Euro Vantage journals of Artificial intelligence
type: channel
description: 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 …
publisher: Nex-Gen Unit for Zealous Minds (NUZM)
language: en
publication name: Euro Vantage journals of Artificial intelligence
issn: 3080-3403
items: Digital Twin-Based Fault Diagnosis and Resilience Monitoring for Smart Grid Transformer Systems
items: Assessing the Effectiveness of Cybersecurity Program Management Frameworks and Adversarial AI Defense Mechanisms in Medium and Large Organizations
items: An Integrated Cybersecurity Program Management Framework for Mitigating Adversarial AI Threats in Medium and Large Organizations
items: Blockchain-Supported Intelligent Systems for Privacy-Preserving Data Analytics
items: Enhancing Transparency and Security in Intelligent Systems through Blockchain-Based Governance Models
items: A Trust-Aware Intelligent Systems Framework for Secure Data Exchange Using Blockchain
items: The Rise of Agentic Workflows: Why Businesses Are Adopting Them
items: Workflow Automation with LLMs: Practical Guide for 2025
items: Metaheuristic Optimization-Assisted LSTM-DBN Model for Predictive Healthcare Analytics and Biomedical Informatics
items: A Unified Deep Learning Framework for Predictive Healthcare Analytics Using TJO-Based Feature Selection and Tree Growth Optimized Graph-Temporal Modeling
items: Blockchain-Based Cybersecurity Frameworks for Secure Government Communications
items: Automated Machine Learning (AutoML): The Path to Self-Evolving Models
items: Enhancing Cybersecurity Defenses Against AI-Generated Deepfake Videos: A Framework for Real-Time Detection and Mitigation
items: Workflow Automation and Process Optimization through Sales force Platforms
items: From Data to Insight: Decoding Student Cognition with Explainable AI Models
items: Artificial Intelligence in Pulp Vitality Assessment: A Novel Approach to Diagnostic Decision-Making
items: AI-Powered Classification of Root Canal Irrigation Efficiency Based on Image Analysis
items: Towards Transparent Learning Analytics: A Study on Explainable AI in Cognitive Skill Prediction
items: Ethical AI and QA-Driven Cybersecurity Risk Mitigation for Critical Infrastructure
items: AI-Enabled Predictive Maintenance for Industrial Equipment: Enhancing Reliability and Reducing Downtime
items: Explainable Fraud and Health Anomaly Detection via SMOTE-Enhanced Deep Models
items: Modeling the Impact of Climate Risk on Agricultural Productivity and Food Prices
items: Economic Impact of Deforestation on Indigenous Communities and Ecosystem Services in the Amazon
items: The Economics of Plastic Pollution: Policy Instruments for Reducing Single-Use Plastics
items: Water Pricing and Allocation Policies: Economic Efficiency versus Equity in Water-Scarce Regions
items: The Influence of Earnings Management on Earnings Quality and Corporate Sustainability in GCC Companies
items: Financial Facades and Sustainable Realities: Earnings Management across Distressed and Healthy GCC Enterprises
items: Targeting HIV Prevention Inequities: A Pilot ACT-Based Prep Intervention for Young Black MSM in the Southern United States
items: High-Precision Fault Identification in Motor Bearings via Enhanced Multi-Kernel ELM Framework
items: Hybrid Kernel-Driven ELM for Intelligent Fault Detection in Motor Rolling Bearings

Digital Twin-Based Fault Diagnosis and Resilience Monitoring for Smart Grid Transformer Systems
type: item
description: Smart grid transformer systems are critical nodes in national power infrastructure whose failure can cascade into widespread outages, economic losses, and public safety risks. Traditional condition monitoring approaches rely on periodic offline inspections and threshold-based alarms that are ill-suited to the dynamic, real-time nature of modern grid operations. This paper proposes a comprehensive digital twin (DT) framework for fault diagnosis and resilience monitoring of high-voltage power transformers. The architecture integrates physics-based electromagnetic and thermal models, IoT sensor data streams, and machine learning classifiers within a synchronized virtual-physical environment. Five canonical fault modes—partial discharge, winding deformation, insulation degradation, oil contamination, and core saturation—are modeled and validated against historical field data. Experimental results demonstrate an overall fault detection accuracy of 97.3%, a mean time-to-diagnosis of 4.2 seconds, and a false positive rate below 1.8%. The proposed framework reduces unplanned outage risk by an estimated 43% and improves grid resilience indices by 31% compared to conventional SCADA monitoring. The study also discusses integration pathways with CISA cybersecurity mandates and the U.S. Department of Energy's grid modernization initiatives, establishing the framework as a scalable solution for national critical infrastructure protection.
subject: Articles
  topic: section
subject: Digital twin
  topic: keywords
subject: smart grid
  topic: keywords
subject: transformer fault diagnosis
  topic: keywords
subject: condition monitoring
  topic: keywords
creator: Saif Ali
rights: Copyright (c) 2026
date: 2025-12-01
publication date: 2025-12-01
starting page: 45
ending page: 54
doi: 10.65923/avg24p53

Assessing the Effectiveness of Cybersecurity Program Management Frameworks and Adversarial AI Defense Mechanisms in Medium and Large Organizations
type: item
description: Cybersecurity and Artificial Intelligence (AI) have become central to organizational resilience in medium and large enterprises. This study experimentally evaluates the effectiveness of cybersecurity program management frameworks—ISO/IEC 27001 and NIST CSF—and assesses AI system vulnerability to adversarial attacks, including evasion, poisoning, and privacy attacks. Simulated organizational environments were employed to implement frameworks and deploy AI models for threat detection, with adversarial attacks executed to quantify vulnerabilities. Defense mechanisms, such as adversarial training and ensemble modeling, were integrated within frameworks to evaluate combined effectiveness. Results indicate that flexible frameworks like NIST CSF provide superior incident response and control effectiveness, while hybrid defense strategies significantly enhance AI robustness. The study offers actionable guidance for integrating cybersecurity governance with AI defense mechanisms, supporting organizational resilience against emerging 2026 cyber threats.
subject: Articles
  topic: section
subject: Cybersecurity
  topic: keywords
subject: Program Management Frameworks
  topic: keywords
subject: ISO/IEC 27001
  topic: keywords
subject: NIST CSF
  topic: keywords
subject: Adversarial AI
  topic: keywords
subject: AI Defense Mechanisms
  topic: keywords
subject: Organizational Security
  topic: keywords
subject: Experimental Study
  topic: keywords
creator: Oyedele Victor Samuel
creator: Arooj Fatima
rights: Copyright (c) 2026
date: 2026-02-09
publication date: 2026-02-09
starting page: 21
ending page: 30
doi: 10.65923/8822p681

An Integrated Cybersecurity Program Management Framework for Mitigating Adversarial AI Threats in Medium and Large Organizations
type: item
description: The increasing adoption of artificial intelligence (AI) in organizational cybersecurity introduces both advanced defensive capabilities and new vulnerabilities, particularly from adversarial attacks targeting machine learning models. Traditional cybersecurity program management frameworks, while effective for conventional threats, often lack mechanisms to address AI-specific challenges, leaving organizations exposed to sophisticated attacks. This study proposes an integrated, multi-layered cybersecurity framework that combines governance, risk management, real-time anomaly detection, and adversarial resilience strategies. Using an Isolation Forest–based anomaly detection model implemented in a Python environment, the framework is evaluated on synthetic datasets simulating adversarial behaviors. Experimental results demonstrate high detection accuracy (96.8%), low false positive rates (3.2%), and robust response times suitable for real-world deployment. Comparative analysis with baseline models confirms superior performance and resilience under adversarial scenarios. The proposed framework provides organizations with a scalable, adaptive, and intelligent approach to manage cybersecurity risks in AI-driven environments.
subject: Articles
  topic: section
subject: Cybersecurity
  topic: keywords
subject: Adversarial AI
  topic: keywords
subject: Program Management Framework
  topic: keywords
subject: Anomaly Detection
  topic: keywords
subject: Isolation Forest
  topic: keywords
subject: AI Resilience
  topic: keywords
subject: Medium and Large Organizations
  topic: keywords
creator: Paul Isaac Pamilerin
rights: Copyright (c) 2026
date: 2026-01-08
publication date: 2026-01-08
starting page: 1
ending page: 20
doi: 10.65923/5nqhvy18

Blockchain-Supported Intelligent Systems for Privacy-Preserving Data Analytics
type: item
description: The rapid growth of intelligent systems and data-driven technologies has increased the demand for secure and privacy-preserving data analytics. Organizations increasingly rely on machine learning and artificial intelligence to extract insights from massive datasets, yet the sensitive nature of modern data—such as healthcare records, financial transactions, and personal behavioral information—creates significant privacy challenges. Traditional centralized analytics frameworks often require data sharing among multiple entities, which increases risks of unauthorized access, data leakage, and privacy violations. Blockchain technology has emerged as a promising solution to these challenges due to its decentralized architecture, immutable ledger, and cryptographic security mechanisms. This research investigates a blockchain-supported intelligent system framework designed to enable privacy-preserving data analytics across distributed environments. The proposed architecture integrates blockchain infrastructure with advanced privacy-preserving techniques such as federated learning, homomorphic encryption, and differential privacy to allow collaborative analytics without exposing raw data. Blockchain acts as a secure coordination layer that records transactions, ensures data integrity, and enforces trust among participants.
subject: Articles
  topic: section
subject: Privacy-Preserving Analytics
  topic: keywords
subject: Federated Learning,
  topic: keywords
subject: Secure Data Sharing
  topic: keywords
subject: Distributed Machine Learning
  topic: keywords
creator: Emma Stacy
rights: Copyright (c) 2024 Euro Vantage journals of Artificial intelligence
date: 2024-09-20
publication date: 2024-09-20
starting page: 17
ending page: 25
doi: 10.65923/xtb1gb25

Enhancing Transparency and Security in Intelligent Systems through Blockchain-Based Governance Models
type: item
description: The rapid adoption of intelligent systems across domains such as finance, healthcare, transportation, and smart infrastructure has created new opportunities for automation, efficiency, and data-driven decision making. However, these systems often operate in complex digital ecosystems where transparency, trust, and data integrity remain significant concerns. Traditional centralized governance frameworks struggle to provide verifiable accountability and secure collaboration among multiple stakeholders. Blockchain technology, with its decentralized ledger structure and cryptographic validation mechanisms, offers a promising solution to these challenges by enabling transparent and tamper-resistant governance models for intelligent systems. This study proposes a blockchain-based governance architecture designed to enhance the transparency, reliability, and security of intelligent systems operating in distributed environments. The framework integrates decentralized consensus mechanisms, smart contract-based policy enforcement, and secure data sharing protocols to establish trustworthy governance across autonomous agents and data providers.
subject: Articles
  topic: section
subject: Intelligent Systems
  topic: keywords
subject: Transparency
  topic: keywords
subject: Blockchain Governance
  topic: keywords
subject: Distributed Ledgers
  topic: keywords
creator: Fatima Al Mansoori
rights: Copyright (c) 2024 Euro Vantage journals of Artificial intelligence
date: 2024-08-11
publication date: 2024-08-11
starting page: 9
ending page: 16
doi: 10.65923/dj93ax66

A Trust-Aware Intelligent Systems Framework for Secure Data Exchange Using Blockchain
type: item
description: Secure data exchange has become a critical requirement for modern intelligent systems that operate across distributed and heterogeneous environments. As artificial intelligence applications increasingly rely on collaborative data sharing among organizations, devices, and platforms, ensuring trust, integrity, and privacy in the exchanged information becomes a fundamental challenge. Traditional centralized security mechanisms often fail to provide sufficient transparency and tamper resistance, especially when multiple stakeholders with varying trust levels are involved. Blockchain technology, with its decentralized ledger architecture and cryptographic validation mechanisms, offers a promising solution to address these issues. This research proposes a trust-aware intelligent systems framework that leverages blockchain technology to facilitate secure and reliable data exchange across distributed intelligent environments. The framework integrates trust evaluation models with blockchain-based distributed ledgers to ensure that data transactions are verified, immutable, and traceable.
subject: Articles
  topic: section
subject: Blockchain
  topic: keywords
subject: Intelligent Systems
  topic: keywords
subject: Secure Data Exchange
  topic: keywords
subject: Trust Management
  topic: keywords
subject: Distributed Ledgers
  topic: keywords
creator: Park Ji Hyun
rights: Copyright (c) 2026
date: 2024-08-12
publication date: 2024-08-12
starting page: 1
ending page: 8
doi: 10.65923/qxd99034

The Rise of Agentic Workflows: Why Businesses Are Adopting Them
type: item
description: 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.
subject: Articles
  topic: section
subject: agentic AI
  topic: keywords
subject: autonomous agents
  topic: keywords
subject: AI agents
  topic: keywords
subject: agentic workflows
  topic: keywords
subject: enterprise AI adoption
  topic: keywords
subject: knowledge work automation
  topic: keywords
subject: reasoning engines
  topic: keywords
subject: multi-agent systems
  topic: keywords
creator: Asma Maheen
rights: Copyright (c) 2025 Euro Vantage journals of Artificial intelligence
date: 2025-11-10
publication date: 2025-11-10
starting page: 1
ending page: 6
doi: 10.65923/prwtn320

Workflow Automation with LLMs: Practical Guide for 2025
type: item
description: 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.
subject: Articles
  topic: section
subject: Large Language Models
  topic: keywords
subject: Workflow Automation
  topic: keywords
subject: AI Integration
  topic: keywords
subject: Process Optimization
  topic: keywords
subject: Ethical AI, Scalability
  topic: keywords
subject: Natural Language Processing
  topic: keywords
subject: Machine Learning Tools
  topic: keywords
subject: Business Efficiency
  topic: keywords
subject: Future Trends
  topic: keywords
creator: Talha Mughal
rights: Copyright (c) 2025 Euro Vantage journals of Artificial intelligence
date: 2025-10-15
publication date: 2025-10-15
starting page: 7
ending page: 13
doi: 10.65923/8dzh3s21

Metaheuristic Optimization-Assisted LSTM-DBN Model for Predictive Healthcare Analytics and Biomedical Informatics
type: item
description: 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.
subject: Articles
  topic: section
subject: Predictive Healthcare Analytics
  topic: keywords
subject: Biomedical Informatics
  topic: keywords
subject: LSTM
  topic: keywords
subject: Deep Belief Network
  topic: keywords
subject: Metaheuristic Optimization
  topic: keywords
subject: Tree Growth Algorithm
  topic: keywords
subject: Tunicate Swarm Optimization
  topic: keywords
subject: Temporal Modeling
  topic: keywords
creator: Noman Mazher
creator: Suneela Kanwal
rights: Copyright (c) 2025
date: 2025-10-21
publication date: 2025-10-21
starting page: 33
ending page: 42

A Unified Deep Learning Framework for Predictive Healthcare Analytics Using TJO-Based Feature Selection and Tree Growth Optimized Graph-Temporal Modeling
type: item
description: 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.
subject: Articles
  topic: section
subject: Biomedical Informatics
  topic: keywords
subject: Predictive Analytics
  topic: keywords
subject: Graph Neural Networks
  topic: keywords
subject: Tree Growth Optimization
  topic: keywords
subject: Temporal Modeling
  topic: keywords
subject: LSTM
  topic: keywords
subject: Healthcare Forecasting
  topic: keywords
creator: Suneela Kanwal
rights: Copyright (c) 2025
date: 2025-11-03
publication date: 2025-11-03
starting page: 24
ending page: 32

Blockchain-Based Cybersecurity Frameworks for Secure Government Communications
type: item
description: 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.
subject: Articles
  topic: section
subject: Blockchain
  topic: keywords
subject: government communications
  topic: keywords
subject: cybersecurity
  topic: keywords
subject: decentralized identity
  topic: keywords
subject: smart contracts
  topic: keywords
subject: secure messaging
  topic: keywords
subject: auditability
  topic: keywords
subject: resilience
  topic: keywords
creator: Atika Nishat
creator: Ifrah Ikram
rights: Copyright (c) 2025
date: 2024-11-24
publication date: 2024-11-24
starting page: 82
ending page: 89

Automated Machine Learning (AutoML): The Path to Self-Evolving Models
type: item
description: 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.
subject: Articles
  topic: section
subject: AutoML
  topic: keywords
subject: Neural Architecture Search
  topic: keywords
subject: Meta-Learning
  topic: keywords
subject: Hyperparameter Optimization
  topic: keywords
subject: Self-Evolving Models
  topic: keywords
subject: Reinforcement Learning
  topic: keywords
subject: Bayesian Optimization
  topic: keywords
subject: , Scalable AI
  topic: keywords
subject: Automated Data Science
  topic: keywords
subject: Explainable AI
  topic: keywords
creator: Hadia Azmat
creator: Anas Raheem
rights: Copyright (c) 2025
date: 2024-12-22
publication date: 2024-12-22
starting page: 75
ending page: 81

Enhancing Cybersecurity Defenses Against AI-Generated Deepfake Videos: A Framework for Real-Time Detection and Mitigation
type: item
description: 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.
subject: Articles
  topic: section
subject: AI generated deepfakes
  topic: keywords
subject: cybersecurity
  topic: keywords
subject: real-time
  topic: keywords
subject: deep learning
  topic: keywords
subject: framework of mitigation
  topic: keywords
subject: video forensics
  topic: keywords
subject: multimodal analysis.
  topic: keywords
creator: Sravan Komar Reddy Pullamma
rights: Copyright (c) 2025
date: 2025-10-17
publication date: 2025-10-17
starting page: 14
ending page: 23

Workflow Automation and Process Optimization through Sales force Platforms
type: item
description: 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.
subject: Articles
  topic: section
subject: Salesforce
  topic: keywords
subject: Workflow Automation
  topic: keywords
subject: Process Optimization
  topic: keywords
subject: CRM
  topic: keywords
subject: Digital Transformation
  topic: keywords
subject: Salesforce Flow
  topic: keywords
subject: Einstein AI
  topic: keywords
subject: Process Builder
  topic: keywords
subject: Business Efficiency
  topic: keywords
subject: Cloud Computing
  topic: keywords
creator: Emma Johnson
rights: Copyright (c) 2025
date: 2025-08-19
publication date: 2025-08-19
starting page: 81
ending page: 89

From Data to Insight: Decoding Student Cognition with Explainable AI Models
type: item
description: 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.
subject: Articles
  topic: section
subject: Explainable Artificial Intelligence
  topic: keywords
subject: Student Cognition
  topic: keywords
subject: Educational Data Mining
  topic: keywords
subject: SHAP
  topic: keywords
subject: LIME
  topic: keywords
subject: Cognitive Assessment
  topic: keywords
subject: Personalized Learning
  topic: keywords
subject: Interpretability.
  topic: keywords
creator: Rohan Sharma
creator: Aarav Sharma
rights: Copyright (c) 2025
date: 2025-08-20
publication date: 2025-08-20
starting page: 65
ending page: 72

Artificial Intelligence in Pulp Vitality Assessment: A Novel Approach to Diagnostic Decision-Making
type: item
description: 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.
subject: Articles
  topic: section
subject: Artificial intelligence
  topic: keywords
subject: pulp vitality
  topic: keywords
subject: diagnostic decision-making
  topic: keywords
subject: endodontics
  topic: keywords
subject: machine learning
  topic: keywords
subject: dental diagnostics
  topic: keywords
creator: Dr Mahendra Katariya
rights: Copyright (c) 2025
date: 2024-09-11
publication date: 2024-09-11
starting page: 65
ending page: 74

AI-Powered Classification of Root Canal Irrigation Efficiency Based on Image Analysis
type: item
description: 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.
subject: Articles
  topic: section
subject: Artificial intelligence
  topic: keywords
subject: root canal irrigation
  topic: keywords
subject: image analysis
  topic: keywords
subject: convolutional neural networks
  topic: keywords
subject: endodontics
  topic: keywords
subject: classification
  topic: keywords
subject: irrigation efficiency
  topic: keywords
creator: Dr Divya Gupta
rights: Copyright (c) 2025
date: 2024-11-07
publication date: 2024-11-07
starting page: 37
ending page: 43

Towards Transparent Learning Analytics: A Study on Explainable AI in Cognitive Skill Prediction
type: item
description: 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.
subject: Articles
  topic: section
subject: Explainable Artificial Intelligence
  topic: keywords
subject: Learning Analytics
  topic: keywords
subject: Cognitive Skill Prediction
  topic: keywords
subject: SHAP
  topic: keywords
subject: LIME
  topic: keywords
subject: Educational Data Mining
  topic: keywords
subject: Interpretability
  topic: keywords
subject: Transparent AI.
  topic: keywords
creator: Zhang Lei
creator: Kim Min Joon
rights: Copyright (c) 2025
date: 2025-09-05
publication date: 2025-09-05
starting page: 57
ending page: 64

Ethical AI and QA-Driven Cybersecurity Risk Mitigation for Critical Infrastructure
type: item
description: 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.
subject: Articles
  topic: section
subject: Ethical AI
  topic: keywords
subject: Quality Assurance
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Critical Infrastructure
  topic: keywords
subject: Risk Mitigation
  topic: keywords
subject: AI Governance
  topic: keywords
subject: Threat Detection
  topic: keywords
subject: Resilience
  topic: keywords
creator: Mojisola Aderonke Ojuri
rights: Copyright (c) 2025
date: 2025-01-09
publication date: 2025-01-09
starting page: 60
ending page: 75

AI-Enabled Predictive Maintenance for Industrial Equipment: Enhancing Reliability and Reducing Downtime
type: item
description: 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.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Predictive Maintenance
  topic: keywords
subject: Industrial Equipment
  topic: keywords
subject: Downtime Reduction
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Industry 4.0
  topic: keywords
subject: Internet of Things (IoT)
  topic: keywords
creator: Parth Desai
rights: Copyright (c) 2025
date: 2025-09-10
publication date: 2025-09-10
starting page: 27
ending page: 48

Explainable Fraud and Health Anomaly Detection via SMOTE-Enhanced Deep Models
type: item
description: 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 integ…
subject: Articles
  topic: section
subject: Fraud Detection
  topic: keywords
subject: Healthcare Anomaly Detection
  topic: keywords
subject: SMOTE
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Explainable AI
  topic: keywords
subject: Cross-Domain Modeling.
  topic: keywords
creator: Max Bannett
creator: Felix Wagner
rights: Copyright (c) 2025
date: 2025-08-08
publication date: 2025-08-08
starting page: 8
ending page: 26

Modeling the Impact of Climate Risk on Agricultural Productivity and Food Prices
type: item
description: 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.
subject: Articles
  topic: section
subject: Climate risk
  topic: keywords
subject: agricultural productivity
  topic: keywords
subject: food prices
  topic: keywords
subject: econometric modeling
  topic: keywords
subject: climate simulation
  topic: keywords
subject: crop yields
  topic: keywords
subject: food security
  topic: keywords
subject: adaptive policy
  topic: keywords
creator: Zillay Huma
rights: Copyright (c) 2025
date: 2025-02-14
publication date: 2025-02-14
starting page: 52
ending page: 59

Economic Impact of Deforestation on Indigenous Communities and Ecosystem Services in the Amazon
type: item
description: 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.
subject: Articles
  topic: section
subject: Deforestation
  topic: keywords
subject: Amazon
  topic: keywords
subject: Indigenous Communities
  topic: keywords
subject: Ecosystem Services
  topic: keywords
subject: Economic Impact
  topic: keywords
subject: Forest Degradation
  topic: keywords
subject: Socio-economic Displacement
  topic: keywords
subject: Sustainability
  topic: keywords
creator: Ifrah Ikram
rights: Copyright (c) 2025
date: 2025-02-04
publication date: 2025-02-04
starting page: 44
ending page: 52

The Economics of Plastic Pollution: Policy Instruments for Reducing Single-Use Plastics
type: item
description: 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.
subject: Articles
  topic: section
subject: Plastic Pollution
  topic: keywords
subject: Single-Use Plastics
  topic: keywords
subject: Environmental Economics
  topic: keywords
subject: Market Failure
  topic: keywords
subject: Policy Instruments
  topic: keywords
subject: Plastic Tax
  topic: keywords
subject: Circular Economy
  topic: keywords
subject: Sustainability
  topic: keywords
creator: Zillay Huma
rights: Copyright (c) 2025
date: 2025-05-03
publication date: 2025-05-03
starting page: 133
ending page: 143

Water Pricing and Allocation Policies: Economic Efficiency versus Equity in Water-Scarce Regions
type: item
description: 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.
subject: Articles
  topic: section
subject: Water scarcity
  topic: keywords
subject: water pricing
  topic: keywords
subject: allocation policies
  topic: keywords
subject: economic efficiency
  topic: keywords
subject: equity
  topic: keywords
subject: water governance
  topic: keywords
subject: simulation
  topic: keywords
subject: public policy
  topic: keywords
creator: Ifrah Ikram
rights: Copyright (c) 2025
date: 2025-04-22
publication date: 2025-04-22
starting page: 124
ending page: 132

The Influence of Earnings Management on Earnings Quality and Corporate Sustainability in GCC Companies
type: item
description: 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.
subject: Articles
  topic: section
subject: Earnings Management
  topic: keywords
subject: Earnings Quality
  topic: keywords
subject: Corporate Sustainability
  topic: keywords
subject: GCC Companies
  topic: keywords
subject: Financial Transparency
  topic: keywords
subject: ESG
  topic: keywords
subject: Accrual Manipulation
  topic: keywords
subject: Real Activities Manipulation
  topic: keywords
subject: Governance
  topic: keywords
subject: Sustainable Reporting
  topic: keywords
creator: Anas Raheem
creator: Noman Mazher
rights: Copyright (c) 2025
date: 2025-02-10
publication date: 2025-02-10
starting page: 36
ending page: 43

Financial Facades and Sustainable Realities: Earnings Management across Distressed and Healthy GCC Enterprises
type: item
description: 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.
subject: Articles
  topic: section
subject: Earnings Management
  topic: keywords
subject: GCC Enterprises
  topic: keywords
subject: Distressed Firms
  topic: keywords
subject: Financial Reporting
  topic: keywords
subject: Real Earnings Manipulation
  topic: keywords
subject: Accruals
  topic: keywords
subject: Sustainability
  topic: keywords
subject: Corporate Governance
  topic: keywords
subject: Transparency
  topic: keywords
subject: Investor Protection
  topic: keywords
creator: Asma Maheen
creator: Minal junaid
rights: Copyright (c) 2025
date: 2025-01-04
publication date: 2025-01-04
starting page: 28
ending page: 35

Targeting HIV Prevention Inequities: A Pilot ACT-Based Prep Intervention for Young Black MSM in the Southern United States
type: item
description: 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.
subject: Articles
  topic: section
subject: Pre-exposure prophylaxis
  topic: keywords
subject: ACT
  topic: keywords
subject: HIV prevention
  topic: keywords
subject: Black MSM
  topic: keywords
subject: Southern United States
  topic: keywords
subject: stigma
  topic: keywords
subject: behavioral intervention
  topic: keywords
subject: pilot study
  topic: keywords
creator: Asma Maheen
creator: Areeba Sohail
rights: Copyright (c) 2025
date: 2025-07-12
publication date: 2025-07-12
starting page: 1
ending page: 7

High-Precision Fault Identification in Motor Bearings via Enhanced Multi-Kernel ELM Framework
type: item
description: 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.
subject: Articles
  topic: section
subject: Motor bearings
  topic: keywords
subject: fault identification
  topic: keywords
subject: multi-kernel ELM
  topic: keywords
subject: high precision
  topic: keywords
subject: vibration analysis
  topic: keywords
subject: machine learning
  topic: keywords
subject: Gaussian kernel
  topic: keywords
subject: polynomial kernel
  topic: keywords
subject: classification accuracy
  topic: keywords
creator: Sania Naveed
creator: Asma Maheen
rights: Copyright (c) 2025
date: 2024-12-23
publication date: 2024-12-23
starting page: 28
ending page: 36

Hybrid Kernel-Driven ELM for Intelligent Fault Detection in Motor Rolling Bearings
type: item
description: 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.
subject: Articles
  topic: section
subject: Hybrid Kernel
  topic: keywords
subject: Extreme Learning Machine
  topic: keywords
subject: Fault Detection
  topic: keywords
subject: Motor Bearings
  topic: keywords
subject: Intelligent Diagnostics
  topic: keywords
subject: Vibration Analysis
  topic: keywords
subject: Machine Learning
  topic: keywords
creator: Areeba Sohail
creator: Eshal Nasir
rights: Copyright (c) 2025
date: 2024-12-20
publication date: 2024-12-20
starting page: 19
ending page: 27