Euro Vantage journals of Artificial intelligence
The Euro Vantage Journal of Artificial Intelligence (EVJAI) is a premier, peer-reviewed, open-access academic journal that provides a comprehensive platform for scholars, researchers, and professionals to explore, discuss, and disseminate advancements in artificial intelligence (AI). Published by NUZM Solutions, EVJAI serves as a bridge between academia and industry, offering a deep insight into the latest developments in AI, machine learning, deep learning, neural networks, and computational intelligence. The main objective of EVJAI is to provide fast and reliable publication to researchers, scientists, engineers, and professionals who wish to communicate new findings, methods, and insights in artificial intelligence and its related disciplines. The journal welcomes original research articles that contribute to theoretical advancements or practical applications and also considers review papers that critically examine emerging trends and highlight future directions in the field. EVJAI covers a wide scope of topics within artificial intelligence, including machine learning, deep learning, neural networks, natural language processing, computer vision, robotics, data science, and computational intelligence. It also publishes work on generative AI, large language models, cybersecurity, and the integration of AI with smart cities, the Internet of Things (IoT), and sustainable development. Special emphasis is given to studies addressing AI ethics, governance, transparency, and responsible adoption in real-world systems. EVJAI is indexed in several major indexing databases and tries to keep an updated record of current indexing in our Indexing & Links page By maintaining this broad focus, EVJAI reflects the interdisciplinary nature of modern AI research, encouraging contributions that merge computer science, applied mathematics, cognitive science, and engineering with practical problem-solving across various sectors such as healthcare, finance, education, and industry. As an open-access journal, EVJAI provides immediate and free availability of all published content. Readers are allowed to read, download, copy, distribute, and share articles without any restrictions, provided proper acknowledgment of the original publication is given. This open-access policy reflects the journal’s mission of promoting a greater global exchange of knowledge in artificial intelligence and related disciplines. The journal is indexed in Google Scholar, which provides global visibility and accessibility for published articles. With a focus on academic excellence, open knowledge sharing, and international collaboration, the EuroVantage Journal of Artificial Intelligence (EVJAI) is committed to becoming a leading source of research and innovation in artificial intelligence. The EuroVantage Journal of Artificial Intelligence (EVJAI) citation style is available for use in EndNote. Authors and researchers can easily download the official EndNote style file from the EVJAI website and add it to their reference management software. Using the official style ensures that all references and citations are formatted correctly and consistently in accordance with the journal’s requirements. For the convenience of authors and readers, download links for citation files are provided on each article’s abstract page. These files can be directly imported into EndNote and Mendeley, allowing easy management of references and ensuring proper formatting according to the EVJAI citation style. For additional information on this journal visit our About the journal page.
The Rise of Agentic Workflows: Why Businesses Are Adopting Them
Agentic workflows represent the next evolutionary step beyond traditional robotic process automation (RPA) and simple rule-based orchestration. Unlike conventional automation tools that execute predefined scripts, agentic workflows employ autonomous AI agents capable of reasoning, planning, multi-step decision-making, tool use, and self-correction in dynamic environments. This paper examines the technological, economic, and organizational drivers behind the rapid enterprise adoption of agentic systems in 2024–2025. Through industry case studies and adoption surveys, we demonstrate that organizations deploying agentic workflows are achieving 3–8× higher automation rates in knowledge-work processes previously considered “unautomatable.” The shift is driven by dramatic improvements in large language models, the maturation of agent frameworks, falling inference costs, and the urgent need to counter shrinking workforces and rising labor costs.
Copyright (c) 2025 Euro Vantage journals of Artificial intelligence
Workflow Automation with LLMs: Practical Guide for 2025
In the rapidly evolving landscape of artificial intelligence, large language models (LLMs) have emerged as transformative tools for automating workflows across various industries. This practical guide explores how LLMs can streamline processes, enhance efficiency, and drive innovation in 2025. By delving into the fundamentals, tools, design strategies, real-world applications, and potential challenges, the paper provides actionable insights for professionals seeking to implement LLM-driven automation. Emphasizing a balance between technological capabilities and ethical considerations, it aims to equip readers with the knowledge to build robust, scalable systems that adapt to dynamic business needs. Through detailed explanations and examples, this guide underscores the potential of LLMs to revolutionize traditional workflows, reducing manual intervention while amplifying human creativity and decision-making.
Metaheuristic Optimization-Assisted LSTM-DBN Model for Predictive Healthcare Analytics and Biomedical Informatics
The integration of artificial intelligence (AI) with healthcare informatics has led to significant advancements in disease prediction, patient risk assessment, and medical decision support. However, healthcare data is highly heterogeneous, dynamic, and prone to imbalance and uncertainty—posing challenges for accurate temporal modeling and feature extraction. This research presents a Metaheuristic Optimization-Assisted LSTM-DBN Model, a hybrid deep learning framework designed to enhance predictive healthcare analytics by fusing Long Short-Term Memory (LSTM) networks for temporal modeling with Deep Belief Networks (DBN) for hierarchical feature abstraction. To optimize performance, Tree Growth Optimization (TGO) and Tunicate Swarm Optimization (TSO) algorithms are employed for dynamic hyperparameter tuning. The model is tested on benchmark biomedical datasets, including physiological signal sequences, patient vital trends, and disease progression records. Experimental outcomes demonstrate superior prediction accuracy, stability, and interpretability compared to traditional deep models. This optimized hybrid architecture enables precise temporal analysis of patient health trajectories, facilitating early diagnosis, clinical decision support, and efficient biomedical data modeling—paving the way for intelligent, data-driven healthcare systems.
A Unified Deep Learning Framework for Predictive Healthcare Analytics Using TJO-Based Feature Selection and Tree Growth Optimized Graph-Temporal Modeling
The intersection of healthcare and biomedical informatics has witnessed exponential data growth through electronic health records (EHRs), wearable devices, and multi-modal biomedical sensors. However, extracting actionable intelligence from this complex, high-dimensional, and temporally evolving data remains a major challenge. This paper proposes a unified deep learning framework that integrates Tree Growth Optimization (TGO)-based feature selection with a Tree Growth Optimized Long Short-Term Memory (TGO-LSTM) and graph-based modeling for predictive healthcare analytics. The framework efficiently handles temporal dependencies and inter-patient relationships by combining graph and sequential learning paradigms. TGO plays a dual role—selecting the most informative biomedical features and tuning LSTM hyperparameters for optimal predictive accuracy. The proposed model was validated using benchmark healthcare datasets such as MIMIC-III and PhysioNet, demonstrating superior disease prediction accuracy, interpretability, and computational efficiency compared to existing CNN-LSTM and GNN-RNN models. Experimental results confirm that integrating Metaheuristic optimization with graph-temporal architectures enhances the predictive power of healthcare systems while maintaining scalability and transparency—key requirements for real-world clinical deployment.
Blockchain-Based Cybersecurity Frameworks for Secure Government Communications
Government communication systems form the backbone of national security, governance, and public service delivery. These systems are frequently targeted by cyber adversaries seeking to intercept, manipulate, or disrupt sensitive information flows. Traditional security mechanisms, while effective to a degree, struggle with issues of centralization, traceability, and resilience against insider or state-sponsored threats. Blockchain technology, with its distributed ledger, immutability, and consensus mechanisms, provides a compelling foundation for reimagining secure government communications. This paper explores blockchain-based cybersecurity frameworks tailored for government contexts, focusing on confidentiality, integrity, authenticity, and resilience. It outlines architectural approaches that integrate blockchain with secure messaging protocols, decentralized identity management, and smart contract-driven access control. Key challenges, including scalability, interoperability, latency, and governance, are analyzed in detail, alongside strategies for hybrid architectures that balance performance with security. The study demonstrates that blockchain-enabled communication networks can enhance trust, accountability, and auditability across inter-agency and cross-border operations, ultimately reinforcing sovereignty and national resilience.
Automated Machine Learning (AutoML): The Path to Self-Evolving Models
The increasing complexity of machine learning (ML) model design has created a growing demand for automation in the end-to-end ML pipeline. Automated Machine Learning (AutoML) has emerged as a revolutionary paradigm that democratizes ML development by automating tasks such as data preprocessing, feature selection, model selection, and hyperparameter optimization. AutoML bridges the gap between expert data scientists and domain practitioners by enabling the creation of high-performing models with minimal human intervention. This paper explores the evolution of AutoML as a pathway toward self-evolving models—systems capable of autonomously improving their performance through continuous feedback and adaptation. It examines the core techniques underlying AutoML, including Bayesian optimization, meta-learning, neural architecture search (NAS), and reinforcement learning-based tuning strategies. Furthermore, the paper discusses the integration of AutoML with emerging technologies such as cloud computing and federated learning, highlighting its potential in building scalable, adaptive, and privacy-aware AI systems. Finally, it identifies key challenges in interpretability, computational cost, and fairness, concluding with insights into how AutoML will shape the future of autonomous, self-optimizing artificial intelligence.
Enhancing Cybersecurity Defenses Against AI-Generated Deepfake Videos: A Framework for Real-Time Detection and Mitigation
Artificial intelligence has increased quickly, allowing the production of highly realistic deepfake videos, which are extremely dangerous to cybersecurity, privacy, and putting trust in people. Conventional detector control systems do not necessarily work in real-time and systems are susceptible to malicious use. The research will suggest a holistic approach to the real-time detection and mitigation of AI-generated deepfake videos. The framework uses sophisticated machine learning and deep learning models, such as convolutional neural networks (CNNs) and transformer based ones, with multi-modal visual, audio, and metadata modeling. The suggested system is built to work hand in hand with the cybersecurity monitoring systems, allowing to perform automated threat detection and response, including flagging of content and verification of the source. Through the framework, experimental analysis has revealed that the framework can attain high detection ability with minimal latency, providing a scalable platform to digital environment measures against new deepfake and fraudsters. The research will help in the creation of more resilient cybersecurity defences as well as offer practical information to policymakers, platform proprietors and security experts.
Workflow Automation and Process Optimization through Sales force Platforms
The increasing complexity of business operations and the demand for efficiency have made workflow automation a crucial component of modern enterprise management. Salesforce, as a leading cloud-based customer relationship management (CRM) platform, offers robust tools for streamlining workflows, automating repetitive processes, and optimizing organizational performance. This paper examines how Salesforce platforms enable workflow automation and process optimization through features such as Salesforce Flow, Process Builder, and Einstein AI. It highlights the strategic role of automation in reducing manual effort, enhancing accuracy, and fostering data-driven decision-making. Furthermore, the study explores the challenges and best practices in implementing Salesforce automation, emphasizing scalability, adaptability, and integration with third-party systems. By leveraging Salesforce’s ecosystem, organizations can not only improve operational efficiency but also align workflows with customer-centric strategies that drive innovation and growth.
From Data to Insight: Decoding Student Cognition with Explainable AI Models
Understanding student cognition is critical for enhancing educational outcomes and creating adaptive learning environments. This paper presents a comprehensive study on the application of Explainable Artificial Intelligence (XAI) models in decoding student cognitive abilities using educational data. By integrating explainable machine learning techniques with educational data mining (EDM), the proposed framework enables accurate assessment of students’ learning behaviors while maintaining transparency and interpretability. The study employs models such as SHAP (Shapley Additive Explanations), LIME (Local Interpretable Model-Agnostic Explanations), and attention-based neural networks to analyze diverse cognitive indicators including attention span, problem-solving skills, and knowledge retention. Experimental results on real-world educational datasets show significant improvements in both prediction accuracy and interpretability compared to traditional black-box models. The findings highlight the potential of XAI to provide educators with actionable insights, enabling personalized learning interventions while ensuring fairness, accountability, and trust in educational AI systems.
Artificial Intelligence in Pulp Vitality Assessment: A Novel Approach to Diagnostic Decision-Making
The advent of artificial intelligence (AI) is a groundbreaking technology in the field of dentistry and it is promising new opportunities to improve the precision of diagnosis and decision-making. A field that is of considerable clinical concern is pulp vitality assessment which is a foundation of endodontic diagnosis and treatment planning. The classical diagnostic techniques, such as thermal pulp, electric pulp, and laser Doppler flowmetry, and the more modern ones, such as laser Doppler flowmetry, are usually associated with issues of subjectivity, variability of patients, and limited accuracy. The new solution offered by AI-based methods is based on the use of machine learning and deep learning algorithms to process various clinical and imaging data, thus, allowing objective and reproducible assessment of pulp status. Combining multimodal data (radiograph and thermography, patient history) will enable AI to be more diagnostic and aid clinicians with real time data. Moreover, the technologies promise improvements in the diagnostic errors, treatment outcomes, and the way to predictive and preventive endodontics. Despite these benefits there are still challenges in the areas of data availability, algorithm transparency, and clinical integration. Before widespread adoption, ethical issues, especially those to do with data privacy and possible bias, need to be addressed. However, AI is a potentially useful paradigm shift in the area of pulp vitality testing, and it could significantly enhance the quality of diagnostic decisions in the field of endodontics.
AI-Powered Classification of Root Canal Irrigation Efficiency Based on Image Analysis
Root canal irrigation plays a vital role in the endodontic treatment success, since it guarantees the elimination of debris, microorganisms, and biofilm of the complex root canal system. Conventional irrigation efficiency assessment techniques tend to be subjective, laborious and low reproducibility. In this research, the authors explain how artificial intelligence (AI) and image analysis can be used to categorize the efficacy of root canal irrigation. Images of treated canals of high resolution were obtained and preprocessed to extract features. A convolutional neural network (CNN)-based model was trained to read morphological patterns and categorize the results of the irrigation as efficient or inefficient. The accuracy of the AI model during the classification process was rather high in the comparison with expert ratings, which indicates the possibility of objective and automated assessment. The results indicate that AI-based image recognition can be an effective supplement to an endodontic study and clinical practice and provide a stable and quick assessment of irrigation procedures. Additional future studies are advised to be conducted on a larger scale, in real time, and to be validated in clinical settings to improve reliability and become part of routine care.
Towards Transparent Learning Analytics: A Study on Explainable AI in Cognitive Skill Prediction
The increasing adoption of artificial intelligence (AI) in educational technology has paved the way for advanced predictive models capable of analyzing and forecasting student cognitive skills. However, the opaque nature of many high-performance AI models limits their trustworthiness and practical utility in educational settings. This paper explores the integration of Explainable AI (XAI) techniques into cognitive skill prediction to achieve transparency, interpretability, and fairness in learning analytics. By employing methods such as Shapley Additive Explanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), and attention-based neural networks, the proposed framework deciphers the factors influencing cognitive skill development while maintaining robust predictive performance. Experimental results on large-scale educational datasets reveal significant improvements in interpretability without compromising accuracy, enabling educators to make data-driven decisions for personalized learning interventions and equitable assessment practices.
Ethical AI and QA-Driven Cybersecurity Risk Mitigation for Critical Infrastructure
The critical infrastructure is becoming more dependent on artificial intelligence (AI) to monitor, detect and respond to cybersecurity threats, including energy grids, healthcare networks, and transportation systems. Nonetheless, with the implementation of AI, there are also other risks, such as the manipulation of algorithms, the manipulation of models, and the risk of privacy invasion. The study examines the application of ethical AI concepts and quality assurance (QA)-based models in enhancing cybersecurity risks mitigation of critical infrastructure. It highlights the importance of transparency, fairness, and accountability in AI driven threat detection, and strict QA testing of AI models to ensure ongoing testing, validation and monitoring. The research report focuses on an organized process of risk scoring, prioritization, and automated system response and maintaining ethical and regulatory standards. With the predictive capabilities of AI coupled with QA and supervision, such work is a roadmap on how to construct resilient, trustworthy, and ethically dedicated cybersecurity systems that will resist emerging threats to critical infrastructure.
AI-Enabled Predictive Maintenance for Industrial Equipment: Enhancing Reliability and Reducing Downtime
The growing sophistication of industrial systems and the high price of unplanned equipment breakdowns has increased the importance of sophisticated maintenance plans. Conventional methods, including reactive and preventive maintenance, tend to be ineffective, which results in excessive downtimes, wastage of resources and safety issues. Predictive maintenance is a transformative solution to artificial intelligence (AI) as it applies machine learning algorithms, sensor data, and real-time analytics to predict equipment failures before they happen. Predictive maintenance systems can be optimized, increase the asset lifespan, and decrease operational costs considerably thanks to the integration with the Industrial Internet of Things (IIoT), big data platforms, or digital twins. The paper will discuss the use of AI in predictive maintenance with its technological basis, industry use, advantages, and limitations and its future opportunities. The research underlines predictive maintenance as one of the foundations of Industry 4.0 and sustainable industrial development by showing how AI-based knowledge promotes better reliability and reduces downtimes.
Explainable Fraud and Health Anomaly Detection via SMOTE-Enhanced Deep Models
The rapid growth of digital finance, energy markets, and healthcare systems has increased the need for robust anomaly detection models that are both accurate and transparent. Traditional deep learning approaches face two critical limitations: severe class imbalance that reduces sensitivity to rare but high-risk events, and their black-box nature, which impedes trust in high-stakes decision-making. This study proposes a unified framework that leverages Synthetic Minority Oversampling Technique (SMOTE) and deep neural architectures, integrated with Explainable Artificial Intelligence (XAI) methods, to address these challenges across two distinct domains: fraud detection in finance and energy transactions, and health anomaly prediction for conditions such as stroke and Alzheimer’s disease. The framework applies SMOTE variants to rebalance datasets, followed by training on hybrid deep models that combine convolutional and recurrent structures with attention mechanisms. Interpretability is achieved through SHAP values, LIME analysis, and Grad-CAM visualizations for medical imaging tasks. Evaluation is conducted using widely accepted metrics, including AUC, precision, recall, F1-score, and explanation fidelity, across multiple real-world datasets from financial transactions, energy market operations, and healthcare records. Results demonstrate that SMOTE-enhanced deep models outperform conventional baselines, improving recall by up to 18% in fraud detection and 15% in healthcare anomaly prediction, without compromising precision. Explainability assessments reveal domain-relevant insights: transaction patterns and ESG-related anomalies in finance, and clinically interpretable risk factors in healthcare. Cross-domain transferability tests further show that anomaly detection strategies in financial datasets can inform healthcare models and vice versa, highlighting the potential of shared approaches in combating rare-event prediction challenges. The study concludes that integrating oversampling, deep learning, and explainability yields a transparent and generalizable framework applicable to multiple anomaly detection domains.
Modeling the Impact of Climate Risk on Agricultural Productivity and Food Prices
Climate change has emerged as one of the most critical global challenges, with profound implications for agricultural productivity and food prices. The complexity and interconnectivity of climatic variables, such as temperature fluctuations, irregular precipitation patterns, and increased frequency of extreme weather events, necessitate sophisticated modeling approaches to capture their influence on agriculture. This study aims to model the impact of climate risk on agricultural productivity and food prices using a hybrid econometric-climate simulation framework. By integrating climate projections with agricultural yield models and price forecasting mechanisms, this research presents a comprehensive understanding of how climate risk translates into economic disruptions in the agri-food sector. Through historical data analysis, regional case studies, and machine learning-assisted regression models, the study provides empirical evidence of climate-induced yield variability and subsequent market volatility. The experimental results demonstrate that climate variability can significantly suppress crop yields, especially in vulnerable regions, which leads to sharp increases in food prices, disproportionately affecting low-income populations. The findings underscore the urgency of adaptive strategies and policy responses aimed at climate resilience and food security.
Economic Impact of Deforestation on Indigenous Communities and Ecosystem Services in the Amazon
Deforestation in the Amazon rainforest continues to be a critical global issue, significantly affecting the ecological balance, climate stability, and the livelihoods of indigenous communities. This study explores the intricate economic repercussions of Amazonian deforestation on both the native populations and the ecosystem services upon which they and the wider global community depend. It utilizes a mixed-method approach combining qualitative field research with economic valuation techniques to quantify the loss in provisioning, regulating, and cultural ecosystem services. The research further analyzes the socio-economic displacement, loss of traditional income sources, and disruption in community health that results from forest degradation. In addition to evaluating data from satellite imagery and ecological models, the study incorporates direct testimonies from Amazonian tribes affected by logging, mining, and agribusiness expansion. The findings underscore a paradox wherein short-term economic gains for external actors come at the cost of long-term ecological damage and socio-economic destabilization of native populations. The results advocate for the urgent integration of indigenous economic systems into conservation policies and highlight the critical role of sustainable forest management.
The Economics of Plastic Pollution: Policy Instruments for Reducing Single-Use Plastics
Plastic pollution has emerged as a pressing environmental and economic concern in the 21st century, particularly driven by the proliferation of single-use plastics. These items, while inexpensive and convenient, impose vast externalities on ecosystems, public health, and economic productivity. This paper critically examines the economic foundations of plastic pollution and evaluates the efficacy of various policy instruments aimed at curbing the use and production of single-use plastics. Employing an economic lens, we analyze market failures associated with plastic consumption and explore corrective mechanisms such as taxes, bans, extended producer responsibility, and subsidies for alternatives. Experimental evidence and international case studies highlight the relative performance of these policies in achieving environmental and economic efficiency. By quantifying external costs and modeling consumer and producer responses to interventions, this paper offers a comprehensive framework for designing plastic reduction policies that balance equity, efficiency, and feasibility. Our findings suggest that a combination of instruments—tailored to regional economic conditions and integrated within circular economy principles—yields the most sustainable outcomes.
Water Pricing and Allocation Policies: Economic Efficiency versus Equity in Water-Scarce Regions
Water scarcity has emerged as a formidable challenge in numerous regions across the globe, necessitating robust and well-calibrated water pricing and allocation policies. This paper investigates the intricate balance between economic efficiency and equity in the management of scarce water resources. The tension between market-based solutions that promote efficiency and regulatory or equity-oriented approaches that ensure fairness is explored using both theoretical foundations and empirical case studies. We critically examine various water pricing models, allocation mechanisms, and the role of government and community participation in policy design. Experimental simulations based on data from arid regions provide insights into how alternative strategies perform under different scarcity scenarios. The results underscore the potential trade-offs and synergies between efficiency and equity goals and suggest a path forward for integrated, adaptive water governance.
The Influence of Earnings Management on Earnings Quality and Corporate Sustainability in GCC Companies
In the dynamic and evolving economies of the Gulf Cooperation Council (GCC), financial transparency and sustainability have become paramount to long-term development goals. However, the persistent practice of earnings management threatens both the quality of reported earnings and the broader corporate sustainability agenda. This study explores the relationship between earnings management, earnings quality, and corporate sustainability in listed GCC companies. By distinguishing between accrual-based and real earnings manipulation techniques, we assess their impact on earnings informativeness, reliability, and long-term strategic performance. The findings reveal that excessive earnings management undermines earnings quality, distorts financial signals to stakeholders, and detracts from environmental, social, and governance (ESG) commitments. The paper calls for stronger governance mechanisms and harmonized regulatory frameworks to promote sustainable financial reporting across the region.
Financial Facades and Sustainable Realities: Earnings Management across Distressed and Healthy GCC Enterprises
Earnings management remains a contentious issue in financial reporting, often obscuring the true economic performance of firms. In the Gulf Cooperation Council (GCC) region, where economic diversification and sustainability are strategic goals, the practice of managing earnings presents challenges to transparency and long-term resilience. This study examines the extent and nature of earnings management across distressed and financially healthy firms in the GCC. By leveraging accrual-based and real earnings management models, and differentiating between distressed and non-distressed entities, we explore how financial pressure influences reporting behavior. The findings suggest that distressed firms are more likely to engage in opportunistic earnings manipulation, while healthy firms tend to manage earnings for signaling stability and attracting investment. These dynamics have profound implications for regulators, investors, and policymakers seeking to foster sustainable and trustworthy financial systems in the region.
Targeting HIV Prevention Inequities: A Pilot ACT-Based Prep Intervention for Young Black MSM in the Southern United States
Pre-exposure prophylaxis (Prep) has demonstrated considerable effectiveness in reducing HIV transmission among high-risk populations. However, disparities in Prep uptake persist, particularly among young Black men who have sex with men (MSM) in the Southern United States, a region disproportionately burdened by HIV. The ACTPrEP pilot study protocol presents an acceptance and commitment therapy (ACT)-based behavioral intervention aimed at improving Prep initiation, adherence, and retention among this demographic. This study proposes a culturally tailored, stigma-sensitive, and psychologically grounded approach designed to address not only behavioral barriers but also contextual and structural challenges related to HIV prevention. The protocol integrates individual counseling, motivational enhancement strategies, and mobile health components to ensure engagement and continuity of care. This paper details the study’s methodology, theoretical grounding, expected outcomes, and pilot testing framework, along with a discussion of the broader implications for Prep interventions in marginalized communities.
High-Precision Fault Identification in Motor Bearings via Enhanced Multi-Kernel ELM Framework
Motor bearings are among the most critical components in rotating machinery, and their failure often leads to costly downtime and maintenance. Accurate and early detection of bearing faults can significantly enhance the reliability and longevity of industrial systems. This study introduces a novel Enhanced Multi-Kernel Extreme Learning Machine (EMK-ELM) framework for high-precision fault identification in motor bearings. The model leverages a dynamic integration of Gaussian and polynomial kernels to improve classification performance over traditional single-kernel methods. By capturing both linear and nonlinear relationships in vibration signal data, the EMK-ELM framework demonstrates superior generalization and faster learning capabilities. The experimental evaluation is carried out using benchmark datasets such as the Case Western Reserve University (CWRU) bearing dataset, highlighting the framework’s robustness across different fault types and severities. Performance is compared with standard machine learning classifiers, including Support Vector Machines and traditional ELM, showing that EMK-ELM offers improved diagnostic accuracy, training efficiency, and fault sensitivity. This paper provides a detailed discussion of model architecture, feature engineering, experimental methodology, and statistical evaluation, supporting its claim as a high-precision, real-time-capable solution for motor bearing fault detection.
Hybrid Kernel-Driven ELM for Intelligent Fault Detection in Motor Rolling Bearings
Motor rolling bearings are critical components in industrial machinery, and their failure can lead to significant downtime and financial losses. Therefore, developing robust and intelligent fault detection systems is essential for ensuring operational reliability. This study presents a novel approach utilizing a Hybrid Kernel-Driven Extreme Learning Machine (HK-ELM) for intelligent fault detection in motor rolling bearings. The hybridization of multiple kernel functions enhances the learning capability and generalization of ELM by capturing complex nonlinear features from vibration signals. The proposed model integrates Gaussian and polynomial kernels to construct a composite kernel function, enabling adaptive feature extraction. The experimental evaluation was conducted on publicly available bearing datasets, where the hybrid kernel model was benchmarked against traditional ELM and other standard machine learning models. Results demonstrate that the HK-ELM model outperforms its counterparts in classification accuracy, training time, and fault recognition robustness, especially in the presence of noise and overlapping classes. This paper also discusses the impact of kernel parameter tuning and the hybridization strategy on diagnostic performance, concluding that the hybrid approach offers a highly scalable and efficient solution for real-time industrial fault detection applications.
A Novel Multi-Kernel Extreme Learning Machine Model for Accurate Diagnosis of Motor Bearing Faults
Accurate fault diagnosis of motor bearings is essential for ensuring the reliability and safety of rotating machinery in various industrial applications. Traditional machine learning models often struggle to balance generalization and precision, particularly when processing complex, non-linear features present in motor vibration signals. To address this challenge, this research introduces a novel Multi-Kernel Extreme Learning Machine (MKELM) model tailored for precise diagnosis of motor bearing faults. The proposed model integrates multiple kernel functions—specifically Gaussian, polynomial, and wavelet kernels—to exploit diverse data characteristics and enhance classification performance. Experimental evaluations using the Case Western Reserve University (CWRU) bearing dataset demonstrate the effectiveness of MKELM in achieving superior accuracy, robustness, and generalization compared to conventional single-kernel ELM and other baseline classifiers such as Support Vector Machines and Random Forests. Through rigorous analysis, the study validates the hypothesis that multi-kernel learning, when fused with ELM's fast learning capabilities, provides a powerful framework for bearing fault detection in real-world scenarios.
Enhanced Fault Diagnosis of Motor Rolling Bearings Using an Improved Multi-Kernel Extreme Learning Machine Approach
Fault diagnosis of motor rolling bearings is critical for maintaining the operational efficiency and safety of rotating machinery systems. With the increasing demand for precise and rapid fault detection, traditional machine learning methods often fall short in terms of adaptability and accuracy under noisy or non-linear conditions. This study proposes an Enhanced Multi-Kernel Extreme Learning Machine (EMK-ELM) approach for robust and accurate fault diagnosis of rolling bearings in electric motors. The methodology integrates multiple kernel functions in the ELM framework to better capture complex data characteristics while mitigating the impact of outliers and noise. The proposed model is trained and validated using the Case Western Reserve University bearing dataset, and its performance is benchmarked against conventional ELM and other advanced classifiers including Support Vector Machines and Convolutional Neural Networks. Experimental results demonstrate that EMK-ELM significantly improves classification accuracy, generalization, and training speed, making it an ideal candidate for real-time fault diagnosis applications. This research lays a solid foundation for scalable, efficient, and intelligent bearing condition monitoring systems.
Self-Reflective Agents: Engineering Meta-Cognition in AI for Ethical Autonomous Decision-Making
This paper introduces a novel architecture for embedding meta-cognitive capabilities in AI agents, enabling them to audit their own reasoning and assess ethical implications before making autonomous decisions. The proposed framework employs layered transformer models to simulate ethical reflection, validated through real-time scenario simulations in autonomous vehicles and military UAV systems. By integrating ethical rule validators within the transformer architecture, the system facilitates "thought auditing," allowing AI agents to evaluate the morality and safety of potential actions. The effectiveness of this approach is measured using metrics such as self-reflection rate, ethical alignment score, and safety override triggers. The findings suggest significant improvements in ethical decision-making and compliance with safety standards, highlighting the potential for policy implications in AI regulation and defense compliance.
Bridging the Digital Divide: A Socio-Technical Framework for AI-Enabled Rural Healthcare Access in Developing Economies
This study introduces a federated, AI-enabled mobile health (mHealth) framework designed to improve healthcare accessibility in digitally underserved rural regions of developing economies. The framework is built upon an offline-first architecture that supports AI-driven clinical triage, referral guidance, and secure caching of medical records on edge devices, minimizing reliance on continuous internet connectivity. Field deployments in rural areas of Pakistan, Nigeria, and Peru provide empirical evidence of the system's viability and adaptability across varied socio-technical contexts. The study outlines key implementation challenges, including infrastructure limitations, healthcare worker training, and multilingual data processing. A comprehensive stakeholder analysis highlights the roles of public health ministries, NGOs, and telecom providers in supporting sustainable deployment. The paper also discusses ethical, regulatory, and data governance considerations essential for cross-national integration of federated learning systems in healthcare. This work contributes a scalable model for bridging the global digital divide in rural healthcare through inclusive, context-sensitive AI innovation.
Global digital divide in healthcare access
Cross-national policy and ethical guidelines
Public-private partnerships in health tech
Socio-technical systems for underserved regions.
Blending Medical Insights with Scalable AI for Predictive Healthcare and Mental Health Solutions
Bringing together artificial intelligence and clinical expertise is starting to reshape how we approach both physical and mental healthcare. With growing pressure on health systems to deliver more personalized and proactive care at scale, we set out to understand how AI can be used more meaningfully, not as a black-box solution, but as something grounded in real clinical insight. In this work, we explore a framework that combines semi-supervised learning, deep convolutional neural networks, and ensemble methods to make sense of complex health data. That includes inputs like wearable sensor readings, electronic health records, and genomic profiles. On the mental health side, we trained emotion prediction models using longitudinal behavioral data to help flag early signs of depression and anxiety. For physical health, our models performed well on conditions like skin cancer and diabetes, with an AUC of 0.94 and an F1 score of 0.91 on our test sets. Performance metrics aside, we put a lot of weight on making these models understandable and clinically relevant. We used SHAP values to explain which features were driving predictions and wove in domain expertise throughout the process, from how we prepared the data to how we assessed the models. The goal wasn’t just to make something that worked, but something that could actually inform care decisions. We also looked at the infrastructure side of things. For AI to be deployed safely and at scale, especially across populations, you need more than good algorithms. Our study points to the importance of cloud infrastructure, spatial data tools, and federated learning in supporting this kind of deployment
Credit, Code, and Consequence: How AI Is Reshaping Risk Assessment and Financial Equity
Artificial Intelligence (AI) has transformed numerous sectors, with financial services among the most significantly impacted. In particular, credit risk evaluation has witnessed a paradigm shift through the adoption of machine learning and advanced data analytics. These technologies promise improved accuracy, efficiency, and inclusivity in credit decision-making. However, the path is not without obstacles. Issues such as algorithmic bias, data privacy concerns, lack of transparency, and the uneven pace of regulatory oversight pose challenges to achieving financial justice. This article explores the multifaceted application of AI in credit risk evaluation, critically analyzing both its potential and pitfalls. Through an interdisciplinary lens, we examine how AI can democratize access to credit, mitigate systemic discrimination, and improve institutional performance—while also highlighting the governance, ethical, and infrastructural prerequisites necessary for its responsible implementation.