A Comprehensive Study of Deep Neural Networks for Unsupervised Deep Learning
Deep learning methods aims at learning meaningful representations in the field of machine learning (ML). Unsupervised deep learning architectures has grown at a fast pace owing to their ability to learn intricate problems. Availability of large amount of
- PDF / 10,427,400 Bytes
- 310 Pages / 453.543 x 683.15 pts Page_size
- 44 Downloads / 170 Views
Aboul Ella Hassanien Roheet Bhatnagar Ashraf Darwish Editors
Artificial Intelligence for Sustainable Development: Theory, Practice and Future Applications
Studies in Computational Intelligence Volume 912
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 Roheet Bhatnagar Ashraf Darwish •
•
Editors
Artificial Intelligence for Sustainable Development: Theory, Practice and Future Applications
123
Editors Aboul Ella Hassanien Information Technology Department, Faculty of Computers and Information Cairo University Giza, Egypt
Roheet Bhatnagar Department of Computer Science and Engineering, Faculty of Engineering Manipal University Jaipur, Rajasthan, India
Ashraf Darwish Faculty of Science Helwan University Cairo, Egypt
ISSN 1860-949X ISSN 1860-9503 (electronic) Studies in Computational Intelligence ISBN 978-3-030-51919-3 ISBN 978-3-030-51920-9 (eBook) https://doi.org/10.1007/978-3-030-51920-9 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 This work is subject to copyright. All rights are solely and exclusively licensed 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 an
Data Loading...