LEADER 04546nam 2201321z- 450 001 9910557613103321 005 20231214133042.0 035 $a(CKB)5400000000045271 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/79603 035 $a(EXLCZ)995400000000045271 100 $a20202203d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvances in Computational Intelligence Applications in the Mining Industry 210 $aBasel$cMDPI - Multidisciplinary Digital Publishing Institute$d2022 215 $a1 electronic resource (324 p.) 311 $a3-0365-3159-9 311 $a3-0365-3158-0 330 $aThis 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. 606 $aTechnology: general issues$2bicssc 606 $aHistory of engineering & technology$2bicssc 610 $atruck dispatching 610 $amining equipment uncertainties 610 $aorebody uncertainty 610 $adiscrete event simulation 610 $aQ-learning 610 $agrinding circuits 610 $aminerals processing 610 $arandom forest 610 $adecision trees 610 $amachine learning 610 $aknowledge discovery 610 $avariable importance 610 $amineral prospectivity mapping 610 $arandom forest algorithm 610 $aepithermal gold 610 $aunstructured data 610 $ablast impact 610 $aempirical model 610 $amining 610 $afragmentation 610 $amine worker fatigue 610 $arandom forest model 610 $ahealth and safety management 610 $astockpiles 610 $aoperational data 610 $amine-to-mill 610 $ageostatistics 610 $aore control 610 $amine optimization 610 $adigital twin 610 $amodes of operation 610 $ageological uncertainty 610 $amultivariate statistics 610 $apartial least squares regression 610 $aoil sands 610 $abitumen extraction 610 $abitumen processability 610 $amine safety and health 610 $aaccidents 610 $anarratives 610 $anatural language processing 610 $arandom forest classification 610 $ahyperspectral imaging 610 $amultispectral imaging 610 $adimensionality reduction 610 $aneighbourhood component analysis 610 $aartificial intelligence 610 $amining exploitation 610 $amasonry buildings 610 $adamage risk analysis 610 $aBayesian network 610 $aNaive Bayes 610 $aBayesian Network Structure Learning (BNSL) 610 $arock type 610 $amining geology 610 $abluetooth beacon 610 $aclassification and regression tree 610 $agaussian nai?ve bayes 610 $ak-nearest neighbors 610 $asupport vector machine 610 $atransport route 610 $atransport time 610 $aunderground mine 610 $atactical geometallurgy 610 $adata analytics in mining 610 $aball mill throughput 610 $ameasurement while drilling 610 $anon-additivity 610 $acoal 610 $apetrographic analysis 610 $amacerals 610 $aimage analysis 610 $asemantic segmentation 610 $aconvolutional neural networks 610 $apoint cloud scaling 610 $afragmentation size analysis 610 $astructure from motion 615 7$aTechnology: general issues 615 7$aHistory of engineering & technology 700 $aGanguli$b Rajive$4edt$01314875 702 $aDessureault$b Sean$4edt 702 $aRogers$b Pratt$4edt 702 $aGanguli$b Rajive$4oth 702 $aDessureault$b Sean$4oth 702 $aRogers$b Pratt$4oth 906 $aBOOK 912 $a9910557613103321 996 $aAdvances in Computational Intelligence Applications in the Mining Industry$93032078 997 $aUNINA