Estimating Ore Grade Using by Ehteram Mohammad (26 results)
Estimating Ore Grade Using Evolutionary Machine Learning Models [Hardcover] [Dec 27, 2022] Ehteram, Mohammad; Khozani, Zohreh Sheikh; Soltani-Mohammadi, Saeed and Abbaszadeh, Maliheh
Ehteram, Mohammad; Khozani, Zohreh Sheikh; Soltani-Mohammadi, Saeed; Abbaszadeh, Maliheh
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Estimating Ore Grade Using Evolutionary Machine Learning Models
Ehteram, Mohammad; Khozani, Zohreh Sheikh; Soltani-Mohammadi, Saeed; Abbaszadeh, Maliheh
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Estimating Ore Grade Using Evolutionary Machine Learning Models
Ehteram, Mohammad; Khozani, Zohreh Sheikh; Soltani-Mohammadi, Saeed; Abbaszadeh, Maliheh
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Estimating Ore Grade Using Evolutionary Machine Learning Models
Ehteram, Mohammad; Khozani, Zohreh Sheikh; Soltani-Mohammadi, Saeed; Abbaszadeh, Maliheh
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Estimating Ore Grade Using Evolutionary Machine Learning Models
Ehteram, Mohammad; Khozani, Zohreh Sheikh; Soltani-Mohammadi, Saeed; Abbaszadeh, Maliheh
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Estimating Ore Grade Using Evolutionary Machine Learning Models
Ehteram, Mohammad; Khozani, Zohreh Sheikh; Soltani-Mohammadi, Saeed; Abbaszadeh, Maliheh
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Estimating Ore Grade Using Evolutionary Machine Learning Models
Ehteram, Mohammad; Khozani, Zohreh Sheikh; Soltani-Mohammadi, Saeed; Abbaszadeh, Maliheh
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Taschenbuch. Condition: Neu. Estimating Ore Grade Using Evolutionary Machine Learning Models | Mohammad Ehteram (u. a.) | Taschenbuch | xiii | Englisch | 2024 | Springer | EAN 9789811981081 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | A…nbieter: preigu.
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 estimatin…g 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 oregrade 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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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 g…rade. 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 oregrade 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.
Estimating Ore Grade Using Evolutionary Machine Learning Models
Ehteram, Mohammad/ Khozani, Zohreh Sheikh/ Soltani-mohammadi, Saeed/ Abbaszadeh, Maliheh
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Estimating Ore Grade Using Evolutionary Machine Learning Models
Ehteram, Mohammad/ Khozani, Zohreh Sheikh/ Soltani-mohammadi, Saeed/ Abbaszadeh, Maliheh
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Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 oregrade 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. 116 pp. Englisch.
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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 mode…ls 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 oregrade 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. 116 pp. Englisch.
Estimating Ore Grade Using Evolutionary Machine Learning Models
Ehteram, Mohammad|Khozani, Zohreh Sheikh|Soltani-Mohammadi, Saeed|Abbaszadeh, Maliheh
Language: English
Published by Springer, Berlin|Springer Nature Singapore|Springer, 2024
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 .
Estimating Ore Grade Using Evolutionary Machine Learning Models
Ehteram, Mohammad|Khozani, Zohreh Sheikh|Soltani-Mohammadi, Saeed|Abbaszadeh, Maliheh
Language: English
Published by Springer, Berlin|Springer Nature Singapore|Springer, 2023
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 .
Estimating Ore Grade Using Evolutionary Machine Learning Models
Ehteram, Mohammad; Khozani, Zohreh Sheikh; Soltani-Mohammadi, Saeed; Abbaszadeh, Maliheh
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Estimating Ore Grade Using Evolutionary Machine Learning Models
Ehteram, Mohammad; Khozani, Zohreh Sheikh; Soltani-Mohammadi, Saeed; Abbaszadeh, Maliheh
- Hardcover
- Print on Demand
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 f…or 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 oregrade 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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 116 pp. Englisch.
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Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000
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Buch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 esti…mating 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 oregrade 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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 116 pp. Englisch.
Estimating Ore Grade Using Evolutionary Machine Learning Models
Ehteram, Mohammad; Khozani, Zohreh Sheikh; Soltani-Mohammadi, Saeed; Abbaszadeh, Maliheh
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Estimating Ore Grade Using Evolutionary Machine Learning Models
Ehteram, Mohammad; Khozani, Zohreh Sheikh; Soltani-Mohammadi, Saeed; Abbaszadeh, Maliheh
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