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Machine learning in industry

Datta, Shubhabrata.

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Machine learning in industry

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning in industry/ edited by Shubhabrata Datta, J. Paulo Davim.
other author:	Datta, Shubhabrata.
Published:	Cham :Springer International Publishing : : 2022.,
Description:	x, 197 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Machine learning. -
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. - Cham :Springer International Publishing :2022. - x, 197 p. :ill., digital ;24 cm. - Management and industrial engineering,2365-0532. - 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:

533906
Machine learning.

LC Class. No.: Q325.5 / .M33 2022

Dewey Class. No.: 006.31

Machine learning in industry
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245 0 0 $a Machine learning in industry $h [electronic resource] / $c edited by Shubhabrata Datta, J. Paulo Davim.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a x, 197 p. : $b ill., digital ; $c 24 cm.
490 1 $a Management and industrial engineering, $x 2365-0532
505 0 $a 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.
520 $a 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.
650 0 $a Machine learning. $3 533906
650 0 $a Artificial intelligence $x Industrial applications. $3 653318
650 1 4 $a Industrial and Production Engineering. $3 891024
650 2 4 $a Machine Learning. $3 3382522
700 1 $a Datta, Shubhabrata. $3 3380810
700 1 $a Davim, J. Paulo. $3 907796
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Management and industrial engineering. $3 2057681
856 4 0 $u https://doi.org/10.1007/978-3-030-75847-9
950 $a Engineering (SpringerNature-11647)