This book is a collection of machine learning and deep learning algorithms, methods, architectures, and software tools that have been developed and widely applied in predictive toxicology. It compiles a set of recent applications using state-of-the-art machine learning and deep learning techniques in analysis of a variety of toxicological endpoint data. The contents illustrate those machine learning and deep learning algorithms, methods, and software tools and summarise the applications of machine learning and deep learning in predictive toxicology with informative text, figures, and tables...

This book provides an overview of computational methods based on peridynamics and nonlocal operators and their application to challenging numerical problems which are difficult to deal with traditional methods such as the finite element method, material failure being “only” one of them. The authors have also developed a higher-order nonlocal operator approaches capable of solving higher-order partial differential equations on arbitrary domains in higher-dimensional space with ease. This book is of interest to those in academia and industry.