2SI AASHTO Abaqus Actix Analyzer ADINA Adobe Acrobat Airports AISC Algorithms Aluminium Animation ANSYS APF Nexus Aquaveo Architecture Artificial Intelligence ASCE ASDIP Ashampoo Asphalt ASTM Autocad Autodesk Bentley BetonExpress BIMware MASTER BitCoin Blast Books Bridges Buildings CAD Calculus CCleaner Cement Chasm Consulting Civil 3D Clay Coastal Structures Codes ComingSoon Computer Engineering Concrete Conference Books CorelCAD Corrosion Courses/Lessons Cranes CSI CTI Vespa2 Daemon Tools Dams Data Analysis Data Mining Deep Freeze Deep Learning Design and Build Websites DiCad Strakon Dictionary Digital Canal DimSoln Dlubal Drainage Dredging Dynamics Earthquake Earthworks EC2 Reinforcement EC3 Steel Connections EC5 Timber Connections Economy Elasticity EnerCalc English Language Ensoft Entertainment Environmental Engineering Equipments Erosion ESPRI ETABS Eurocode Excavation Excel Expansive Soils FIDES DV-Partner Finite Element Model Fire Safety Fluid Mechanics Forensic Engineering Formulas Fortran Foundations Foxit Phantom FRP Game of Thrones Geo-Slope Geo5 Geoenvironmental Engineering Geographic Information Systems Geology Geomechanics Geomembranes Geosolve GeoStru GeoStudio Geosynthetics GeoTec Elpla Geotechnical Engineering Golden Software Graitec Grammar Grapher Ground Anchors Groundwater Grouting Gstarsoft Harry Potter Highways Historic Structures HTML5 Hydraulics Hydrology IBM IceCream Ebook Reader IDEA StatiCa IELTS IES Ikon Science InfraWorks Itasca Flac2D Java KESZ ConSteel Landslides LaTeX Limcon LimitState: GEO Lindo Linear Algebra Lingo Liquefaction LPile Lusas Malwarbytes Management Maple MapViewer Masonry Walls Materials Mathematica Maths MathType MATLAB Mechanical Engineering Mechanics Metaheuristic Algorithms Microsoft MicroStation Midas Minitab Money Movies Nanocomposites Neural Networks NovoTech Nuclear Power Plants Numerical Mathematics OaSys Octave Office Offshore OLGA Optimization Pavements PC Games PDF Phase2 Physics Piles PipeLines Pipesim Plants Plasticity Plaxis Polymath Polymer Power GEOPAK Powerpoint Precast Prestressed Concrete Pro Sap Proektsoft Programming Projects PROKON ProStructures ProtaStructure PTC MathCad Python QuickConcreteWall QuickConcretWall QuickFooting QuickMasonary QuickRWall R Language Radar System Railways RAM RCDC Regression Analysis Reinforced Concrete Reinforced Masonry Retaining Structures RetainPro Revit RISA Risk Analysis Roads RocDoc Rock Mechanics Rocscience Roofs S-Frame S.T.A. DATA 3Muri SAFE Safety Salford Predictive Modeler SAP2000 SCAD Office Schedule it Schlumberger SCIA Engineer Security Seepage Settle 3D Sewage ShapeBuilder Shotcrete Slide Slope Stability Sludge Smart Cities Snow Loads Softwares Soil Improvement Soil Mechanics SoilOffice SoilWorks SPSS STAAD.Foundation STAAD.Pro Standards Stat-Ease Stata Statics Statistics Steel Stone Strater StruCalc Structural Designer Structural Office StructurePoint Structures StruSoft Surfer Surveying Swarm Intelligence System Mechanic Tableau Technical Drawing Technology Tedds Tekla Testing The Big Bang Theory Thermodynamics Timber TOEFL Topology Torrent Traffic Transmission Lines Transportation Engineering Trimble Tunnels Turbo Pascal TV Series TweakBit Unsaturated Visual Basic Visual Integrity VisualAnalysis VisualFoundation VisualPlate VisualShearWall Water Welding Wind Loads Windows WinRAR Wolfram Wood Word

Deep Learning and Missing Data in Engineering Systems

March 20, 2019
Collins Achepsah Leke, T. Marwala ... 179 pages - Publisher: Springer; (December, 2018) ... Language: English - ASIN: B07LD4XHTL by Amazon ...

Deep Learning and Missing Data in Engineering Systems uses deep learning and swarm intelligence methods to cover missing data estimation in engineering systems. The missing data estimation processes proposed in the book can be applied in image recognition and reconstruction. To facilitate the imputation of missing data, several artificial intelligence approaches are presented, including: deep autoencoder neural networks; deep denoising autoencoder networks; the bat algorithm; the cuckoo search algorithm; and the firefly algorithm. The hybrid models proposed are used to estimate the missing data in high-dimensional data settings more accurately. Swarm intelligence algorithms are applied to address critical questions such as model selection and model parameter estimation. The authors address feature extraction for the purpose of reconstructing the input data from reduced dimensions by the use of deep autoencoder neural networks. They illustrate new models diagrammatically, report their findings in tables, so as to put their methods on a sound statistical basis. The methods proposed speed up the process of data estimation while preserving known features of the data matrix. This book is a valuable source of information for researchers and practitioners in data science. Advanced undergraduate and postgraduate students studying topics in computational intelligence and big data, can also use the book as a reference for identifying and introducing new research thrusts in missing data estimation.

Deep Learning and Missing Data in Engineering Systems, Collins Achepsah Leke, Tshilidzi Marwala


...WelCome to GeoTeknikk.COM ... Everytime, best engineering resources are on its pages. Try out. Post your request via the contact form.

Contact Form

Name

Email *

Message *

Theme images by latex. Powered by Blogger.