04964nam 2200937z- 450 991055735900332120220111(CKB)5400000000042302(oapen)https://directory.doabooks.org/handle/20.500.12854/77114(oapen)doab77114(EXLCZ)99540000000004230220202201d2021 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierStatistical Data Modeling and Machine Learning with ApplicationsBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20211 online resource (184 p.)3-0365-2692-7 3-0365-2693-5 The modeling and processing of empirical data is one of the main subjects and goals of statistics. Nowadays, with the development of computer science, the extraction of useful and often hidden information and patterns from data sets of different volumes and complex data sets in warehouses has been added to these goals. New and powerful statistical techniques with machine learning (ML) and data mining paradigms have been developed. To one degree or another, all of these techniques and algorithms originate from a rigorous mathematical basis, including probability theory and mathematical statistics, operational research, mathematical analysis, numerical methods, etc. Popular ML methods, such as artificial neural networks (ANN), support vector machines (SVM), decision trees, random forest (RF), among others, have generated models that can be considered as straightforward applications of optimization theory and statistical estimation. The wide arsenal of classical statistical approaches combined with powerful ML techniques allows many challenging and practical problems to be solved. This Special Issue belongs to the section "Mathematics and Computer Science". Its aim is to establish a brief collection of carefully selected papers presenting new and original methods, data analyses, case studies, comparative studies, and other research on the topic of statistical data modeling and ML as well as their applications. Particular attention is given, but is not limited, to theories and applications in diverse areas such as computer science, medicine, engineering, banking, education, sociology, economics, among others. The resulting palette of methods, algorithms, and applications for statistical modeling and ML presented in this Special Issue is expected to contribute to the further development of research in this area. We also believe that the new knowledge acquired here as well as the applied results are attractive and useful for young scientists, doctoral students, and researchers from various scientific specialties.Information technology industriesbicsscartificial neural networksassessmentbankingbrain-computer interfacebreast cancer subtypingCART ensembles and baggingcategorical datacitizen scienceclassificationclassification and regression treeclusteringCNN-LSTM architecturesconsensus modelsconvexitycross-validationdam inflow predictiondamped Newtondata qualitydata-adaptive kernel functionsdeep forestEEG motor imageryensemble modelfeature selectionGower's interpolation formulaGower's metrichedonic priceshousinghyper-parameter optimizationimage datainput predictor selectionkernel clusteringkernel density estimationlong short-term memorymachine learningmathematical competencyMETABRIC datasetmixed datamulti-category classifiermulti-omics datamultidimensional scalingmultivariate adaptive regression splinesn/anon-linear optimizationpredictive modelsquantile regressionreal-time motion imagery recognitionsimilaritystochastic gradient descentsupport vector machinewavelet transformInformation technology industriesGocheva-Ilieva Snezhanaedt1303375Gocheva-Ilieva SnezhanaothBOOK9910557359003321Statistical Data Modeling and Machine Learning with Applications3026963UNINA06321nam 22016813a 450 991034668950332120250203235430.09783039211173303921117X10.3390/books978-3-03921-117-3(CKB)4920000000094774(oapen)https://directory.doabooks.org/handle/20.500.12854/47767(ScCtBLL)8e630e1d-408a-43ac-973a-2244e9bf0358(OCoLC)1126125053(oapen)doab47767(EXLCZ)99492000000009477420250203i20192019 uu engurmn|---annantxtrdacontentcrdamediacrrdacarrierFlow and Transport Properties of Unconventional Reservoirs 2018Jianchao Cai, Harpreet Singh, Zhien Zhang, Qinjun KangMDPI - Multidisciplinary Digital Publishing Institute2019Basel, Switzerland :MDPI,2019.1 electronic resource (364 p.)9783039211166 3039211161 Unconventional reservoirs are usually complex and highly heterogeneous, such as shale, coal, and tight sandstone reservoirs. The strong physical and chemical interactions between fluids and pore surfaces lead to the inapplicability of conventional approaches for characterizing fluid flow in these low-porosity and ultralow-permeability reservoir systems. Therefore, new theories and techniques are urgently needed to characterize petrophysical properties, fluid transport, and their relationships at multiple scales for improving production efficiency from unconventional reservoirs. This book presents fundamental innovations gathered from 21 recent works on novel applications of new techniques and theories in unconventional reservoirs, covering the fields of petrophysical characterization, hydraulic fracturing, fluid transport physics, enhanced oil recovery, and geothermal energy. Clearly, the research covered in this book is helpful to understand and master the latest techniques and theories for unconventional reservoirs, which have important practical significance for the economic and effective development of unconventional oil and gas resources.History of engineering and technologybicsscshale gaspermeabilityprediction by NMR logsmatrix–fracture interactionfaultsremaining oil distributionsunconventional reservoirscoal deformationreservoir depletioncarbonate reservoirnanoporefracturing fluidpseudo-potential modelshale reservoirsmatrix-fracture interactionsmulti-scale fracturesuccession pseudo-steady state (SPSS) methodfluid transport physicsintegrated methodschelating agentdissolved gasnon-equilibrium permeabilityeffective stressfractalfracture networkspontaneous imbibitiontight oilporous media0-1 programmingthe average flow velocitygeothermal watermicro-fracturepore typespore network modelpetrophysical characterizationnitrogen adsorptionanalysis of influencing factorsmudstonerheologyvelocity profileshale permeabilityflow resistanceglobal effecttight sandstonesfractal dimensioncontact angletemperature-resistancefractured well transient productivityreservoir classificationsdeep circulation groundwaterviscosityNMRfractional diffusionlattice Boltzmann methodmultiporosity and multiscalefractal geometryimbibition frontproductivity contribution degree of multimediumwetting anglepH of formation waterenhanced oil recoveryisotopestight sandstonefracture diversionshaleSRV-fractured horizontal welllow-salinity water floodingshale gas reservoirtight reservoirsfracture continuum methodtight oil reservoirLucaogou Formationhydraulic fracturingclean fracturing fluidrecovery factorflow regimeslocal effectcomplex fracture networkpore structuregas adsorption capacitypolymernon-linear flowconformable derivativeproduction simulationanalytical modelenhanced geothermal systemmulti-scale flowexperimental evaluationextended finite element methodfluid-solid interactiongroundwater flowwell-placement optimizationthickenerimbibition recoveryequilibrium permeabilityslip lengthlarge density ratioclay mineral compositionfinite volume methodvolume fracturinginfluential factorssulfonate gemini surfactantHistory of engineering and technologyCai Jianchao1305939Singh HarpreetZhang ZhienKang QinjunScCtBLLScCtBLLBOOK9910346689503321Flow and Transport Properties of Unconventional Reservoirs 20183028042UNINA