Deep Learning and Missing Data in Engineering Systems
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 recogn
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Collins Achepsah Leke Tshilidzi Marwala
Deep Learning and Missing Data in Engineering Systems
Studies in Big Data Volume 48
Series editor Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland e-mail: [email protected]
The series “Studies in Big Data” (SBD) publishes new developments and advances in the various areas of Big Data- quickly and with a high quality. The intent is to cover the theory, research, development, and applications of Big Data, as embedded in the fields of engineering, computer science, physics, economics and life sciences. The books of the series refer to the analysis and understanding of large, complex, and/or distributed data sets generated from recent digital sources coming from sensors or other physical instruments as well as simulations, crowd sourcing, social networks or other internet transactions, such as emails or video click streams and others. The series contains monographs, lecture notes and edited volumes in Big Data spanning the areas of computational intelligence including neural networks, evolutionary computation, soft computing, fuzzy systems, as well as artificial intelligence, data mining, modern statistics and operations research, as well as self-organizing systems. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output.
More information about this series at http://www.springer.com/series/11970
Collins Achepsah Leke Tshilidzi Marwala •
Deep Learning and Missing Data in Engineering Systems
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Collins Achepsah Leke Faculty of Engineering and Built Environment University of Johannesburg Auckland Park, South Africa
Tshilidzi Marwala Faculty of Engineering and Built Environment University of Johannesburg Auckland Park, South Africa
ISSN 2197-6503 ISSN 2197-6511 (electronic) Studies in Big Data ISBN 978-3-030-01179-6 ISBN 978-3-030-01180-2 (eBook) https://doi.org/10.1007/978-3-030-01180-2 Library of Congress Control Number: 2018960236 Intel® and Core™ are trademarks of Intel Corporation or its subsidiaries in the U.S. and/or other countries, Intel Corporation, 2200 Mission College Boulevard, Santa Clara, CA 95054, USA, https:// www.intel.com MATLAB® is a registered trademark of The MathWorks, Inc., 1 Apple Hill Drive, Natick, MA 01760-2098, USA, http://www.mathworks.com © Springer Nature Switzerland AG 2019 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific s
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