Deep learning (DL) is one of the most promising artificial intelligence (AI) methods that tries to imitate the workings of the human brain in processing information, and automatically generates patterns for decision making and other complicated tasks. DL is able to learn with/without human supervision, drawing from data, even unstructured and/or unlabelled. However, the achievements of DL techniques do not stop at only arriving and outperforming the results of other AI algorithms: DL’s accomplishments are generally better than human results for tasks like image recognition or game playing, thus beyond the expectations of the experts.
The aim of this special section is to provide a diverse, but complementary, set of contributions to demonstrate new developments and applications of DL to solve problems in diverse fields. The ultimate goal is to promote research and development of DL by publishing high-quality survey and research articles in this rapidly growing field.
The topics of interest include
New architectures, theories, analytics for DL
Deep convolutional neural network
Deep graph neural network
DL with attention mechanism
DL applications, e.g., IoT, medical image analysis, multimedia technology, and enhanced learning
New papers, or extended versions of papers presented at related conferences, are welcome. Submissions must not be currently under review for publication elsewhere. Conference papers may be submitted only if they are substantially extended (more than 50%) and must be referenced. All submitted papers will be peer-reviewed using the normal standards of CAEE, and accepted based on quality, originality, novelty, and relevance to the theme of the special section. By submitting a paper to this issue, the authors agree to referee one paper (if asked) within the time frame of the special issue.
Before submission, authors should carefully read the Guide for Authors available at
Authors should submit their papers through the journal’s web submission tool at https://www.editorialmanager.com/compeleceng/default.aspx by selecting “VSI-radl” under the “Issues” tab.
For additional questions, contact the Main Guest Editor.
Submission of manuscript: Dec. 15, 2021
First notification: March 15, 2022
Submission of revised manuscript: April 15, 2022
Notification of the re-review: May 15, 2022
Final notification: June 15, 2022
Final paper due: July 15, 2022
Publication: Oct 15, 2022
Yu-Dong Zhang (Main Guest Editor)
University of Leicester, Leicester, UK
Email: [email protected]
Juan Manuel Gorriz
University of Granada, Spain
Email: [email protected]
Vanderbilt University, USA
Email: [email protected]
Yu-Dong Zhang received the PhD degree in Signal and Information Processing from Southeast University in 2010. He worked as a postdoc from 2010 to 2012 with Columbia University, USA; and as an assistant research scientist from 2012 to 2013 with Research Foundation of Mental Hygiene (RFMH), USA. He served as a Full Professor from 2013 to 2017 with Nanjing Normal University. Now he serves as Professor with Department of Informatics, University of Leicester, UK. His research interests include deep learning and medical image analysis.
He is a Fellow of IET (FIET), and Senior Members of IEEE, IES, and ACM. He was included in “Most Cited Chinese researchers (Computer Science)” by Elsevier from 2014 to 2018. He was the 2019 recipient of “Web of Science Highly Cited Researcher”. He won “Emerald Citation of Excellence 2017” and “MDPI Top 10 Most Cited Papers 2015”. He was included in “Top Scientist” in Guide2Research. He is the author of over 200 peer-reviewed articles, including more than 30 “ESI Highly Cited Papers”, and 3 “ESI Hot Papers”. His citation reached 13719 in Google Scholar, and 8025 in Web of Science. He has conducted many successful industrial projects and academic grants from NSFC, NIH, Royal Society, EPSRC, MRC, and British Council.
J.M. Górriz received the B.Sc. degree in physics, the B.Sc. degree in electronic engineering from the University of Granada, Spain, in 2000 and 2001, respectively, the Ph.D. degree from the University of Cádiz, Spain, in 2003, and the Ph.D. degree from the University of Granada in 2006. He is currently a Full Professor with the Department of Signal Theory, Networking and Communications, University of Granada and Visiting Professor at the University of Cambridge, UK. He has co-authored over 400 technical journals and conference papers in these areas. His current interests include statistical signal processing and its application to biosignal and medical image processing. He received the National Academy of Engineering Medal in 2015. He has served as an Editor for several journals and books.
Yuankai Huo is an Assistant Professor of Computer Science at Vanderbilt University, TN, USA. He received his B.S. degree in Electrical Engineering from Nanjing University of Posts and Telecommunications (NJUPT) in 2008, and Master degree in Electrical Engineering from Southeast University in 2011. After graduation, He worked in Columbia University and New York State Psychiatric Institute as a staff engineer and research officer from 2011 to 2014. He received his Master degree in Computer Science from Columbia University in 2014, and Ph.D. degree in Electrical Engineering from Vanderbilt University in 2018. Then, he had worked as a Research Assistant Professor at Vanderbilt University, and later, a Senior Research Scientist at PAII Labs. Since 2020, he has been a faculty member at Department of Electrical Engineering and Computer Science, and Data Science Institute, Vanderbilt University. Dr. Huo has received grant funding from NIH Specialized Program of Research Excellence (SPORE) program. He has been an academic editor for PlosOne, as well as active reviewer for Medical Image Analysis, IEEE Transactions on Medical Imaging, IEEE Transactions on Biomedical Engineering, NeuroImage, EEE Journal of Biomedical and Health Informatics, IEEE Transactions on Industrial Informatics, Human Brain Mapping, Medical Physics, MICCAI, MIDL etc. He has published more than 70 journal and conference papers in the areas of medical image analysis and machine learning.