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Distributed deep learning and explai...

Krishnasamy, Lalitha.

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Distributed deep learning and explainable AI (XAI) in Industry 4.0

Record Type:	Electronic resources : Monograph/item
Title/Author:	Distributed deep learning and explainable AI (XAI) in Industry 4.0 / edited by Lalitha Krishnasamy ... [et al.].
other author:	Krishnasamy, Lalitha.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	vi, 424 p. :ill. (chiefly col.), digital ;24 cm.
[NT 15003449]:	Introduction to Industry 4.0: Practical issues and challenges -- Explainable AI Principles for Industry 4.0 -- Explainable AI principles of building industry 4.0 -- Transformative Healthcare: Industry 4.0 Integration of Distributed Deep Learning and XAI.
Contained By:	Springer Nature eBook
Subject:	Artificial intelligence - Industrial applications. -
Online resource:	https://doi.org/10.1007/978-3-031-94637-0
ISBN:	9783031946370

Distributed deep learning and explainable AI (XAI) in Industry 4.0
Distributed deep learning and explainable AI (XAI) in Industry 4.0 [electronic resource] /edited by Lalitha Krishnasamy ... [et al.]. - Cham :Springer Nature Switzerland :2025. - vi, 424 p. :ill. (chiefly col.), digital ;24 cm. - Information systems engineering and management,v. 553004-9598 ;. - Information systems engineering and management ;v. 55..

Introduction to Industry 4.0: Practical issues and challenges -- Explainable AI Principles for Industry 4.0 -- Explainable AI principles of building industry 4.0 -- Transformative Healthcare: Industry 4.0 Integration of Distributed Deep Learning and XAI.

This book is a comprehensive resource that delves into the integration of advanced artificial intelligence techniques within the context of modern industrial practices. It systematically explores how distributed deep learning methodologies can be effectively combined with explainable AI to enhance transparency in Industry 4.0 applications. In recent years, neural networks and other deep learning models have produced remarkable outcomes in a variety of fields, including image recognition, natural language processing, and decision-making. Concerns have been raised regarding the transparency and interpretability of these models as a result of their increasing intricacy. The demand for methodologies and approaches associated with explainable artificial intelligence (XAI) has consequently increased. The primary aim of XAI is to enhance the transparency and comprehensibility of deep learning model decision-making processes for stakeholders, irrespective of their technical expertise.

ISBN: 9783031946370

Standard No.: 10.1007/978-3-031-94637-0doiSubjects--Topical Terms:

653318
Artificial intelligence
--Industrial applications.

LC Class. No.: TA347.A78

Dewey Class. No.: 670.28563

Distributed deep learning and explainable AI (XAI) in Industry 4.0
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245 0 0 $a Distributed deep learning and explainable AI (XAI) in Industry 4.0 $h [electronic resource] / $c edited by Lalitha Krishnasamy ... [et al.].
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2025.
300 $a vi, 424 p. : $b ill. (chiefly col.), digital ; $c 24 cm.
490 1 $a Information systems engineering and management, $x 3004-9598 ; $v v. 55
505 0 $a Introduction to Industry 4.0: Practical issues and challenges -- Explainable AI Principles for Industry 4.0 -- Explainable AI principles of building industry 4.0 -- Transformative Healthcare: Industry 4.0 Integration of Distributed Deep Learning and XAI.
520 $a This book is a comprehensive resource that delves into the integration of advanced artificial intelligence techniques within the context of modern industrial practices. It systematically explores how distributed deep learning methodologies can be effectively combined with explainable AI to enhance transparency in Industry 4.0 applications. In recent years, neural networks and other deep learning models have produced remarkable outcomes in a variety of fields, including image recognition, natural language processing, and decision-making. Concerns have been raised regarding the transparency and interpretability of these models as a result of their increasing intricacy. The demand for methodologies and approaches associated with explainable artificial intelligence (XAI) has consequently increased. The primary aim of XAI is to enhance the transparency and comprehensibility of deep learning model decision-making processes for stakeholders, irrespective of their technical expertise.
650 0 $a Artificial intelligence $x Industrial applications. $3 653318
650 0 $a Deep learning (Machine learning) $3 3538509
650 0 $a Industry 4.0. $3 3491401
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650 2 4 $a Data Engineering. $3 3409361
650 2 4 $a Artificial Intelligence. $3 769149
700 1 $a Krishnasamy, Lalitha. $3 3791749
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830 0 $a Information systems engineering and management ; $v v. 55. $3 3791750
856 4 0 $u https://doi.org/10.1007/978-3-031-94637-0
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)