Advances in Computational Intelligence Applications in the Mining Industry
This book captures advancements in the applications of computational intelligence (artificial intelligence, machine learning, etc.) to problems in the mineral and mining industries. The papers present the state of the art in four broad categories: mine operations, mine planning, mine safety, and adv...
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| Year of Publication: | 2022 |
| Language: | English |
| Physical Description: | 1 electronic resource (324 p.) |
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| 100 | 1 | |a Ganguli, Rajive |4 edt | |
| 245 | 1 | 0 | |a Advances in Computational Intelligence Applications in the Mining Industry |
| 260 | |a Basel |b MDPI - Multidisciplinary Digital Publishing Institute |c 2022 | ||
| 300 | |a 1 electronic resource (324 p.) | ||
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| 520 | |a This book captures advancements in the applications of computational intelligence (artificial intelligence, machine learning, etc.) to problems in the mineral and mining industries. The papers present the state of the art in four broad categories: mine operations, mine planning, mine safety, and advances in the sciences, primarily in image processing applications. Authors in the book include both researchers and industry practitioners. | ||
| 546 | |a English | ||
| 650 | 7 | |a Technology: general issues |2 bicssc | |
| 650 | 7 | |a History of engineering & technology |2 bicssc | |
| 653 | |a truck dispatching | ||
| 653 | |a mining equipment uncertainties | ||
| 653 | |a orebody uncertainty | ||
| 653 | |a discrete event simulation | ||
| 653 | |a Q-learning | ||
| 653 | |a grinding circuits | ||
| 653 | |a minerals processing | ||
| 653 | |a random forest | ||
| 653 | |a decision trees | ||
| 653 | |a machine learning | ||
| 653 | |a knowledge discovery | ||
| 653 | |a variable importance | ||
| 653 | |a mineral prospectivity mapping | ||
| 653 | |a random forest algorithm | ||
| 653 | |a epithermal gold | ||
| 653 | |a unstructured data | ||
| 653 | |a blast impact | ||
| 653 | |a empirical model | ||
| 653 | |a mining | ||
| 653 | |a fragmentation | ||
| 653 | |a mine worker fatigue | ||
| 653 | |a random forest model | ||
| 653 | |a health and safety management | ||
| 653 | |a stockpiles | ||
| 653 | |a operational data | ||
| 653 | |a mine-to-mill | ||
| 653 | |a geostatistics | ||
| 653 | |a ore control | ||
| 653 | |a mine optimization | ||
| 653 | |a digital twin | ||
| 653 | |a modes of operation | ||
| 653 | |a geological uncertainty | ||
| 653 | |a multivariate statistics | ||
| 653 | |a partial least squares regression | ||
| 653 | |a oil sands | ||
| 653 | |a bitumen extraction | ||
| 653 | |a bitumen processability | ||
| 653 | |a mine safety and health | ||
| 653 | |a accidents | ||
| 653 | |a narratives | ||
| 653 | |a natural language processing | ||
| 653 | |a random forest classification | ||
| 653 | |a hyperspectral imaging | ||
| 653 | |a multispectral imaging | ||
| 653 | |a dimensionality reduction | ||
| 653 | |a neighbourhood component analysis | ||
| 653 | |a artificial intelligence | ||
| 653 | |a mining exploitation | ||
| 653 | |a masonry buildings | ||
| 653 | |a damage risk analysis | ||
| 653 | |a Bayesian network | ||
| 653 | |a Naive Bayes | ||
| 653 | |a Bayesian Network Structure Learning (BNSL) | ||
| 653 | |a rock type | ||
| 653 | |a mining geology | ||
| 653 | |a bluetooth beacon | ||
| 653 | |a classification and regression tree | ||
| 653 | |a gaussian naïve bayes | ||
| 653 | |a k-nearest neighbors | ||
| 653 | |a support vector machine | ||
| 653 | |a transport route | ||
| 653 | |a transport time | ||
| 653 | |a underground mine | ||
| 653 | |a tactical geometallurgy | ||
| 653 | |a data analytics in mining | ||
| 653 | |a ball mill throughput | ||
| 653 | |a measurement while drilling | ||
| 653 | |a non-additivity | ||
| 653 | |a coal | ||
| 653 | |a petrographic analysis | ||
| 653 | |a macerals | ||
| 653 | |a image analysis | ||
| 653 | |a semantic segmentation | ||
| 653 | |a convolutional neural networks | ||
| 653 | |a point cloud scaling | ||
| 653 | |a fragmentation size analysis | ||
| 653 | |a structure from motion | ||
| 776 | |z 3-0365-3159-9 | ||
| 776 | |z 3-0365-3158-0 | ||
| 700 | 1 | |a Dessureault, Sean |4 edt | |
| 700 | 1 | |a Rogers, Pratt |4 edt | |
| 700 | 1 | |a Ganguli, Rajive |4 oth | |
| 700 | 1 | |a Dessureault, Sean |4 oth | |
| 700 | 1 | |a Rogers, Pratt |4 oth | |
| 906 | |a BOOK | ||
| ADM | |b 2023-12-15 05:40:50 Europe/Vienna |f system |c marc21 |a 2022-04-04 09:22:53 Europe/Vienna |g false | ||
| AVE | |i DOAB Directory of Open Access Books |P DOAB Directory of Open Access Books |x https://eu02.alma.exlibrisgroup.com/view/uresolver/43ACC_OEAW/openurl?u.ignore_date_coverage=true&portfolio_pid=5338095400004498&Force_direct=true |Z 5338095400004498 |b Available |8 5338095400004498 | ||