| Description |
1 online resource (322 pages) |
| Contents |
Cover -- Title Page -- Copyright Page -- Contents -- List of Contributors -- Preface -- Chapter 1 Introduction -- 1.1 Background -- 1.2 Current State -- 1.2.1 Flammability Characteristics Prediction Using Quantitative Structure-Property Relationship -- 1.2.2 Consequence Prediction Using Quantitative Property-Consequence Relationship -- 1.2.3 Machine Learning in Process Safety and Asset Integrity Management -- 1.2.4 Machine Learning for Process Fault Detection and Diagnosis -- 1.2.5 Intelligent Method for Chemical Emission Source Identification -- 1.2.6 Machine Learning and Deep Learning Applications in Medical Image Analysis -- 1.2.7 Predictive Nanotoxicology: Nanoinformatics Approach to Toxicity Analysis of Nanomaterials -- 1.2.8 Machine Learning in Environmental Exposure Assessment -- 1.2.9 Air Quality Prediction Using Machine Learning -- 1.3 Software and Tools -- 1.3.1 R -- 1.3.2 Python -- References -- Chapter 2 Machine Learning Fundamentals -- 2.1 What Is Learning? -- 2.1.1 Machine Learning Applications and Examples -- 2.1.2 Machine Learning Tasks -- 2.2 Concepts of Machine Learning -- 2.3 Machine Learning Paradigms -- 2.4 Probably Approximately Correct Learning -- 2.4.1 Deterministic Setting -- 2.4.2 Stochastic Setting -- 2.5 Estimation and Approximation -- 2.6 Empirical Risk Minimization -- 2.6.1 Empirical Risk Minimizer -- 2.6.2 VC-dimension Generalization Bound -- 2.6.3 General Loss Functions -- 2.7 Regularization -- 2.7.1 Regularized Loss Minimization -- 2.7.2 Constrained and Regularized Problem -- 2.7.3 Trade-off Between Estimation and Approximation Error -- 2.8 Maximum Likelihood Principle -- 2.8.1 Maximum Likelihood Estimation -- 2.8.2 Cross Entropy Minimization -- 2.9 Optimization -- 2.9.1 Linear Regression: An Example -- 2.9.2 Closed-form Solution -- 2.9.3 Gradient Descent -- 2.9.4 Stochastic Gradient Descent -- References |
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Chapter 3 Flammability Characteristics Prediction Using QSPR Modeling -- 3.1 Introduction -- 3.1.1 Flammability Characteristics -- 3.1.2 QSPR Application -- 3.1.2.1 Concept of QSPR -- 3.1.2.2 Trends and Characteristics of QSPR -- 3.2 Flowchart for Flammability Characteristics Prediction -- 3.2.1 Dataset Preparation -- 3.2.2 Structure Input and Molecular Simulation -- 3.2.3 Calculation of Molecular Descriptors -- 3.2.4 Preliminary Screening of Molecular Descriptors -- 3.2.5 Descriptor Selection and Modeling -- 3.2.6 Model Validation -- 3.2.6.1 Model Fitting Ability Evaluation -- 3.2.6.2 Model Stability Analysis -- 3.2.6.3 Model Predictivity Evaluation -- 3.2.7 Model Mechanism Explanation -- 3.2.8 Summary of QSPR Process -- 3.3 QSPR Review for Flammability Characteristics -- 3.3.1 Flammability Limits -- 3.3.1.1 LFLT and LFL -- 3.3.1.2 UFLT and UFL -- 3.3.2 Flash Point -- 3.3.3 Auto-ignition Temperature -- 3.3.4 Heat of Combustion -- 3.3.5 Minimum Ignition Energy -- 3.3.6 Gas-liquid Critical Temperature -- 3.3.7 Other Properties -- 3.4 Limitations -- 3.5 Conclusions and Future Prospects -- References -- Chapter 4 Consequence Prediction Using Quantitative Property-Consequence Relationship Models -- 4.1 Introduction -- 4.2 Conventional Consequence Prediction Methods -- 4.2.1 Empirical Method -- 4.2.2 Computational Fluid Dynamics (CFD) Method -- 4.2.3 Integral Method -- 4.3 Machine Learning and Deep Learning-Based Consequence Prediction Models -- 4.4 Quantitative Property-Consequence Relationship Models -- 4.4.1 Consequence Database -- 4.4.2 Property Descriptors -- 4.4.3 Machine Learning and Deep Learning Algorithms -- 4.5 Challenges and Future Directions -- References -- Chapter 5 Machine Learning in Process Safety and Asset Integrity Management -- 5.1 Opportunities and Threats -- 5.2 State-of-the-Art Reviews -- 5.2.1 Artificial Neural Networks (ANNs) |
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5.2.2 Principal Component Analysis (PCA) -- 5.2.3 Genetic Algorithm (GA) -- 5.3 Case Study of Asset Integrity Assessment -- 5.4 Data-Driven Model of Asset Integrity Assessment -- 5.4.1 Condition Monitoring Data Collection -- 5.4.2 Data Processing and Storage -- 5.4.3 Data Mining for Risk Quantification and Monitoring Control -- 5.4.4 AIM Application -- 5.4.5 The Application of the Framework -- 5.5 Conclusion -- References -- Chapter 6 Machine Learning for Process Fault Detection and Diagnosis -- 6.1 Background -- 6.2 Machine Learning Approaches in Fault Detection and Diagnosis -- 6.3 Supervised Methods for Fault Detection and Diagnosis -- 6.3.1 Neural Network -- 6.3.1.1 Neural Network Theory and Algorithm -- 6.3.1.2 Neural Network Learning for Fault Classification -- 6.3.1.3 Algorithm for Fault Classification Using Neural Network -- 6.3.2 Support Vector Machine -- 6.3.2.1 Support Vector Machine Theory and Algorithm -- 6.3.3 Support Vector Machine Model Selection and Algorithm -- 6.3.4 Support Vector Machine Multiclass Classification -- 6.4 Unsupervised Learning Models for Fault Detection and Diagnosis -- 6.4.1 K-Nearest Neighbors -- 6.4.2 One-Class Support Vector Machine -- 6.4.3 One-Class Neural Network -- 6.4.4 Comparison Between Deep Learning with Machine Learning in Fault Detection and Diagnosis -- 6.5 Intelligent FDD Using Machine Learning -- 6.5.1 Model Development -- 6.5.2 Data Collection -- 6.5.2.1 Model Development Steps -- 6.5.2.2 Result Comparison -- 6.6 Concluding Remarks -- References -- Chapter 7 Intelligent Method for Chemical Emission Source Identification -- 7.1 Introduction -- 7.1.1 Development of Detecting Gas Emission -- 7.1.2 Development of Source Term Identification -- 7.2 Intelligent Methods for Recognizing Gas Emission -- 7.2.1 Leakage Recognition of Sequestrated CO2 in the Atmosphere |
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7.2.1.1 Gas Leakage Recognition for CO2 Geological Sequestration -- 7.2.1.2 Case Studies for CO2 Recognition -- 7.2.2 Emission Gas Identification with Artificial Olfactory -- 7.2.2.1 Features of Responses in AOS -- 7.2.2.2 Support Vector Machine Models for Gas Identification -- 7.2.2.3 Deep Learning Models for Gas Identification -- 7.3 Intelligent Methods for Identifying Emission Sources -- 7.3.1 Source Estimation with Intelligent Optimization Method -- 7.3.1.1 Principle of Source Estimation with Optimization Method -- 7.3.1.2 Case Studies of Source Estimation with Optimization Method -- 7.3.2 Source Estimation with MRE-PSO Method -- 7.3.2.1 Principle of PSO-MRE for Source Estimation -- 7.3.2.2 Case Studies -- 7.3.3 Source Estimation with PSO-Tikhonov Regulation Method -- 7.3.3.1 Principle of PSO-Tikhonov Regularization Hybrid Method -- 7.3.3.2 Case Study -- 7.3.4 Source Estimation with MCMC-MLA Method -- 7.3.4.1 Forward Gas Dispersion Model Based on MLA -- 7.3.4.2 Source Estimation with MCMC-MLA Method -- 7.3.4.3 Case Study -- 7.4 Conclusions and Future Work -- 7.4.1 Conclusions -- 7.4.2 Limitations and Future Work -- References -- Chapter 8 Machine Learning and Deep Learning Applications in Medical Image Analysis -- 8.1 Introduction -- 8.1.1 Machine Learning in Medical Imaging -- 8.1.2 Deep Learning in Medical Imaging -- 8.2 CNN-Based Models for Classification -- 8.2.1 ResNet50 -- 8.2.2 YOLOv4 (Darknet53) -- 8.2.3 Grad-CAM -- 8.3 Case Study -- 8.3.1 Background -- 8.3.2 Study Design -- 8.3.3 Training and Testing Database Preparation -- 8.3.4 Results -- 8.3.4.1 Classification Performance of the Modified ResNet50 Model -- 8.3.4.2 Classification Performance of the YOLOv4 Model -- 8.3.4.3 Post-Processing Via Grad-CAM Model and HSV -- 8.3.5 Conclusion -- 8.4 Limitations and Future Work -- References |
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Chapter 9 Predictive Nanotoxicology: Nanoinformatics Approach to Toxicity Analysis of Nanomaterials -- 9.1 Predictive Nanotoxicology -- 9.1.1 Introduction -- 9.1.2 Nano Quantitative Structure-Activity Relationship (QSAR) -- 9.1.3 Importance of Data for Nanotoxicology -- 9.2 Machine Learning Modeling for Predictive Nanotoxicology -- 9.2.1 Overview -- 9.2.2 Unsupervised Learning -- 9.2.2.1 Data Exploration Via Self-Organizing Maps (SOMs) -- 9.2.2.2 Evaluating Associations among Sublethal Toxicity Responses -- 9.2.3 Supervised Learning -- 9.2.3.1 Random Forest Models -- 9.2.3.2 Support Vector Machines -- 9.2.3.3 Bayesian Networks -- 9.2.3.4 Supervised Classification and Regression-Based Models for Nano-(Q)SARs -- 9.2.4 Predictive Nano-(Q)SARs for the Assessment of Causal Relationships -- 9.3 Development of Machine Learning Based Models for Nano-(Q)SARs -- 9.3.1 Overview -- 9.3.1.1 Data-Driven Models -- 9.3.1.2 Mechanistic/Theoretical Models -- 9.3.2 Data Generation, Collection, and Preprocessing -- 9.3.3 Descriptor Selection -- 9.3.4 Model Selection and Training -- 9.3.5 Model Validation -- 9.3.5.1 Descriptor Importance -- 9.3.5.2 Applicability Domain -- 9.3.6 Model Diagnosis and Debugging -- 9.4 Nanoinformatics Approaches to Predictive Nanotoxicology -- 9.5 Summary -- References -- Chapter 10 Machine Learning in Environmental Exposure Assessment -- 10.1 Introduction -- 10.2 Environmental Exposure Modeling -- 10.3 Machine Learning Exposure Models -- 10.4 Model Evaluation -- 10.5 Case Study -- 10.6 Other Topics -- 10.6.1 Bias and Fairness -- 10.6.2 Wearable Sensors -- 10.6.3 Interpretability -- 10.6.4 Extreme Events -- 10.7 Conclusion -- References -- Chapter 11 Air Quality Prediction Using Machine Learning -- 11.1 Introduction -- 11.2 Air Quality and Climate Data Acquisition -- 11.2.1 Earth Satellite Observation Datasets |
| Notes |
11.2.1.1 Basics of Earth Satellite Observations |
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Description based on publisher supplied metadata and other sources |
| Form |
Electronic book
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| Author |
Cai, Changjie
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| ISBN |
1119817498 |
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9781119817499 |
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