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This book examines the abilities of new machine learning models for
predicting ore grade in mining engineering. A variety of case
studies are examined in this book. A motivation for preparing this
book was the absence of robust models for estimating ore grade.
Models of current books can also be used for the different sciences
because they have high capabilities for estimating different
variables. Mining engineers can use the book to determine the ore
grade accurately. This book helps identify mineral-rich regions for
exploration and exploitation. Exploration costs can be decreased by
using the models in the current book. In this book, the author
discusses the new concepts in mining engineering, such as
uncertainty in ore grade modeling. Ensemble models are presented in
this book to estimate ore grade. In the book, readers learn how to
construct advanced machine learning models for estimating ore
grade. The authors of this book present advanced and hybrid models
used to estimate ore grade instead of the classic methods such as
kriging. The current book can be used as a comprehensive handbook
for estimating ore grades. Industrial managers and modelers can use
the models of the current books. Each level of ore grade modeling
is explained in the book. In this book, advanced optimizers are
presented to train machine learning models. Therefore, the book can
also be used by modelers in other fields. The main motivation of
this book is to address previous shortcomings in the modeling
process of ore grades. The scope of this book includes mining
engineering, soft computing models, and artificial intelligence.
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