Machine Learning and Data Mining in Aerospace Technology
This book explores the main concepts, algorithms, and techniques of Machine Learning and data mining for aerospace technology. Satellites are the ‘eagle eyes’ that allow us to view massive areas of the Earth simultaneously, and can gather more data, more
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Aboul Ella Hassanien Ashraf Darwish Hesham El-Askary Editors
Machine Learning and Data Mining in Aerospace Technology
Studies in Computational Intelligence Volume 836
Series Editor Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland
The series “Studies in Computational Intelligence” (SCI) publishes new developments and advances in the various areas of computational intelligence—quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life sciences, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent 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. The books of this series are submitted to indexing to Web of Science, EI-Compendex, DBLP, SCOPUS, Google Scholar and Springerlink.
More information about this series at http://www.springer.com/series/7092
Aboul Ella Hassanien Ashraf Darwish Hesham El-Askary •
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Editors
Machine Learning and Data Mining in Aerospace Technology
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Editors Aboul Ella Hassanien Faculty of Computers and Artificial Intelligence, Information Technology Department Cairo University Cairo, Egypt
Ashraf Darwish Faculty of Science Helwan University Cairo, Egypt
Hesham El-Askary Center of Excellence in Earth Systems Modeling and Observations, Schmid College of Science and Technology Chapman University Orange, CA, USA Department of Environmental Sciences Faculty of Science Alexandria University Alexandria, Egypt
ISSN 1860-949X ISSN 1860-9503 (electronic) Studies in Computational Intelligence ISBN 978-3-030-20211-8 ISBN 978-3-030-20212-5 (eBook) https://doi.org/10.1007/978-3-030-20212-5 © Springer Nature Switzerland AG 2020 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 statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believe
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