Hybrid Optimization-Based GRU Neural Network for Software Reliability Prediction
Aiming at the problems of low prediction accuracy and weak generalization ability of current reliability prediction models, this paper proposes a hybrid multi-layer heterogeneous particle swarm optimization algorithm (HMHPSO) that can simultaneously optim
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unications in Computer and Information Science
Data Science 6th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2020 Taiyuan, China, September 18–21, 2020 Proceedings, Part II
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Communications in Computer and Information Science Commenced Publication in 2007 Founding and Former Series Editors: Simone Diniz Junqueira Barbosa, Phoebe Chen, Alfredo Cuzzocrea, Xiaoyong Du, Orhun Kara, Ting Liu, Krishna M. Sivalingam, Dominik Ślęzak, Takashi Washio, Xiaokang Yang, and Junsong Yuan
Editorial Board Members Joaquim Filipe Polytechnic Institute of Setúbal, Setúbal, Portugal Ashish Ghosh Indian Statistical Institute, Kolkata, India Igor Kotenko St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, St. Petersburg, Russia Raquel Oliveira Prates Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil Lizhu Zhou Tsinghua University, Beijing, China
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Pinle Qin Hongzhi Wang Guanglu Sun Zeguang Lu (Eds.) •
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Data Science 6th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2020 Taiyuan, China, September 18–21, 2020 Proceedings, Part II
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Editors Pinle Qin North University of China Taiyuan, China
Hongzhi Wang Harbin Institute of Technology Harbin, China
Guanglu Sun Harbin University of Science and Technology Harbin, China
Zeguang Lu National Academy of Guo Ding Institute of Data Science Beijing, China
ISSN 1865-0929 ISSN 1865-0937 (electronic) Communications in Computer and Information Science ISBN 978-981-15-7983-7 ISBN 978-981-15-7984-4 (eBook) https://doi.org/10.1007/978-981-15-7984-4 © Springer Nature Singapore Pte Ltd. 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