Interpretable artificial intelligence for modulated metasurface antenna design using SHAP and MLP by Amrollah Amini, Ali Moshiri, Mohammad Amin Chaychi Zadeh, Vahid Nayyeri

“…In this work, we propose an interpretable artificial intelligence framework that integrates SHapley Additive exPlanations (SHAP) with a multi-layer perceptron (MLP) to predict two key radiation metrics: sidelobe level (SLL) and half-power beamwidth (HPBW). …”
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End-to-End Stroke Imaging Analysis Using Effective Connectivity and Interpretable Artificial Intelligence by Wojciech Ciezobka, Joan Falco-Roget, Cemal Koba, Alessandro Crimi

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Interpretable artificial intelligence model for predicting heart failure severity after acute myocardial infarction by Chenglong Guo, Binyu Gao, Xuexue Han, Tianxing Zhang, Tianqi Tao, Jinggang Xia, Honglei Liu

“…This study aimed to develop an interpretable artificial intelligence (AI) model for HF severity prediction using multidimensional clinical data. …”
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An interpretable artificial intelligence framework for designing synthetic lethality-based anti-cancer combination therapies by Jing Wang, Yuqi Wen, Yixin Zhang, Zhongming Wang, Yuyang Jiang, Chong Dai, Lianlian Wu, Dongjin Leng, Song He, Xiaochen Bo

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Interpretable artificial intelligence (AI) for cervical cancer risk analysis leveraging stacking ensemble and expert knowledge by Priyanka Roy, Mahmudul Hasan, Md Rashedul Islam, Md Palash Uddin

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A comprehensive evaluation of interpretable artificial intelligence for epileptic seizure diagnosis using an electroencephalogram: A systematic review by Daraje Kaba Gurmessa, Worku Jimma

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Blood glucose level prediction in type 1 diabetes: A comparative analysis of interpretable artificial intelligence approaches by Ilaria Basile, Giovanna Sannino

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Explainable artificial intelligence for energy efficiency in experimental refrigeration Systems: Advanced cutting-edge sunflower optimization by Mehmet Das, Oguzhan Pektezel, Cebrail Barut, Gungor Yildirim, Bilal Alatas

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Target Material Property‐Dependent Cluster Analysis of Inorganic Compounds by Nobuya Sato, Akira Takahashi, Shin Kiyohara, Kei Terayama, Ryo Tamura, Fumiyasu Oba

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Blastocyst selection through an interpretable artificial intelligence method versus traditional morphology grading: study protocol for a randomised controlled trial by Lei Chen, Yu Sun, Shanshan Wang, Chen Sun, Hang Liu, Guanqiao Shan, Haixiang Sun

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Using Interpretable Artificial Intelligence Algorithms in the Management of Blunt Splenic Trauma: Applications of Optimal Policy Trees as a Treatment Prescription Aid to Improve Patient Mortality by Vahe S. Panossian, Yu Ma, Bolin Song, Jefferson A. Proaño-Zamudio, Veerle P. C. van Zon, Ikemsinachi C. Nzenwa, Azadeh Tabari, George C. Velmahos, Haytham M. A. Kaafarani, Dimitris Bertsimas, Dania Daye

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XAI Unveiled: Revealing the Potential of Explainable AI in Medicine: A Systematic Review by Noemi Scarpato, Patrizia Ferroni, Fiorella Guadagni

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A Spectral Interpretable Bearing Fault Diagnosis Framework Powered by Large Language Models by Panfeng Bao, Wenjun Yi, Yue Zhu, Yufeng Shen, Haotian Peng

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Current Signature-Based Bearing Fault Severity Classification Using a Robust Multilevel Cascaded Framework by Korawege N. C. Jayasena, Battur Batkhishig, Babak Nahid-Mobarakeh, Ali Emadi

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Machine learning based shear strength prediction in reinforced concrete beams using Levy flight enhanced decision trees by Aybike Özyüksel Çiftçioğlu, Anıl Delikanlı, Torkan Shafighfard, Faramarz Bagherzadeh

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Interpretable Reinforcement Learning for Sequential Strategy Prediction in Language-Based Games by Jun Zhao, Jintian Ji, Robail Yasrab, Shuxin Wang, Liang Yu, Lingzhen Zhao

Subjects: “…interpretable artificial intelligence…”
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MEN: leveraging explainable multimodal encoding network for precision prediction of CYP450 inhibitors by Abena Achiaa Atwereboannah, Wei-Ping Wu, Mugahed A. Al-antari, Sophyani B. Yussif, Chukwuebuka J. Ejiyi, Edwin K. Tenagyei, Grace-Mercure B. Kissanga, Gyarteng S. A. Emmanuel, Yeong Hyeon Gu, Emmanuel Ahene

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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

“…The transparency and interpretability of the modeling results are enhanced by combining the Shapley Additive Explanations (SHAP) with interpretable artificial intelligence. Model performance is evaluated on a test set by several statistical metrics, which shows that the optimized random forest model performs best and outperforms SVM, XGBoost, and LightGBM. …”
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The DynAIRx Project Protocol: Artificial Intelligence for dynamic prescribing optimisation and care integration in multimorbidity by Lauren E Walker, Aseel S Abuzour, Danushka Bollegala, Andrew Clegg, Mark Gabbay, Alan Griffiths, Cecil Kullu, Gary Leeming, Frances S Mair, Simon Maskell, Samuel Relton, Roy A Ruddle, Eduard Shantsila, Matthew Sperrin, Tjeerd Van Staa, Alan Woodall, Iain Buchan

“…Objective To extract information from scattered clinical records on how health and medications change over time, apply interpretable artificial intelligence (AI) approaches to predict risks of poor outcomes and overlay this information on care records to inform SMRs. …”
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Explainable quality assessment of effective aligned skeletal representations for martial arts movements by multi-machine learning decisions by Yiqun Pang, Kaiqi Zhang, Fengmei Li

“…In addition, this study is the first to use interpretable artificial intelligence to provide feedback for teaching and learning Wushu. …”
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