Machine Learning in Industry / edited by Shubhabrata Datta, J. Paulo Davim

Contributor(s):

Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Management and Industrial EngineeringPublisher: Cham : Springer International Publishing, 2022Publisher: Cham : Imprint: Springer, 2022Edition: 1st ed. 2022Description: 1 Online-Ressource(X, 197 p. 83 illus., 71 illus. in color.)ISBN:

9783030758479

Subject(s):

670 23

DOI: DOI: 10.1007/978-3-030-75847-9Online resources:

Summary: Fundamentals of Machine learning -- Neural network model identification studies to predict residual stress of a steel plate based on a non-destructive Barkhausen noise measurement -- Data Driven Optimization of Blast Furnace Iron Making Process Using Evolutionary Deep Learning -- A brief appraisal of machine learning in industrial sensing probes -- Mining the genesis of sliver defects through Rough and Fuzzy Set Theories.Summary: This book covers different machine learning techniques such as artificial neural network, support vector machine, rough set theory and deep learning. It points out the difference between the techniques and their suitability for specific applications. This book also describes different applications of machine learning techniques for industrial problems. The book includes several case studies, helping researchers in academia and industries aspiring to use machine learning for solving practical industrial problems.PPN: PPN: 1765223946Package identifier: Produktsigel: ZDB-2-ENG | ZDB-2-SXE | ZDB-2-SEB | BSZ-2-SN-Auswahl

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