Advances in Computational Intelligence Applications in the Mining Industry

© 2022 by the authors; CC BY-NC-ND license

truck dispatching; mining equipment uncertainties; orebody uncertainty; discrete event simulation; Q-learning; grinding circuits; minerals processing; random forest; decision trees; machine learning; knowledge discovery; variable importance; mineral prospectivity mapping; random forest algorithm; machine learning; epithermal gold; unstructured data; machine learning; blast impact; empirical model; mining; fragmentation; machine learning; mine worker fatigue; random forest model; health and safety management; stockpiles; operational data; mine-to-mill; geostatistics; ore control; mine optimization; discrete event simulation; digital twin; modes of operation; geological uncertainty; multivariate statistics; partial least squares regression; oil sands; bitumen extraction; bitumen processability; mine safety and health; accidents; narratives; machine learning; natural language processing; random forest classification; hyperspectral imaging; multispectral imaging; dimensionality reduction; neighbourhood component analysis; artificial intelligence; machine learning; mining exploitation; masonry buildings; damage risk analysis; artificial intelligence; Bayesian network; Naive Bayes; Bayesian Network Structure Learning (BNSL); machine learning; random forest; rock type; mining geology; bluetooth beacon; classification and regression tree; gaussian naïve bayes; k-nearest neighbors; support vector machine; transport route; transport time; underground mine; tactical geometallurgy; data analytics in mining; ball mill throughput; measurement while drilling; non-additivity; coal; petrographic analysis; macerals; image analysis; semantic segmentation; convolutional neural networks; point cloud scaling; fragmentation size analysis; structure from motion; n/a