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Deep Learning and Missing Data in Engineering Systems (Hardcover, 1st ed. 2019)
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Deep Learning and Missing Data in Engineering Systems (Hardcover, 1st ed. 2019)
Series: Studies in Big Data, 48
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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.
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