Tree-Based Algorithms and Incremental Feature Optimization for Fault Detection and Diagnosis in Photovoltaic Systems by Khaled Chahine

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Enhanced data-driven shear strength predictive modeling framework for RCDBs using explainable boosting-based ensemble learning algorithms coupled with Bayesian optimization by Imad Shakir Abbood, Noorhazlinda Abd Rahman, B.H. Abu Bakar

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A Hybrid Machine Learning Approach for Predicting Power Transformer Failures Using Internet of Things-Based Monitoring and Explainable Artificial Intelligence by Emrah Aslan, Yildirim Ozupak, Feyyaz Alpsalaz, Zakaria M. S. Elbarbary

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A New Computer-Aided Diagnosis System for Breast Cancer Detection from Thermograms Using Metaheuristic Algorithms and Explainable AI by Hanane Dihmani, Abdelmajid Bousselham, Omar Bouattane

“…In this study, a novel interpretable computer aided diagnosis (CAD) system for breast cancer detection is proposed, leveraging Explainable Artificial Intelligence (XAI) throughout its various phases. …”
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In‐Situ Rheology Measurements via Machine‐Learning Enhanced Direct‐Ink‐Writing by Robert D. Weeks, Jennifer M. Ruddock, J. Daniel Berrigan, Jennifer A. Lewis, James. O. Hardin

“…The behavior of this model is verified and analyzed with explainable artificial intelligence tools, linking printed feature importance to one's known physical understanding of the process.…”
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Recent advances in explainable Machine Learning models for wildfire prediction by Abira Sengupta, Brendon J. Woodford

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SEGT-GO: a graph transformer method based on PPI serialization and explanatory artificial intelligence for protein function prediction by Yansong Wang, Yundong Sun, Baohui Lin, Haotian Zhang, Xiaoling Luo, Yumeng Liu, Xiaopeng Jin, Dongjie Zhu

“…Based on game theory, the SHAP eXplainable Artificial Intelligence (XAI) framework optimizes model input and filters out feature noise, enhancing model performance. …”
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User perspectives on AI explainability in aerospace manufacturing: a Card-Sorting study by Sofia Morandini, Federico Fraboni, Mark Hall, Santiago Quintana-Amate, Luca Pietrantoni

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Explainable Boosting Machines Identify Key Metabolomic Biomarkers in Rheumatoid Arthritis by Fatma Hilal Yagin, Cemil Colak, Abdulmohsen Algarni, Ali Algarni, Fahaid Al-Hashem, Luca Paolo Ardigò

“…Despite these recent developments, an explainable artificial intelligence (XAI)-based methodology has not been used to identify RA metabolomic biomarkers and distinguish patients with RA. …”
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Multiclass skin lesion classification and localziation from dermoscopic images using a novel network-level fused deep architecture and explainable artificial intelligence by Mehak Arshad, Muhammad Attique Khan, Nouf Abdullah Almujally, Areej Alasiry, Mehrez Marzougui, Yunyoung Nam

“…The goal is to address challenges like irregular lesion shapes, inter-class similarities, and class imbalances while providing explainability through artificial intelligence. Methods A novel hybrid contrast enhancement technique was applied for pre-processing and dataset augmentation. …”
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EnsembleXAI-Motor: A Lightweight Framework for Fault Classification in Electric Vehicle Drive Motors Using Feature Selection, Ensemble Learning, and Explainable AI by Md. Ehsanul Haque, Mahe Zabin, Jia Uddin

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Explainable AI for Lightweight Network Traffic Classification Using Depthwise Separable Convolutions by Mustafa Ghaleb, Mosab Hamdan, Abdulaziz Y. Barnawi, Muhammad Gambo, Abubakar Danasabe, Saheed Bello, Aliyu Habib

“…In addition, we have integrated eXplainable Artificial Intelligence techniques, specifically LIME and SHAP, to provide valuable insights into model predictions. …”
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A Clinically Interpretable Approach for Early Detection of Autism Using Machine Learning With Explainable AI by Oishi Jyoti, Hafsa Binte Kibria, Zareen Tasnim Pear, Md Nahiduzzaman, Md. Faysal Ahamed, Khandaker Reajul Islam, Jaya Kumar, Muhammad E. H. Chowdhury

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An explainable analytical approach to heart attack detection using biomarkers and nature-inspired algorithms by Maithri Bairy, Krishnaraj Chadaga, Niranjana Sampathila, R. Vijaya Arjunan, G. Muralidhar Bairy

“…This study used five explainable artificial intelligence techniques (XAI) to ensure that predictions made by the model are understandable and interpretable to facilitate clinical decisions. …”
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LISE: A Logic-Based Interactive Similarity Explainer for Clusters of RDF Data by Simona Colucci, Francesco Maria Donini, Verdiana Schena, Floriano Scioscia, Eugenio Di Sciascio

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XAIHO: explainable AI leveraging hybrid optimized framework for liver cirrhosis detection by Prashant Kumar Mishra, Brijesh Kumar Chaurasia, Man Mohan Shukla

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Development and clinical application of an automated machine learning-based delirium risk prediction model for emergency polytrauma patients by Zhenyi Liu, Yihao Huang, Long Li, Yisha Xu, Peng Wu, Zhigang Zhang, Tingyong Han, Liangjie Zhang, Ming Zhang

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The assessment of soil variables relative importance for cereal yield prediction under rainfed cropping system in Morocco by Oumnia Ennaji, Abdellah Hamma, Leonardus Vergütz, Achraf El Allali

“…In this study, Explainable Artificial Intelligence (XAI) techniques were applied to identify the most important factors influencing crop yield prediction, with a focus on strategies for sustainable agriculture. …”
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Explainable Model of Fusion Network With Enhanced Optimization Approach for Tuberculosis Diagnosis by C. R. Dhivyaa, K. Nithya, C. Sathiya Kumar, R. Sudhakar

“…The second approach utilizes the EAMSO (Ensemble of AMS Optimization model) for feature selection. It combines the results from AEO (Artificial Ecosystem-based Optimization), MBO (Monarch butterfly optimization), and Seagull-Algorithm to form three optimized feature subsets. …”
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A Data-Driven Intelligent Methodology for Developing Explainable Diagnostic Model for Febrile Diseases by Constance Amannah, Kingsley Friday Attai, Faith-Michael Uzoka

“…This study presents a prototype diagnostic framework integrating machine learning (ML) and explainable artificial intelligence (XAI) to enhance diagnostic performance, interpretability, and usability in resource-constrained settings. …”
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