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

Lalitha Krishnasamy

ISBN 10: 3031946367 / ISBN 13: 9783031946363

Editore: Springer, Berlin, Springer Nature Switzerland, Springer, 2025

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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.

Codice articolo 9783031946363

Dati bibliografici

• Titolo: Distributed Deep Learning and Explainable AI (XAI) in Industry 4.0
• Editore: Springer, Berlin, Springer Nature Switzerland, Springer
• Data di pubblicazione: 2025
• Lingua: Inglese
• ISBN 10: 3031946367
• ISBN 13: 9783031946363
• Rilegatura: Buch
• Condizione: Neu
• Dimensioni: 235 L x 155 A x 35 P (millimetri) 235 millimetri di larghezza per 155 millimetri di altezza per 35 millimetri di profondità

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Riassunto

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.