{
"item_title" : "Machine Learning in Industry",
"item_author" : [" Shubhabrata Datta", "J. Paulo Davim "],
"item_description" : "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.",
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Overview
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.
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Details
- ISBN-13: 9783030758462
- ISBN-10: 303075846X
- Publisher: Springer
- Publish Date: July 2021
- Dimensions: 9.21 x 6.14 x 0.5 inches
- Shipping Weight: 1.03 pounds
- Page Count: 197
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