國立高雄大學圖資館 |

Language: English

Back

Estimating ore grade using evolutionary machine learning models

Record Type:	Electronic resources : Monograph/item
Title/Author:	Estimating ore grade using evolutionary machine learning modelsby Mohammad Ehteram ... [et al.].
other author:	Ehteram, Mohammad.
Published:	Singapore :Springer Nature Singapore :2023.
Description:	xiii, 101 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	OresSampling and estimation
Online resource:	https://doi.org/10.1007/978-981-19-8106-7
ISBN:	9789811981067$q(electronic bk.)

Estimating ore grade using evolutionary machine learning models
Estimating ore grade using evolutionary machine learning models[electronic resource] /by Mohammad Ehteram ... [et al.]. - Singapore :Springer Nature Singapore :2023. - xiii, 101 p. :ill., digital ;24 cm.

Explains the importance of ore grade estimation -- Reviews machine learning models for ore grade estimation -- Explains the structure of different kinds of machine learning models -- Explains different training algorithms and optimization algorithms. This chapter also explains the structure of evolutionary machine learning models -- Explains the Bayesian model averaging and multilayer perceptron networks for estimating AL2O3 grade in a mine -- Explains the structure of inclusive multiple models and optimized radial basis function neural networks for estimating Sio2 grade in a mine -- Explains the application of hybrid kriging and extreme learning machine models for estimating copper ore grade in a mine -- Explains the application of optimized group machine data handling, support vector machines, and Adaptive neuro-fuzzy interface systems for estimating iron ore grade in mines -- Presents the conclusion, general comments, and suggestions for the next books.

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.

ISBN: 9789811981067$q(electronic bk.)

Standard No.: 10.1007/978-981-19-8106-7doiSubjects--Topical Terms:

953371
Ores
--Sampling and estimation

LC Class. No.: TN560

Dewey Class. No.: 622.0285631

Estimating ore grade using evolutionary machine learning models
LDR:03607nmm a2200325 a 4500 001 644699
003 DE-He213
005 20221217115806.0
006 m d
007 cr nn 008mamaa
008 231208s2023 si s 0 eng d
020 $a 9789811981067$q(electronic bk.)
020 $a 9789811981050$q(paper)
024 7 $a 10.1007/978-981-19-8106-7 $2 doi
035 $a 978-981-19-8106-7
040 $a GP $c GP
041 0 $a eng
050 4 $a TN560
072 7 $a RBGG $2 bicssc
072 7 $a SCI048000 $2 bisacsh
072 7 $a RBGG $2 thema
082 0 4 $a 622.0285631 $2 23
090 $a TN560 $b .E81 2023
245 0 0 $a Estimating ore grade using evolutionary machine learning models $h [electronic resource] / $c by Mohammad Ehteram ... [et al.].
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2023.
300 $a xiii, 101 p. : $b ill., digital ; $c 24 cm.
505 0 $a Explains the importance of ore grade estimation -- Reviews machine learning models for ore grade estimation -- Explains the structure of different kinds of machine learning models -- Explains different training algorithms and optimization algorithms. This chapter also explains the structure of evolutionary machine learning models -- Explains the Bayesian model averaging and multilayer perceptron networks for estimating AL2O3 grade in a mine -- Explains the structure of inclusive multiple models and optimized radial basis function neural networks for estimating Sio2 grade in a mine -- Explains the application of hybrid kriging and extreme learning machine models for estimating copper ore grade in a mine -- Explains the application of optimized group machine data handling, support vector machines, and Adaptive neuro-fuzzy interface systems for estimating iron ore grade in mines -- Presents the conclusion, general comments, and suggestions for the next books.
520 $a 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.
650 0 $a Ores $x Sampling and estimation $x Data processing. $3 953371
650 0 $a Machine learning. $3 188639
650 1 4 $a Mineralogy. $3 273728
650 2 4 $a Machine Learning. $3 833608
650 2 4 $a Data Science. $3 913495
650 2 4 $a Artificial Intelligence. $3 212515
700 1 $a Ehteram, Mohammad. $3 951038
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-981-19-8106-7
950 $a Earth and Environmental Science (SpringerNature-11646)