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There is a growing interest in the application of machine learning algorithms in chemical safety and health-related model development, with applications in areas including property and toxicity prediction, consequence prediction, and fault detection. This book is the first to review the current status of machine learning implementation in chemical safety and health research and to provide guidance for implementing machine learning techniques and algorithms into chemical safety and health research.Machine Learning in Chemical Safety and Health serves as an essential guide on both the fundamentals and applications of machine learning for industry professionals and researchers in the fields of process safety, chemical safety, occupational and environmental health, and industrial hygiene.
Machine Learning Fundamentals
Flammability Characteristics Prediction Using QSPR Modeling
Consequence Prediction Using Quantitative Property–Consequence Relationship Models
Machine Learning in Process Safety and Asset Integrity Management
Machine Learning for Process Fault Detection and Diagnosis
Intelligent Method for Chemical Emission Source Identification
Machine Learning and Deep Learning Applications in Medical Image Analysis
Predictive Nanotoxicology: Nanoinformatics Approach to Toxicity Analysis of Nanomaterials
Machine Learning in Environmental Exposure Assessment
Air Quality Prediction Using Machine Learning
Current Challenges and Perspectives