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Unplanned dilution and ore loss prediction in longhole stoping mines via multiple regression and artificial neural network analyses
H. Jang, E. Topal, Y. Kawamura
Research output
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Contribution to journal
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Article
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peer-review
18
Citations (Scopus)
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Dive into the research topics of 'Unplanned dilution and ore loss prediction in longhole stoping mines via multiple regression and artificial neural network analyses'. Together they form a unique fingerprint.
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INIS
prediction
100%
losses
100%
neural networks
100%
dilution
100%
mines
100%
ores
100%
network analysis
100%
productivity
33%
mining
33%
underground
33%
design
16%
planning
16%
comparative evaluations
16%
interactions
16%
data
16%
values
16%
tools
16%
correlations
16%
nonlinear problems
16%
datasets
16%
regression analysis
16%
western australia
16%
Earth and Planetary Sciences
Dilution
100%
Artificial Neural Network
100%
Stoping
100%
Multiple Regression
100%
Network Analysis
100%
Western Australia
16%
Correlation Coefficient
16%
Biochemistry, Genetics and Molecular Biology
Artificial Neural Network
100%
Dilution
100%
Material Science
Dilution
100%
Agricultural and Biological Sciences
Neural Network
100%
Keyphrases
Nonlinear Regression Analysis
20%