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020			_a9783030011802
024	7		_a10.1007/978-3-030-01180-2 _2doi
040			_bspa _dES-MaUEC _cES-MaUEC
050		4	_aQA276 _b2019 EB
100	1		_aLeke, Collins Achepsah _eautor _9671344
245	1	0	_aDeep Learning and Missing Data in Engineering Systems _cby Collins Achepsah Leke, Tshilidzi Marwala.
264		1	_aCham _bImprint: Springer _c2019
300			_a1 recurso en línea (XIV, 179 páginas)
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF _2rda
490	0		_aStudies in Big Data _x2197-6503 _v48
490	0		_aIntelligent Technologies and Robotics (Springer-42732)
505	0		_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.
520	3		_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.
988			_aPrimersemestre_2019_Robotics
650		7	_2embne _9495511 _aDatos masivos
650		7	_2embne _aInteligencia artificial _9413115
700	1		_aMarwala, Tshilidzi. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
776	0	8	_iPrinted edition: _z9783030011796
776	0	8	_iPrinted edition: _z9783030011819
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-01180-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
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998			_aSI _cm _dz _feng _ggw _h0 _b11/2019 _ek _zSI