LEADER 00889nam0-22003251i-450- 001 990001435420403321 005 20091203143635.0 010 $a0-387-90641-X 035 $a000143542 035 $aFED01000143542 035 $a(Aleph)000143542FED01 035 $a000143542 100 $a20001205d1981----km-y0itay50------ba 101 0 $aeng 102 $aUS 105 $aa---a---001yy 200 1 $a<>science of programming$fDavid Gries 210 $aNew York$cSpringer$dc1981 215 $axv, 366 p.$d24 cm 225 1 $aTexts and Monographs in Computer Science 610 0 $aComputer science$aProgrammazione 676 $a005.1 700 1$aGries,$bDavid$0944 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990001435420403321 952 $a005.1-GRI-1$b7 presidenza$fSC1 959 $aSC1 996 $aScience of programming$9375081 997 $aUNINA LEADER 01752nam 2200469Ia 450 001 9910697817803321 005 20081218120031.0 035 $a(CKB)5470000002392808 035 $a(OCoLC)288938774 035 $a(EXLCZ)995470000002392808 100 $a20081218d2008 ua 0 101 0 $aeng 135 $aurbn||||a|||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aEngaging consumers$b[electronic resource] $ewhat can be learned from public health consumer education programs? : final report /$fMargaret Gerteis, Matthew Hodges, James Mulligan 210 1$aCambridge, MA :$cMathematica Policy Research, Inc. ;$aWashington, DC :$cMedPAC,$d[2008] 215 $a1 volume (various pagings) $cdigital, PDF file 225 1 $a[MedPAC contract research series] ;$vno. 08-6 300 $aTitle from title screen (viewed on Dec. 18, 2008). 300 $a"March 6, 2008." 300 $a"Contract no.: RFP03-06-MedPAC/E4034941." 320 $aIncludes bibliographical references (page [A-1] - A-9). 517 $aEngaging consumers 606 $aHealth promotion$zUnited States 606 $aMedical care$zUnited States$xDecision making 606 $aConsumer education$zUnited States 615 0$aHealth promotion 615 0$aMedical care$xDecision making. 615 0$aConsumer education 700 $aGerteis$b Margaret$01408698 701 $aHodges$b Matthew$01408699 701 $aMulligan$b James$01408700 712 02$aMathematica Policy Research, Inc. 712 02$aMedicare Payment Advisory Commission (U.S.) 801 0$bGPO 801 1$bGPO 906 $aBOOK 912 $a9910697817803321 996 $aEngaging consumers$93493196 997 $aUNINA LEADER 04583nam 2201345z- 450 001 9910557613103321 005 20220321 035 $a(CKB)5400000000045271 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/79603 035 $a(oapen)doab79603 035 $a(EXLCZ)995400000000045271 100 $a20202203d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aAdvances in Computational Intelligence Applications in the Mining Industry 210 $aBasel$cMDPI - Multidisciplinary Digital Publishing Institute$d2022 215 $a1 online resource (324 p.) 311 08$a3-0365-3159-9 311 08$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 $aHistory of engineering & technology$2bicssc 606 $aTechnology: general issues$2bicssc 610 $aaccidents 610 $aartificial intelligence 610 $aball mill throughput 610 $aBayesian network 610 $aBayesian Network Structure Learning (BNSL) 610 $abitumen extraction 610 $abitumen processability 610 $ablast impact 610 $abluetooth beacon 610 $aclassification and regression tree 610 $acoal 610 $aconvolutional neural networks 610 $adamage risk analysis 610 $adata analytics in mining 610 $adecision trees 610 $adigital twin 610 $adimensionality reduction 610 $adiscrete event simulation 610 $aempirical model 610 $aepithermal gold 610 $afragmentation 610 $afragmentation size analysis 610 $agaussian nai?ve bayes 610 $ageological uncertainty 610 $ageostatistics 610 $agrinding circuits 610 $ahealth and safety management 610 $ahyperspectral imaging 610 $aimage analysis 610 $ak-nearest neighbors 610 $aknowledge discovery 610 $amacerals 610 $amachine learning 610 $amasonry buildings 610 $ameasurement while drilling 610 $amine optimization 610 $amine safety and health 610 $amine worker fatigue 610 $amine-to-mill 610 $amineral prospectivity mapping 610 $aminerals processing 610 $amining 610 $amining equipment uncertainties 610 $amining exploitation 610 $amining geology 610 $amodes of operation 610 $amultispectral imaging 610 $amultivariate statistics 610 $an/a 610 $aNaive Bayes 610 $anarratives 610 $anatural language processing 610 $aneighbourhood component analysis 610 $anon-additivity 610 $aoil sands 610 $aoperational data 610 $aore control 610 $aorebody uncertainty 610 $apartial least squares regression 610 $apetrographic analysis 610 $apoint cloud scaling 610 $aQ-learning 610 $arandom forest 610 $arandom forest algorithm 610 $arandom forest classification 610 $arandom forest model 610 $arock type 610 $asemantic segmentation 610 $astockpiles 610 $astructure from motion 610 $asupport vector machine 610 $atactical geometallurgy 610 $atransport route 610 $atransport time 610 $atruck dispatching 610 $aunderground mine 610 $aunstructured data 610 $avariable importance 615 7$aHistory of engineering & technology 615 7$aTechnology: general issues 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