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Titlebook: Explainable AI: Foundations, Methodologies and Applications; Mayuri Mehta,Vasile Palade ,Indranath Chatterjee Book 2023 The Editor(s) (if

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發(fā)表于 2025-3-21 18:15:08 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Explainable AI: Foundations, Methodologies and Applications
編輯Mayuri Mehta,Vasile Palade ,Indranath Chatterjee
視頻videohttp://file.papertrans.cn/320/319284/319284.mp4
概述Written for beginners and advanced machine learning users, including engineers and researchers on AI and applications.Covers concepts such as black box models, transparency, interpretable machine lear
叢書(shū)名稱Intelligent Systems Reference Library
圖書(shū)封面Titlebook: Explainable AI: Foundations, Methodologies and Applications; Mayuri Mehta,Vasile Palade ,Indranath Chatterjee Book 2023 The Editor(s) (if
描述.This book presents an overview and several applications of explainable artificial intelligence (XAI). It covers different aspects related to explainable artificial intelligence, such as the need to make the AI models interpretable, how black box machine/deep learning models can be understood using various XAI methods, different evaluation metrics for XAI, human-centered explainable AI, and applications of explainable AI in health care, security surveillance, transportation, among other areas..The book is suitable for students and academics aiming to build up their background on explainable AI and can guide them in making machine/deep learning models more transparent. The book can be used as a reference book for teaching a graduate course on artificial intelligence, applied machine learning, or neural networks. Researchers working in the area of AI can use this book to discover the recent developments in XAI. Besides its use in academia, this book could be used by practitioners in AI industries, healthcare industries, medicine, autonomous vehicles, and security surveillance, who would like to develop AI techniques and applications with explanations..
出版日期Book 2023
關(guān)鍵詞Intelligent Systems; Artificial Intelligence; Explainable AI; Neural Networks; Deep Learning; Applied Mac
版次1
doihttps://doi.org/10.1007/978-3-031-12807-3
isbn_softcover978-3-031-12809-7
isbn_ebook978-3-031-12807-3Series ISSN 1868-4394 Series E-ISSN 1868-4408
issn_series 1868-4394
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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發(fā)表于 2025-3-21 22:02:19 | 只看該作者
Black Box Models for eXplainable Artificial Intelligence,o multiple small options for the IDS area. This chapter aims to implement the arrangement of issues labeled in the various black box methods. This survey helps the researcher to understand the classification of various black box models.
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Methods and Metrics for Explaining Artificial Intelligence Models: A Review, For clarity on the XAI implementation stage, Pre-model, In-model, and Post-model explainability are elaborated along with the model-agnostic and model-specific techniques. The chapter concludes with a brief discussion on a simple use-case of implementing the XAI method in a real-life problem follow
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Explainable Machine Learning for Autonomous Vehicle Positioning Using SHAP, is a safety critical one and thus requires a qualitative assessment of the reasons for the predictions of the WhONet model at any point of use. There is therefore the need to provide explanations for the WhONet’s predictions to justify its reliability and thus provide a higher level of transparency
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An Overview of Explainable AI Methods, Forms and Frameworks,
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