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Explainable edge AIa futuristic computing perspective /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Explainable edge AIedited by Aboul Ella Hassanien ... [et al.].
Reminder of title:	a futuristic computing perspective /
other author:	Hassanien, Aboul Ella.
Published:	Cham :Springer International Publishing :2023.
Description:	x, 181 p. :ill. (chiefly color), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Artificial intelligence.
Online resource:	https://doi.org/10.1007/978-3-031-18292-1
ISBN:	9783031182921$q(electronic bk.)

Explainable edge AIa futuristic computing perspective /
Explainable edge AIa futuristic computing perspective /[electronic resource] :edited by Aboul Ella Hassanien ... [et al.]. - Cham :Springer International Publishing :2023. - x, 181 p. :ill. (chiefly color), digital ;24 cm. - Studies in computational intelligence,v. 10721860-9503 ;. - Studies in computational intelligence ;v. 216..

Explainable Artificial Intelligence: Concepts and Current Progression -- Explainable Artificial Intelligence (XAI): Understanding and Future Perspectives -- Explainable Artificial Intelligence (XAI): Conception, Visualization and Assessment approaches towards amenable XAI -- Explainable AI (XAI): A Survey of Current and Future Opportunities -- Recent Challenges on Edge AI with its application: A Brief Introduction -- Explainable Artificial Intelligence in Health Care: How XAI Improves User Trust in High-Risk Decisions -- Role of Explainable Edge AI to Resolve Real Time Problem -- Explainable Data Fusion on Edge: Challenges and Opportunities -- Trust Model Based Data Fusion in Explainable Artificial Intelligence for Edge Computing Using Secure Sequential Discriminant Auto Encoder with Lightweight Optimization Algorithm -- A Deep Learning Based Target Coverage Protocol for Edge Computing Enabled Wireless Sensor Networks.

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 will facilitate enhanced prediction accuracy with the comprehensible decision and traceability of actions performed at the edge and have a significant impact on futuristic computing scenarios. This book is highly relevant to graduate/postgraduate students, academicians, researchers, engineers, professionals, and other personnel working in artificial intelligence, machine learning, and intelligent systems.

ISBN: 9783031182921$q(electronic bk.)

Standard No.: 10.1007/978-3-031-18292-1doiSubjects--Topical Terms:

194058
Artificial intelligence.

LC Class. No.: Q335

Dewey Class. No.: 006.3

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505 0 $a Explainable Artificial Intelligence: Concepts and Current Progression -- Explainable Artificial Intelligence (XAI): Understanding and Future Perspectives -- Explainable Artificial Intelligence (XAI): Conception, Visualization and Assessment approaches towards amenable XAI -- Explainable AI (XAI): A Survey of Current and Future Opportunities -- Recent Challenges on Edge AI with its application: A Brief Introduction -- Explainable Artificial Intelligence in Health Care: How XAI Improves User Trust in High-Risk Decisions -- Role of Explainable Edge AI to Resolve Real Time Problem -- Explainable Data Fusion on Edge: Challenges and Opportunities -- Trust Model Based Data Fusion in Explainable Artificial Intelligence for Edge Computing Using Secure Sequential Discriminant Auto Encoder with Lightweight Optimization Algorithm -- A Deep Learning Based Target Coverage Protocol for Edge Computing Enabled Wireless Sensor Networks.
520 $a 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 will facilitate enhanced prediction accuracy with the comprehensible decision and traceability of actions performed at the edge and have a significant impact on futuristic computing scenarios. This book is highly relevant to graduate/postgraduate students, academicians, researchers, engineers, professionals, and other personnel working in artificial intelligence, machine learning, and intelligent systems.
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