Titlebook: Machine Learning in Industry; Shubhabrata Datta,J. Paulo Davim Book 2022 The Editor(s) (if applicable) and The Author(s), under exclusive

Enzyme

2365-0532 ent 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.978-3-030-75849-3978-3-030-75847-9Series ISSN 2365-0532 Series E-ISSN 2365-0540

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2365-0532 ndustrial problemsThis 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 differ

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Neural Network Model Identification Studies to Predict Residual Stress of a Steel Plate Based on a ion performance. It was also found that the genetic algorithm proposed earlier by the authors is applicable for selecting a well-generalizing model to the system, but the performance of the deterministic selection techniques is also comparable to the genetic algorithm.

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N斯巴達(dá)人

Mining the Genesis of Sliver Defects Through Rough and Fuzzy Set Theories,mbership functions. A rule base thus evolves in the form of a fuzzy inference system constituting a few important variables, which serves as a perceptive model for predicting the severity of sliver defects in cold rolled steel. Validation of the fuzzy system is done using actual industrial trials.

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