Machine Learning in Industry - Datta, Shubhabrata; Davim, J. Paulo; SpringerLink (Online service) (Cham : Springer International Publishing : Imprint: Springer, 2022)
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.
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.
Keywords: Industrial engineering; Production engineering; Machine learning; Industrial and Production Engineering; Machine Learning; Artificial intelligence -- Industrial applications; Machine learning