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.
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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.
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc/4.0
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
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0
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
Copyright (c) 2026 Essay International Journal of Engineering, Science and Management