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.

Provides an introduction to XAI and its integration with deep learning Covers case studies and examples to illustrate how XAI can be implemented in the real world Presents new concepts, cutting-edge research, frameworks, tools that facilitate comprehension of deep learning models

Inhalt

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.