Predicting Building-Related Carbon Emissions: A Test of Machine Learning Models
This chapter evaluates and compares the performance of six machine-learning (ML) algorithms in predicting China’s building-related carbon emissions. The models took into account five input parameters influencing building-related CO2 emissions: urbanisatio
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Aboul-Ella Hassanien Mohamed Hamed N. Taha Nour Eldeen M. Khalifa Editors
Enabling AI Applications in Data Science
Studies in Computational Intelligence Volume 911
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 Mohamed Hamed N. Taha Nour Eldeen M. Khalifa •
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Editors
Enabling AI Applications in Data Science
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Editors Aboul-Ella Hassanien Faculty of Computers and Artificial Intelligence Cairo University Giza, Egypt
Mohamed Hamed N. Taha Faculty of Computers and Artificial Intelligence Cairo University Giza, Egypt
Chair of the scientific Research Group in Egypt Cairo University Giza, Egypt Nour Eldeen M. Khalifa Faculty of Computers and Artificial Intelligence Cairo University Giza, Egypt
ISSN 1860-949X ISSN 1860-9503 (electronic) Studies in Computational Intelligence ISBN 978-3-030-52066-3 ISBN 978-3-030-52067-0 (eBook) https://doi.org/10.1007/978-3-030-52067-0 © 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 and information in this boo
