Machine learning in industry [electronic resource]

• 其他作者：
  • Datta, Shubhabrata.
  • Davim, J. Paulo.
  • SpringerLink (Online service)
• 其他題名：
  • Management and industrial engineering.
• 出版： Cham : Springer International Publishing :Imprint: Springer
• 叢書名： Management and industrial engineering,
• 主題： Machine learning. , Artificial intelligence--Industrial applications. , Industrial and Production Engineering. , Machine Learning.
• ISBN： 9783030758479 (electronic bk.) 、 9783030758462 (paper)
• FIND@SFXID: CGU
• 資料類型: 電子書
• 內容註: 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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• 摘要註: 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.
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