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100   $a20181213d2019      u|         0
101 0 $aeng
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181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aDeep Learning and Missing Data in Engineering Systems /$fby Collins Achepsah Leke, Tshilidzi Marwala
205   $a1st ed. 2019.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019.
215   $a1 online resource (188 pages)
225 1 $aStudies in Big Data,$x2197-6503 ;$v48
311   $a3-030-01179-8 
327   $aIntroduction to Missing Data Estimation -- Introduction to Deep Learning -- Missing Data Estimation Using Bat Algorithm -- Missing Data Estimation Using Cuckoo Search Algorithm -- Missing Data Estimation Using Firefly Algorithm -- Missing Data Estimation Using Ant Colony Optimization Algorithm -- Missing Data Estimation Using Ant-Lion Optimizer Algorithm -- Missing Data Estimation Using Invasive Weed Optimization Algorithm -- Missing Data Estimation Using Swarm Intelligence Algorithms from Reduced Dimensions -- Missing Data Estimation Using Swarm Intelligence Algorithms: Deep Learning Framework Analysis -- Conclusion.
330   $aDeep Learning and Missing Data in Engineering Systems uses deep learning and swarm intelligence methods to cover missing data estimation in engineering systems. The missing data estimation processes proposed in the book can be applied in image recognition and reconstruction. To facilitate the imputation of missing data, several artificial intelligence approaches are presented, including: deep autoencoder neural networks; deep denoising autoencoder networks; the bat algorithm; the cuckoo search algorithm; and the firefly algorithm. The hybrid models proposed are used to estimate the missing data in high-dimensional data settings more accurately. Swarm intelligence algorithms are applied to address critical questions such as model selection and model parameter estimation. The authors address feature extraction for the purpose of reconstructing the input data from reduced dimensions by the use of deep autoencoder neural networks. They illustrate new models diagrammatically, report their findings in tables, so as to put their methods on a sound statistical basis. The methods proposed speed up the process of data estimation while preserving known features of the data matrix. This book is a valuable source of information for researchers and practitioners in data science. Advanced undergraduate and postgraduate students studying topics in computational intelligence and big data, can also use the book as a reference for identifying and introducing new research thrusts in missing data estimation.
410  0$aStudies in Big Data,$x2197-6503 ;$v48
606   $aComputational intelligence
606   $aBig data
606   $aArtificial intelligence
606   $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014
606   $aBig Data$3https://scigraph.springernature.com/ontologies/product-market-codes/I29120
606   $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000
615  0$aComputational intelligence.
615  0$aBig data.
615  0$aArtificial intelligence.
615 14$aComputational Intelligence.
615 24$aBig Data.
615 24$aArtificial Intelligence.
676   $a006.31
676   $a519.5
700   $aLeke$b Collins Achepsah$4aut$4http://id.loc.gov/vocabulary/relators/aut$01225109
702   $aMarwala$b Tshilidzi$4aut$4http://id.loc.gov/vocabulary/relators/aut
906   $aBOOK
912   $a9910483526703321
996   $aDeep Learning and Missing Data in Engineering Systems$92844577
997   $aUNINA