Distributed Deep Learning and Explainable AI (XAI) in Industry 4.0

Lalitha Krishnasamy

ISBN 10: 3031946367 / ISBN 13: 9783031946363

Published by Springer, Berlin, Springer Nature Switzerland, Springer, 2025

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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.

Seller Inventory # 9783031946363

Bibliographic Details

• Title: Distributed Deep Learning and Explainable AI (XAI) in Industry 4.0
• Publisher: Springer, Berlin, Springer Nature Switzerland, Springer
• Publication Date: 2025
• Language: English
• ISBN 10: 3031946367
• ISBN 13: 9783031946363
• Binding: Buch
• Condition: Neu
• Dimensions: 235W x 155H x 35D millimeters 235 millimeters width by 155 millimeters height by 35 millimeters depth

About this title

Synopsis

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.