Название: Deep Learning and Missing Data in Engineering Systems Автор: Collins Achepsah Leke, Tshilidzi Marwala
Издательство: Springer
Год: 2018 (2019 Edition)
Язык: английский
Формат: pdf (true), djvu
Размер: 10.16 MB
Deep Learning and Missing Data in Engineering Systems discuss concepts and applications of artificial intelligence, specifically, deep learning. The artificial intelligence techniques that are studied include multilayer autoencoder networks and deep autoencoder networks. Also studied in this book are computational and swarm intelligence techniques which include ant colony optimization, ant lion optimizer, bat algorithm, cuckoo search optimization, firefly algorithm and invasive weed optimization algorithm. In addition, this book explores using deep autoencoder networks with a varying number of hidden layers. This book also studies the reconstruction of images from reduced dimensions obtained from the bottleneck layer of the deep autoencoder. These techniques are used to solve the missing data problem in an image recognition and reconstruction context.
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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