Data Generation Using Gene Expression Generator

Generative adversarial networks (GANs) could be used efficiently for image and video generation when labeled training data is available in bulk. In general, building a good machine learning model requires a reasonable amount of labeled training data. Howe

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Cesar Analide · Paulo Novais · David Camacho · Hujun Yin (Eds.)

Intelligent Data Engineering and Automated Learning – IDEAL 2020 21st International Conference Guimaraes, Portugal, November 4–6, 2020 Proceedings, Part II

Lecture Notes in Computer Science Founding Editors Gerhard Goos Karlsruhe Institute of Technology, Karlsruhe, Germany Juris Hartmanis Cornell University, Ithaca, NY, USA

Editorial Board Members Elisa Bertino Purdue University, West Lafayette, IN, USA Wen Gao Peking University, Beijing, China Bernhard Steffen TU Dortmund University, Dortmund, Germany Gerhard Woeginger RWTH Aachen, Aachen, Germany Moti Yung Columbia University, New York, NY, USA

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More information about this series at http://www.springer.com/series/7409

Cesar Analide Paulo Novais David Camacho Hujun Yin (Eds.) •





Intelligent Data Engineering and Automated Learning – IDEAL 2020 21st International Conference Guimaraes, Portugal, November 4–6, 2020 Proceedings, Part II

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Editors Cesar Analide University of Minho Braga, Portugal

Paulo Novais University of Minho Braga, Portugal

David Camacho Technical University of Madrid Madrid, Spain

Hujun Yin University of Manchester Manchester, UK

ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Computer Science ISBN 978-3-030-62364-7 ISBN 978-3-030-62365-4 (eBook) https://doi.org/10.1007/978-3-030-62365-4 LNCS Sublibrary: SL3 – Information Systems and Applications, incl. Internet/Web, and HCI © 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 believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

The International Conference on Intelligent Data Engineering and Automated Learning (IDEAL) is an annual international conference dedicated to emerging and cha