An Interpretable Machine Learning Procedure Which Unravels Hidden Interplanetary Drivers of the Low Latitude Dayside Magnetopause by Sheng Li, Yang‐Yi Sun, Chieh‐Hung Chen

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The Potential of SHAP and Machine Learning for Personalized Explanations of Influencing Factors in Myopic Treatment for Children by Jun-Wei Chen, Hsin-An Chen, Tzu-Chi Liu, Tzu-En Wu, Chi-Jie Lu

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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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Identification of maize kernel varieties based on interpretable ensemble algorithms by Chunguang Bi, Chunguang Bi, Xinhua Bi, Jinjing Liu, Hao Xie, Shuo Zhang, He Chen, Mohan Wang, Lei Shi, Lei Shi, Shaozhong Song

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Integrating radiological and clinical data for clinically significant prostate cancer detection with machine learning techniques by Luis Mariano Esteban, Ángel Borque-Fernando, Maria Etelvina Escorihuela, Javier Esteban-Escaño, Jose María Abascal, Pol Servian, Juan Morote

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Internet of things-driven approach integrated with explainable machine learning models for ship fuel consumption prediction by Van Nhanh Nguyen, Nghia Chung, G.N. Balaji, Krzysztof Rudzki, Anh Tuan Hoang

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Impact of Operational and Financial Efficiency on Aviation Stock Prices: A Machine Learning Model with SHAP Interpretability by Ahmet Akusta

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