國立虎尾科技大學 |

Machine Learning in Industry

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Machine Learning in Industry/ edited by Shubhabrata Datta, J. Paulo Davim.
other author:	Datta, Shubhabrata.
Description:	X, 197 p. 83 illus., 71 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Industrial engineering. -
Online resource:	https://doi.org/10.1007/978-3-030-75847-9
ISBN:	9783030758479

Machine Learning in Industry
Machine Learning in Industry[electronic resource] /edited by Shubhabrata Datta, J. Paulo Davim. - 1st ed. 2022. - X, 197 p. 83 illus., 71 illus. in color.online resource. - Management and Industrial Engineering,2365-0540. - Management and Industrial Engineering,.

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.

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.

ISBN: 9783030758479

Standard No.: 10.1007/978-3-030-75847-9doiSubjects--Topical Terms:

679492
Industrial engineering.

LC Class. No.: T55.4-60.8

Dewey Class. No.: 670

Machine Learning in Industry
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