TY  - BOOK
AU  - Datta,Shubhabrata
AU  - Davim,J.Paulo
ED  - SpringerLink (Online service)
TI  - Machine Learning in Industry
T2  - Management and Industrial Engineering,
SN  - 9783030758479
AV  - TA347.A78 
PY  - 2022///
CY  - Cham
PB  - Springer International Publishing, Imprint: Springer
KW  - Industrial engineering
KW  - Production engineering
KW  - Machine learning
KW  - Industrial and Production Engineering
KW  - Machine Learning
KW  - Artificial intelligence -- Industrial applications
N1  - 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
N2  - 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
UR  - https://doi.org/10.1007/978-3-030-75847-9
ER  -