A comprehensive review of machine learning for heart disease prediction: challenges, trends, ethical considerations, and future directions by Raman Kumar, Raman Kumar, Sarvesh Garg, Rupinder Kaur, M. G. M. Johar, Sehijpal Singh, Sehijpal Singh, Soumya V. Menon, Pulkit Kumar, Pulkit Kumar, Ali Mohammed Hadi, Shams Abbass Hasson, Jasmina Lozanović

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Artificial intelligence-based prediction of second stage duration in labor: a multicenter retrospective cohort analysisResearch in context by Xiaoqing Huang, Xiaodan Di, Suiwen Lin, Minrong Yao, Suijin Zheng, Shuyi Liu, Wayan Lau, Zhixin Ye, Zilian Wang, Bin Liu

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Machine learning model for random forest acute oral toxicity prediction by A.M. Elsayad, M.M. Zeghid, K.A. Elsayad, A.N. Khan, ِA.K.M. Baareh, A. Sadiq, S.A. Mukhtar, H.F. Ali, S. Abd El-kader

“…A surrogate decision tree developed from random forests predictions reached an area under the curve of 0.929.CONCLUSION: Random forest models effectively predicted acute oral toxicity, particularly when addressing class imbalance through cost-sensitive learning and resampling. leveraging explainable artificial intelligence techniques, including permutation feature importance, surrogate decision tree analysis and local interpretable model-agnostic explanations, this study identified key molecular descriptors driving toxicity. …”
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Harnessing AI to revolutionize photocatalytic degradation of Tetracycline via optimized UV/ZrO2/NaOCl reaction pathways by Amir Sheikhmohammadi, Saeed Hosseinpour, Zahra Jalilzadeh, Hossein Azarpira, Mahmood Yousefi

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Building Information Modeling and AI Algorithms for Optimizing Energy Performance in Hot Climates: A Comparative Study of Riyadh and Dubai by Mohammad H. Mehraban, Aljawharah A. Alnaser, Samad M. E. Sepasgozar

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Explainable correlation-based anomaly detection for Industrial Control Systems by Ermiyas Birihanu, Imre Lendák

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Artificial intelligence based classification and prediction of medical imaging using a novel framework of inverted and self-attention deep neural network architecture by Junaid Aftab, Muhammad Attique Khan, Sobia Arshad, Shams ur Rehman, Dina Abdulaziz AlHammadi, Yunyoung Nam

“…GradCAM, an explainable artificial intelligence (XAI) approach, analyzes custom models. …”
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Prediction of pulmonary embolism by an explainable machine learning approach in the real world by Qiao Zhou, Ruichen Huang, Xingyu Xiong, Zongan Liang, Wei Zhang

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XAI-FruitNet: An explainable deep model for accurate fruit classification by Shirin Sultana, Md All Moon Tasir, S.M. Nuruzzaman Nobel, Md Mohsin Kabir, M.F. Mridha

“…Through rigorous experimentation, we demonstrate that XAI-FruitNet advances state-of-the-art fruit classification accuracy and sets a new standard for explainable artificial intelligence (XAI) in agricultural applications. …”
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Fuzzy evaluation and explainable machine learning for diagnosis of rheumatic and autoimmune diseases by Mohammed Fadhil Mahdi, Arezoo Jahani, Dhafar Hamed Abd

“…The FDOSM process involves assessments from three domain experts to ensure a robust and well-rounded evaluation. Furthermore, the explainable artificial intelligence (XAI) technique provides global and local explanations for model predictions. …”
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Interpretable Machine Learning for Explaining and Predicting Collapse Hazards in the Changbai Mountain Region by Xiangyang He, Qiuling Lang, Jiquan Zhang, Yichen Zhang, Qingze Jin, Jinyuan Xu

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Enhancing smart city sustainability with explainable federated learning for vehicular energy control by Khalid Ishaq Abdullah Almaazmi, Saif Jasim Almheiri, Muhammad Adnan Khan, Asghar Ali Shah, Sagheer Abbas, Munir Ahmad

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Stress management with HRV following AI, semantic ontology, genetic algorithm and tree explainer by Ayan Chatterjee, Michael A. Riegler, K. Ganesh, Pål Halvorsen

“…These optimized features play a crucial role in developing a stress management system within a Semantic framework. …”
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A Federated Explainable AI Framework for Smart Agriculture: Enhancing Transparency, Efficiency, and Sustainability by Hassam Ahmed Tahir, Walaa Alayed, Waqar Ul Hassan

“…This paper presents a comprehensive framework for integrating Explainable Artificial Intelligence (XAI) into smart agriculture to address challenges in transparency, interpretability, and trust associated with AI-driven decision-making. …”
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Reliable Autism Spectrum Disorder Diagnosis for Pediatrics Using Machine Learning and Explainable AI by Insu Jeon, Minjoong Kim, Dayeong So, Eun Young Kim, Yunyoung Nam, Seungsoo Kim, Sehoon Shim, Joungmin Kim, Jihoon Moon

“…<b>Background:</b> As the demand for early and accurate diagnosis of autism spectrum disorder (ASD) increases, the integration of machine learning (ML) and explainable artificial intelligence (XAI) is emerging as a critical advancement that promises to revolutionize intervention strategies by improving both accuracy and transparency. …”
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Multimodal AI/ML for discovering novel biomarkers and predicting disease using multi-omics profiles of patients with cardiovascular diseases by William DeGroat, Habiba Abdelhalim, Elizabeth Peker, Neev Sheth, Rishabh Narayanan, Saman Zeeshan, Bruce T. Liang, Zeeshan Ahmed

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Half-hourly electricity price prediction model with explainable-decomposition hybrid deep learning approach by Sujan Ghimire, Ravinesh C. Deo, Konstantin Hopf, Hangyue Liu, David Casillas-Pérez, Andreas Helwig, Salvin S. Prasad, Jorge Pérez-Aracil, Prabal Datta Barua, Sancho Salcedo-Sanz

“…The final prediction combines the trend, seasonal, and residual components’ predictions. Explainable Artificial Intelligence (xAI) methods were used to enhance model interpretability and trustworthiness, with optimization via the Optuna algorithm. …”
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Behavioural user segmentation of app users based on functionality interaction patterns by Li-Yoong Ooi, Choo-Yee Ting, Helmi Zakariah, Eashvaren Chandar

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Pest classification: Explainable few-shot learning vs. convolutional neural networks vs. transfer learning by Nitiyaa Ragu, Jason Teo

“…By advancing both the detection capabilities and interpretability of Artificial Intelligence (AI) systems, this research provides a novel contribution to smart agriculture, enabling robust pest detection systems tailored to real-world, data-scarce scenarios.…”
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Toward Robust Lung Cancer Diagnosis: Integrating Multiple CT Datasets, Curriculum Learning, and Explainable AI by Amira Bouamrane, Makhlouf Derdour, Akram Bennour, Taiseer Abdalla Elfadil Eisa, Abdel-Hamid M. Emara, Mohammed Al-Sarem, Neesrin Ali Kurdi

“…Finally, explainable artificial intelligence (XAI) using Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to better understand the model. …”
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