplementary activity rather than an integrated process. This study proposes a risk-driven framework that embeds risk management directly into the software development lifecycle to improve system reliability, quality assurance, and decision-making efficiency. The framework integrates continuous risk identification, assessment, prioritization, and mitigation across all phases of development, from requirements engineering to maintenance. Building on established work by Hijazi et al. (2014), who emphasized structured integration of risk management into software processes, this research extends the concept by incorporating adaptive risk evaluation mechanisms aligned with modern iterative development models. The study demonstrates that embedding risk-aware practices enhances early defect detection, reduces project failure probability, and improves overall software resilience.
Artificial Intelligence and Semantic Knowledge Representation for Optimized Web Service Registry Systems
The rapid expansion of distributed computing and service-oriented architectures has intensified the need for efficient web service discovery and selection mechanisms. Traditional keyword-based service registries often suffer from ambiguity, low precision, and poor scalability in dynamic environments. This paper presents an integrated approach combining Artificial Intelligence techniques with Semantic Knowledge Representation to optimize web service registry filtering systems. By leveraging ontology-driven frameworks and machine learning-enhanced semantic reasoning, the proposed model improves service matching accuracy, reduces search complexity, and enhances adaptability in heterogeneous service environments. The study builds upon foundational work in semantic-based service filtering mechanisms and extends it by incorporating intelligent decision-making capabilities. Experimental analysis demonstrates that the hybrid approach significantly improves service retrieval performance compared to conventional registry systems. The findings highlight the potential of AI-enabled semantic frameworks in advancing intelligent service-oriented computing.
Explainable and Trustworthy Artificial Intelligence in Endodontics: Combining Clinical Text Analysis, Machine Learning, and CBCT Imaging
Artificial intelligence (AI) is rapidly transforming healthcare by improving diagnostic accuracy, treatment planning, and clinical decision-making. In endodontics, the integration of explainable and trustworthy AI systems with cone-beam computed tomography (CBCT) imaging and clinical text analysis offers significant potential for enhancing dental diagnosis and patient care. This study explores the development of an explainable AI framework that combines machine learning algorithms, natural language processing, and CBCT imaging for intelligent endodontic analysis. The framework utilizes clinical text records, radiographic imaging, and AI-driven classification techniques to improve lesion detection, root canal assessment, and treatment planning accuracy. Explainable AI mechanisms are incorporated to ensure transparency, interpretability, and clinical trustworthiness in diagnostic predictions. Experimental analysis demonstrates improved diagnostic precision, enhanced image interpretation, and more reliable treatment recommendations compared to traditional diagnostic approaches. The integration of CBCT imaging with machine learning-assisted clinical text analysis also improves decision support and reduces diagnostic uncertainty. Furthermore, the framework supports safer deployment in clinical environments through validation-driven AI methodologies. The findings demonstrate that explainable and trustworthy AI systems can significantly improve endodontic diagnostics, workflow efficiency, and intelligent dental healthcare delivery.
Fei-Fei Li, Cynthia Rudin, Hugh Durrant-Whyte
Design and Optimization of Logarithmic Slot Antennas for High-Frequency Medical Imaging and Wireless Healthcare Systems: A Test-Driven AI Framework for Performance Enhancement and Reliable Deployment
The advancement of biomedical imaging and wireless healthcare technologies has created a growing demand for compact, efficient, and high-gain antenna systems capable of operating effectively in complex biological environments. This study presents the design and optimization of substrate integrated waveguide (SIW)-based logarithmic slot antennas for high-frequency medical imaging and intelligent healthcare systems. The proposed antenna utilizes logarithmic slot geometry to achieve broadband operation, enhanced impedance matching, stable radiation characteristics, and improved signal transmission across microwave and millimeter-wave frequency bands. Electromagnetic simulations and experimental evaluations demonstrate superior gain, reduced signal attenuation, enhanced bandwidth, and improved imaging resolution compared to conventional antenna structures. The research also investigates the compatibility of the antenna with wearable and implantable healthcare devices used in modern smart healthcare systems. In addition, a test-driven artificial intelligence framework is incorporated into the optimization process to improve design validation, parameter tuning, and deployment reliability. Machine learning-assisted optimization significantly reduces design iterations while improving consistency between simulated and experimental results. Furthermore, the study highlights the relevance of advanced medical imaging technologies such as EEG-based image classification and cone-beam computed tomography in supporting intelligent healthcare systems. The results confirm that SIW-based logarithmic slot antennas provide an effective solution for next-generation biomedical imaging and wireless healthcare communication systems.
Artificial Intelligence-Based Clinical Decision Support Systems in Endodontics: Integrating Machine Learning Signal Processing, EEG-Based Pattern Recognition, and CBCT Imaging Analysis.
Clinical Decision Support Systems based on artificial intelligence have become revolutionary tools in contemporary endodontics, allowing increased diagnostic accuracy, predictive analytics, and individualized treatment planning. This paper discusses the combination of machine learning signal processing, EEG-based pattern recognition, and cone-beam computed tomography imaging in a single AI-driven clinical decision-making framework in endodontic practice. The study develops a multi-modal framework that utilizes deep learning algorithms to understand complex imaging data and signal patterns to enhance the accuracy of diagnoses and treatment outcomes. Evaluation of the system is based on various performance metrics such as accuracy, precision, recall, and F1-score, and it is shown that the system performs significantly better than traditional diagnostic processes. The authors highlight the potential of integrating neuro-signal analysis with high-quality imaging to develop intelligent, adaptive, and reliable clinical support systems. This paper contributes to the literature on AI-focused healthcare by offering a scalable and interpretable model to improve clinical procedures, reduce diagnostic errors, and promote evidence-based decision-making in endodontics
Rajesh P. N. Rao, Niels Birbaumer, Dimitrios K. Iakovidis
AI-Driven Predictive Analytics Framework for Real-Time Decision Support Systems
A real-time decision-making process has turned out to be a pressing need of contemporary sectors in the healthcare, finance, manufacturing, and smarter cities. The current paper introduces an AI-based predictive analytics model that will be used to process large amounts of data in real-time, produce insights that can be acted upon, and help make immediate decisions. The framework unites machine learning, deep learning, streaming architectures, feature engineering pipeline, and model optimization plans to increase the accuracy of prediction, response time, and reliability. The paper examines the current literature and different research gaps and offers a scalable multi-layer architecture based on data ingestion, real-time analytics, and adaptive learning. The experimental tests show that the framework is able to generate the result with high accuracy at the same time has low latency when the workload is adjusted. Its practical constraints are reliance on high quality continuous streams of data, computationally expensive hardware requirements and model drift due to changes in dynamic environments. The future trends are to combine self-learning hybrid AI models, create lightweight edge minimized models, and increase the interpretability to address high-risk areas of application.
Federated Learning for Cross-Institutional Fraud Monitoring in National Healthcare Security
Healthcare fraud poses a significant threat to national healthcare security, leading to substantial financial losses, reduced service quality, and compromised patient trust. Traditional centralized fraud detection systems require aggregating sensitive patient and institutional data into a single repository, raising serious concerns regarding privacy, data ownership, and regulatory compliance. To address these challenges, this paper proposes a Federated Learning (FL)-based framework for cross-institutional fraud monitoring in national healthcare systems. The proposed approach enables multiple healthcare institutions, including hospitals, insurance providers, and regulatory bodies-to collaboratively train machine learning models without sharing raw data. Instead, locally trained model updates are securely aggregated to produce a global fraud detection model, preserving data privacy while leveraging diverse and distributed datasets. The framework integrates secure aggregation protocols, differential privacy mechanisms, and anomaly detection algorithms tailored to healthcare fraud patterns such as billing irregularities, duplicate claims, and abnormal treatment procedures. Experimental evaluations using simulated multi-institutional healthcare datasets demonstrate that the federated model achieves detection performance comparable to centralized approaches while significantly reducing privacy risks and regulatory barriers. Furthermore, the system enhances robustness against data heterogeneity and institutional bias through adaptive model weighting and continuous learning mechanisms. This research highlights the potential of federated learning as a scalable, privacy-preserving solution for national-level healthcare fraud monitoring. By enabling secure cross-institutional collaboration, the proposed framework strengthens healthcare security infrastructure, promotes trust among stakeholders, and supports data-driven policy enforcement without compromising sensitive patient information
The Economics Of Global Renewable Energy Supply Chains: Opportunities And Vulnerabilities For The U.S.
The global transition toward renewable energy represents one of the most significant economic transformations of the 21st century, reshaping international trade patterns and geopolitical relationships. As nations accelerate decarbonization efforts to meet Paris Agreement targets, renewable energy has evolved into a complex, globally interconnected supply chain system spanning raw material extraction, component manufacturing, and technology deployment across continents (International Energy Agency [IEA], 2023). This research examines the economic dimensions of global renewable energy supply chains, focusing on opportunities and vulnerabilities facing the United States in maintaining competitiveness while ensuring energy security. The globalization of clean energy supply chains has generated substantial economic benefits, including dramatic cost reductions in solar photovoltaic (PV) modules, wind turbines, and battery storage systems through economies of scale and international competition (Helveston et al., 2022). Solar PV costs have declined by approximately 90% since 2010, while lithium-ion battery costs have fallen by 89%, making renewable energy cost-competitive with fossil fuels in most markets (Bloomberg New Energy Finance [BNEF], 2023). These reductions result from geographically dispersed supply chains leveraging comparative advantages across nations (Gereffi & Fernandez-Stark, 2016). For the United States, global renewable energy supply chains present significant opportunities. Continued cost reduction through global sourcing enables faster domestic renewable energy deployment, supporting climate objectives (Jenkins et al., 2021). American firms maintain competitive advantages in high-value segments including research and development, advanced materials, software systems, and project financing (Varas et al., 2021). Growing global demand creates export opportunities for U.S. companies in specialized components and services, potentially supporting manufacturing employment and economic growth (U.S. Department of Energy [DOE], 2022). This research analyzes the economic structure of global renewable energy supply chains, evaluates U.S. competitive positioning opportunities, assesses vulnerabilities from geographic concentration, and examines policy options for balancing efficiency, resilience, and strategic interests.
Mohammad Rabidul Islam, Istiaque Ahmed Badhan, Md Hafizur Rahman, Md Nurul Hasnain, Irfan chowdhury
Public Policy Interventions and MSME Growth in Post-Pandemic India
Micro, small, and medium enterprises (MSMEs) continue to be at the core of the Indian employment base, local manufacturing capacity, competitiveness in export, and social mobility, but the pandemic has revealed the structural vulnerabilities of this sector in a particularly strange way. This paper builds a policy analysis of the role played by the post-pandemic public policy structure in India in influencing MSME recovery, adaptation, and growth. Based on the international literature on crisis, Indian policy documents, and recent evidence on digital finance, formalization, resilience, and digital transformation, the paper is based on six domains of intervention, including emergency credit support, formalization and registration reform, digital public infrastructure, market access and procurement, capability building and cluster support, and inclusion-oriented entrepreneurship policy. It is argued in the analysis that India has responded most to the policy where it was able to integrate speed, scale, and administrative simplicity, particularly in collateral-free credit guarantees, digital registration, and digital commerce infrastructure. The effects of the growth of these interventions are, however, skewed due to the fact that survival support has in many cases been much faster than productivity support, and formal enrollment has led to greater advancement than the enrichment of deeper capabilities. The paper suggests a comprehensive framework of the following steps of MSME policy based on finance, formalization, digital adoption, market access, resilience, and institutional coordination. It concludes that the future post-pandemic development of MSME in India will not rely as much on relief as on the ability of public policy to transform recovery into productivity, competitiveness, and inclusive enterprise development.
A Comprehensive Study of Emerging Cybersecurity Threats and Advanced Defense Mechanisms
In the rapidly evolving digital landscape, cybersecurity has become a critical concern for individuals, organizations, and governments due to the increasing frequency and sophistication of cyber threats. This study aims to provide a comprehensive analysis of modern cybersecurity threats and the corresponding defense mechanisms employed to mitigate these risks. The research adopts a qualitative approach based on an extensive review of existing literature, industry reports, and documented case studies to identify prevalent attack vectors and evaluate current security practices. The findings reveal that malware, phishing attacks, ransomware, network intrusions, and advanced persistent threats (APTs) remain among the most significant cybersecurity challenges. Additionally, human factors such as lack of awareness and poor security practices continue to contribute to system vulnerabilities. The study further highlights that effective defense strategies include the implementation of multi-layered security frameworks, intrusion detection systems, encryption techniques, and the integration of emerging technologies such as artificial intelligence and machine learning for proactive threat detection. In conclusion, the research emphasizes the necessity of adopting a holistic and adaptive cybersecurity approach that combines technological solutions with user awareness and policy enforcement. The implications of this study suggest that continuous innovation, regular security assessments, and strategic investment in cybersecurity infrastructure are essential to combat the dynamic nature of cyber threats and ensure robust digital protection.
Copyright (c) 2024 https://creativecommons.org/licenses/by-nc/4.0
Open Digital Commerce as Inclusive Business Model Innovation: An Embedded Case Study of ONDC and India’s Digital Public Infrastructure
This paper presents a revised case-study manuscript on the Open Network for Digital Commerce (ONDC) as an important example of business model innovation in an emerging digital economy. The study argues that ONDC is better understood not simply as another e-commerce initiative but as an open market architecture built on digital public infrastructure. Using an embedded case-study approach and a wide body of secondary evidence, the paper examines how interoperable payments, modular participation, service orchestration, compliance visibility, and inclusive design combine to make open digital commerce viable. The discussion shows that ONDC changes the basis of competition. In closed marketplaces, value capture often depends on controlling discovery, data, and traffic within one firm boundary. In ONDC, by contrast, value depends more on capability differentiation across buyer applications, seller applications, logistics, payments, analytics, and customer service. The paper further shows that the developmental promise of open commerce depends on merchant enablement, multilingual and accessible interfaces, accountable service recovery, and trust-building routines rather than on technical openness alone. Because of these features, the ONDC case also has wider implications for mobile payments, tax technology, tourism services, supply-chain transparency, entrepreneurship, inclusive digital support systems, and AI-enabled service design. The paper concludes that open digital commerce can widen participation and support innovation, but only when interoperability is matched with strong governance, operational capability, and user-centered service design.
Enhancing Cross-Cultural Collaboration Via Digital Communication In The Transformation Era
Recent research and best practices are synthesised in this paper to provide a comprehensive model of how to equip students with the communication skillset required in an increasingly digital, data-driven, and multicultural society. We can get insights into how these interact based on books and articles published between, say, 2000 and 2025, of which I’ve listed a selection. We extend existing models by suggesting that systematically integrating these areas—through school curricula and corporate training—can streamline, expedite, and tone down messages. Our research suggests that teaching people how to communicate digitally with authentic tools, through culturally sensitive practices, and as part of an on-going feedback process makes them more confident communicators, more trusting of others, and better able to experiment in business. The report concludes with a “strategic approach built on data” that educators, administrators and policymakers can implement to help students become more effective communicators across the globe. The goal is to effectively communicate in digital spaces in an inclusive, imaginative, and responsible manner. Our article also addresses how new technologies, including generative AI, real-time language translation and augmented reality, are redefining what communicators in all corners of the globe require. We turn to a case-based analysis to demonstrate how communication strategies must evolve in schools, workplaces, and public service venues. As nations contend with an increasing number of globally interconnected and tech-mediated contacts, there is a greater urgency for cross-functional communication capabilities.
Manoj Shankar Mishra, Dr. Nempal Singh, Dr. SeetaJaiswar
Artificial Intelligence-Enabled Transformation Across Management and Engineering Domains: An Integrative Review of Finance, Services, Supply Chains, Education, and Social Innovation
AI has ceased to be a technical and expert ability, but it is a strategic asset and is used to make decisions, provide services, predict, engage with customers, comply, and innovate in various fields. But the literature is frequently unresponsive. Finance scholarship is focused on prediction, compliance and risk, service and marketing scholarship on personalization and automation, supply chain scholarship on visibility and resilience, educational scholarship on guidance and learning support, and smart city scholarship on social innovation, inclusion and solving urban problems. This disruption prevents the creation of a consistent view of how artificial intelligence creates value in the fields of management and engineering as well as introduces new governance, trust, and sustainability issues. The paper will create an integrative review of cross-domain applications of artificial intelligence and suggest a strategic model of AI-enabled sustainable value creation. The review summarizes the recent literature along with eleven area contributions including disability peer support, urban technologies, quantum-enhanced AI, blockchain and IoT in supply chains, AI-generated educational visuals, AI agents in customer service, sustainable tourism, AI in tax compliance, stock market prediction, digital school counselling, and AI in finance. By relying on the discussion of dynamic capabilities, technology acceptance, trust and risk perspectives, and sustainable value logic, the paper contends that the key to successful AI adoption is not only based on the performance of the algorithm but on the design that is human-centered, its domain relevance, data quality, and institutional legitimacy, as well as quantifiable organizational outcomes. The paper has three contributions. First, it charts the movement of AI capabilities throughout a variety of management and engineering environments. Second, it determines repetitive processes that relate AI inputs to operational, strategic, and social outputs. Third, it provides an inclusive, governance, explainability, capacity building, and responsible innovation cross-domain research agenda. The review has concluded that AI generates long-term value when technical complexities are combined with trust, sustainability, and implementation that is contextual.
Dr.Rafiq Ahmad Kumar, Peer Zada Rayees, Dr. Irshad Rashid
Design of Machine Learning Models for Financial Risk Assessment and Investment Decision Support in the Financial Sector
Accurate financial risk assessment is critical for investment decision-making and portfolio management in today's volatile markets. This paper presents the design of machine learning models specifically tailored for financial risk assessment and investment decision support. Our approach integrates ensemble methods, time series forecasting, and sentiment analysis to evaluate credit risk, market risk, and operational risk comprehensively. The models are trained on historical financial data and validated using stress testing scenarios. Results indicate superior performance compared to traditional risk models, with area under ROC curve values exceeding 0.92 for credit risk prediction and portfolio optimization achieving 15% higher risk-adjusted returns. The research provides financial institutions with advanced tools for navigating complex investment landscapes.
Development of Advanced Business Intelligence Systems Using Artificial Intelligence to Transform Large-Scale Data Into Strategic Insights
The exponential growth of organizational data presents both opportunities and challenges for strategic decision-making. This research develops advanced business intelligence systems that harness artificial intelligence to transform large-scale, heterogeneous data into actionable strategic insights. Our proposed architecture combines big data processing frameworks with machine learning algorithms to enable real-time analytics, predictive modeling, and automated insight generation. The system incorporates natural language generation capabilities to translate complex analytical findings into executive-ready narratives. Evaluation across multiple enterprise deployments demonstrates a 47% reduction in time-to-insight and 34% improvement in forecast accuracy. The study advances the theoretical understanding of AI-enabled business intelligence and offers practical implementation guidance.
AI-IOT Integration For Adaptive Smart Manufacturing: A Multidimensional Approach
The rapid evolution of Industry 4.0 has emphasized the need for adaptive smart manufacturing systems capable of responding dynamically to changing production demands. While IoT enables real-time data acquisition from distributed sensors, artificial intelligence (AI) offers predictive analytics and decision-making capabilities. However, existing studies largely focus on isolated implementations of AI or IoT, resulting in limited adaptability and suboptimal operational efficiency. This research proposes a multidimensional framework for integrating AI and IoT to enable adaptive manufacturing processes. Using a combination of edge computing, cloud-based AI analytics, and IoT-enabled cyber-physical systems, the study demonstrates enhanced production flexibility, reduced downtime, and improved resource utilization. Experimental validation through a simulated smart factory environment shows that the integrated system outperforms conventional approaches in predictive maintenance accuracy and process optimization. The findings highlight the practical implications of AI-IoT synergy, providing manufacturers with a scalable and resilient approach to achieving real-time adaptability, operational efficiency, and competitive advantage in dynamic industrial environments.
AI-IoT integration, adaptive smart manufacturing, Industry 4.0, edge computing, predictive maintenance, cyber-physical systems, operational efficiency, real-time adaptability, process optimization, resilient manufacturing
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