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Titlebook: Explainable Edge AI: A Futuristic Computing Perspective; Aboul Ella Hassanien,Deepak Gupta,Ankit Garg Book 2023 The Editor(s) (if applicab

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發(fā)表于 2025-3-21 17:43:38 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Explainable Edge AI: A Futuristic Computing Perspective
編輯Aboul Ella Hassanien,Deepak Gupta,Ankit Garg
視頻videohttp://file.papertrans.cn/320/319296/319296.mp4
概述Presents explainability in edge AI, an amalgamation of edge computing and AI.Presents the fundamental concepts of explainable artificial intelligence (XAI).Describes the concept of explainable edge AI
叢書(shū)名稱(chēng)Studies in Computational Intelligence
圖書(shū)封面Titlebook: Explainable Edge AI: A Futuristic Computing Perspective;  Aboul Ella Hassanien,Deepak Gupta,Ankit Garg Book 2023 The Editor(s) (if applicab
描述.This book presents explainability in edge AI, an amalgamation of edge computing and AI. The issues of transparency, fairness, accountability, explainability, interpretability, data-fusion, and comprehensibility that are significant for edge AI are being addressed in this book through explainable models and techniques. The concept of explainable edge AI is new in front of the academic and research community, and consequently, it will undoubtedly explore multiple research dimensions. The book presents the concept of explainability in edge AI which is the amalgamation of edge computing and AI. In the futuristic computing scenario, the goal of explainable edge AI will be to execute the AI tasks and produce explainable results at the edge. First, this book explains the fundamental concepts of explainable artificial intelligence (XAI), then it describes the concept of explainable edge AI, and finally, it elaborates on the technicalities of explainability in edge AI. Owing to the quick transition in the current computing scenario and integration with the latest AI-based technologies, it is significant to facilitate people-centric computing through explainable edge AI. Explainable edge AI
出版日期Book 2023
關(guān)鍵詞Computational Intelligence; Explainable Edge AI; XAI; Machine Learning; Deep Learning; Fairness; Accountab
版次1
doihttps://doi.org/10.1007/978-3-031-18292-1
isbn_softcover978-3-031-18294-5
isbn_ebook978-3-031-18292-1Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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https://doi.org/10.1007/978-1-4614-9035-7AI is used in every field where AI can be used but with some modification or by adding some techniques of XAI as “SHAP (Shaply Additive exPlanations), DeepSHAP, DeepLIFT, CXplain, and LIME”. The main goal of this chapter is to provide a brief overview of XAI by covering almost every aspect of XAI. T
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https://doi.org/10.1007/978-0-387-85731-2ery difficult to understand. It sometimes becomes a very hard task for the domain experts too to understand the ML algorithms of the black block models, so the need for the development of this type of technology was felt. Many times, results are developed with very high accuracy are quite easy to un
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發(fā)表于 2025-3-22 15:24:53 | 只看該作者
Satellite in Real (Perturbed) Orbit,riteria. The Edge AI community, which spans several ICT, engineering, and perform computer science subfields, researches unique machine learning techniques for the edge computing environment. The objective is to offer a hypothetical roadmap that may unite important players and enablers in order to t
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Michael S. Ritsner,Irving I. Gottesmanstomer service. The best example of explainable edge AI is virtual assistants such as Alexa, google assistant. They learn from the user’s world and phrases and can store them directly on the device. These are just a few examples later, and we have possible applications in future works on the explain
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發(fā)表于 2025-3-23 02:51:59 | 只看該作者
George Foussias,Ofer Agid,Gary Remingtonof parameters, it is difficult to interpret the model creating a black box. The chapter aims to outline the explainability of data fusion at the edge. It highlights different data models of fusion, discusses a framework for AI and data fusion at the edge and identifies potential challenges and possi
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發(fā)表于 2025-3-23 08:50:43 | 只看該作者
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