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Deep Learning and Missing Data in Engineering Systems (Studies in big Data) (en Inglés)
Collins Achepsah Leke; Tshilidzi Marwala (Autor)
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Springer Nature Switzerland Ag
· Tapa Dura
Deep Learning and Missing Data in Engineering Systems (Studies in big Data) (en Inglés) - Collins Achepsah Leke; Tshilidzi Marwala
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Reseña del libro "Deep Learning and Missing Data in Engineering Systems (Studies in big Data) (en Inglés)"
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 estimationDeep 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; andthe 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.
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