https://mhco.ca/qt8a2yb6tm6 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 […]

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Machine Learning in Industry

Length: 207 pages
Edition: 1
Language: English
Publisher: Springer
Publication Date: 2021-07-25
ISBN-10: 303075846X
ISBN-13: 9783030758462
Sales Rank: #0 (See Top 100 Books)

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here 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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go here Preface Contents About the Editors Fundamentals of Machine Learning 1 Introduction 2 Artificial Intelligence 3 Data Analytics 3.1 Types of Data Analytics 3.2 Data Mining 4 Big Data 5 Supervised Learning 6 Unsupervised Learning 7 Reinforcement Learning 8 Decision Tree 9 Least Squares 10 Linear Regression 11 Neural Networks 12 Cluster Analysis 13 Deep Learning 14 Summary References Neural Network Model Identification Studies to Predict Residual Stress of a Steel Plate Based on a Non-destructive Barkhausen Noise Measurement 1 Introduction 2 Model Identification 2.1 Feature Extraction 2.2 Feature Selection 2.3 Neural Network Structure Selection 2.4 Final Model Training 2.5 Model Performance Assessment 3 Considered Data Set 4 Results and Discussion 4.1 The Selected Features 4.2 Final Model Selection and Model Performance Assessment 5 Concluding Remarks and Recommendations References Data-Driven Optimization of Blast Furnace Iron Making Process Using Evolutionary Deep Learning 1 Background 1.1 Challenges in Multi-objective Optimization by Evolutionary Techniques 2 Blast Furnace 2.1 Blast Furnace Modeling and Optimization 3 Evolutionary Data-Driven Models 3.1 Predator-Prey Genetic Algorithm (PPGA) 3.2 Evolutionary Neural Network (EvoNN) 3.3 Bi-Objective Genetic Programming (BioGP) 3.4 Evolutionary Deep Neural Network (EvoDN2) 3.5 Reference Vector Evolutionary Algorithm (RVEA) 4 Handling a Real-World Blast Furnace Iron Making Problem Using Evolutionary Approaches 4.1 Objective and Scope 4.2 Data Preparation 4.3 Time Series Modeling 4.4 Many Objectives Optimization Formulation 4.5 Construction of Metamodels 4.6 Training and the Correlation Coefficients 4.7 Single Variable Response (SVR) 4.8 Optimization Work 4.9 Result Analysis 5 Conclusion and Future Prospect References A Brief Appraisal of Machine Learning in Industrial Sensing Probes 1 Introduction 2 Functional Flowchart of ML 2.1 Data Pretreatment 2.2 Pattern Recognition and Dimension Reduction 2.3 Machine Modeling 3 Survey 4 Final Remarks and Recommendations References Mining the Genesis of Sliver Defects Through Rough and Fuzzy Set Theories 1 Introduction 2 Dataset 3 Methodology 3.1 p-value 3.2 Rough Set Model 3.3 Fuzzy Inference System (FIS) 4 Results and Discussion 4.1 p-value Results 4.2 Rough Set Results 4.3 Development of FIS Model 4.4 FIS Prediction 5 Experimental Trials for Validation 6 Conclusion References Machine Learning Studies in Materials Science 1 Introduction: The 4th Paradigm of Science in the 4th Industrial Revolution 2 Methods 3 Materials–Processes–Knowledge Formalization 4 Materials 5 Processes 6 Formalization of Knowledge: Development of Knowledge Bases and Semantic Integration 7 Conclusions References Accurate, Real-Time Replication of Governing Equations of Physical Systems with Transpose CNNs — for Industry 4.0 and Digital Twins 1 Introduction 2 Related Work and Significance in Industry 2.1 Related Developments 2.2 Transpose Convolutions 2.3 Real-Time Mapping from Sensors to Field Interior Values 3 Application Domains for Demonstration of Concepts 3.1 Compressible Potential Flow Over a Flat Plate at Incidence 3.2 Strong Shocks and Axial Drag Resolution over ONERA-M6 Wing 4 Principles of CNN Architectures for Relevant Application Domains 4.1 Architectural Principles 4.2 Presentation of Results 5 Conclusions References Deep Learning in Vision-Based Automated Inspection: Current State and Future Prospects 1 Introduction to Computer Vision and Machine Vision 1.1 Vision-Based Automated Inspection 2 Introduction to Deep Learning 2.1 Traditional Vision Versus Deep Learning for Vision-Based Inspection 3 Deep Learning Methods in Machine Vision 3.1 Challenges in Adopting Deep Learning for Machine Vision 3.2 Enablers of DL in Machine Vision 3.3 Deep Learning-Based Machine Vision Project Pipeline 3.4 Deep Learning Hardware 3.5 Deep Learning Software 4 Industries Using Deep Learning-Based Machine Vision 5 Future Prospects 6 5G Technology References Performance Improvement in Hot Rolling Process with Novel Neural Architectural Search 1 Introduction 2 Formulation 2.1 Evolutionary Neural Architecture Search for Optimal ANN Design 2.2 Hot Rolling Process Description and Data Generation 3 Results and Discussions 3.1 Data Pre-processing 3.2 Neural Architecture Search 3.3 Discussions and Future Scope 4 Conclusions References