Aim and Scope
The exponentially growing availability of data such as images, videos and speech from myriad sources, including social media and the Internet of Things, is driving the demand for high-performance data analysis algorithms. Deep learning is currently an extremely active research area in machine learning and pattern recognition. It provides computational models of multiple nonlinear processing neural network layers to learn and represent data with increasing levels of abstraction. Deep neural networks are able to implicitly capture intricate structures of large-scale data and deploy in cloud computing and high-performance computing platforms. The deep learning approach has demonstrated remarkable performances across a range of applications, including computer vision, image classification, face/speech recognition, natural language processing, and medical communications. However, deep neural networks yield ‘black-box’ input-output mappings that can be challenging to explain to users. Especially in the healthcare, cybersecurity, and legal fields, black-box machine learning techniques are unacceptable, since decisions may have a profound impact on peoples’ lives due to the lack of interpretability. In addition, many other open problems and challenges still exist, such as computational and time costs, repeatability of the results, convergence, and the ability to learn from a very small amount of data and to evolve dynamically. Further, despite their enormous societal benefits, deep learning can pose real threats to personal privacy. For example, deep neural networks and other machine learning models are built based on patients’ personal and highly sensitive data such as clinical records or tracked health data in the domain of healthcare. Moreover, they can be vulnerable to attackers trying to infer the sensitive data that was used to build the model. This raises important research questions about how to develop deep learning models that protect private data against inference attacks while still being accurate and useful predictive models.
This Special Issue will present robust, explainable, and efficient next-generation deep learning algorithms with data privacy and theoretical guarantees for solving challenging artificial intelligence problems. This Special Issue aims to: 1) improve the understanding and explainability of deep neural networks; 2) improve the accuracy of deep learning leveraging new stochastic optimization and neural architecture search; 3) enhance the mathematical foundation of deep neural networks; 4) design new data privacy mechanisms to optimally tradeoff between utility and privacy; and 5) increase the computational efficiency and stability of the deep learning training process with new algorithms that will scale. Potential topics include but are not limited to the following:
Novel theoretical insights on the deep neural networks
Exploration of post-hoc interpretation methods which can shed light on how deep learning models produce a specific prediction and generate a representation
Investigation of interpretable models which aim to construct self-explanatory models and incorporate interpretability directly into the structure of a deep learning model
Quantifying or visualizing the interpretability of deep neural networks
Stability improvement of deep neural network optimization
Optimization methods for deep learning
Privacy preserving machine learning (e.g., federated machine learning, learning over encrypted data)
Novel deep learning approaches in the applications of image/signal processing, business intelligence, games, healthcare, bioinformatics, and security
Submission Deadline: August 31, 2021
First Review Decision: September 30, 2021
Revisions Due: October 31, 2021
Final Decision: November 30, 2021
Final Manuscript: December 31, 2021
Dissemination, Composition and Review Procedures
A Call for Papers (CFP) will be circulated to invite submissions.
World leading researchers will be invited as authors.
To further attracting contributors from around the world, the CFP will be advertised across numerous society newsletters, different websites, mailing lists, conferences, associations, and social media groups, etc.
This special issue will run as per the timeline given from submission to publication, while maintaining the rigorous peer review and high standards of the journal. All manuscripts submitted must be original, not under consideration elsewhere, and not previously published. A guide for authors and other relevant information for submission of manuscripts are available on the Guide for Authors’ page. Authors can expect their manuscripts to be reviewed fairly, and in a skilled, conscientious manner. To enhance objectivity, and to guarantee high scientific quality and relevance to the subject, three peer reviewers will be selected to evaluate a manuscript. The peer review process shall be designed to avoid bias and conflict of interest on the part of reviewers and shall be composed of experts in the relevant field of research. A key criterion in publication decisions will be the manuscript’s fit for the special issue and the readership of KBS. Papers will be published online as soon as accepted in continuous flow.
The submission system will be open around one week before the first paper comes in. When submitting your manuscript please select the article type “VSI: Deep Learning”. Please submit your manuscript before the submission deadline.
All submissions deemed suitable to be sent for peer review will be reviewed by at least two independent reviewers. Once your manuscript is accepted, it will go into production, and will be simultaneously published in the current regular issue and pulled into the online Special Issue. Articles from this Special Issue will appear in different regular issues of the journal, though they will be clearly marked and branded as Special Issue articles.
Please see an example here: https://www.sciencedirect.com/journal/science-of-the-total-environment/special-issue/10SWS2W7VVV
Please ensure you read the Guide for Authors before writing your manuscript. The Guide for Authors and the link to submit your manuscript is available on the Journal’s homepage.
Lead Guest Editor
Dr. Nian Zhang, University of District of Columbia, Washington, D.C., USA. [email protected]
Personal webpage: https://www.udc.edu/research/facultystaff-profiles/nian-zhang-phd/
Dr. Zhigang Zeng, Huazhong University of Science and Technology, Wuhan, China. [email protected]
Personal webpage: http://aia.hust.edu.cn/zhigangzeng/
Dr. Yaochu Jin, University of Surrey, Guildford, United Kingdom. [email protected]
Personal webpage: https://www.surrey.ac.uk/people/yaochu-jin
Brief Biography of Guest Editors
Dr. Nian Zhang is a Professor in the Department of Electrical and Computer Engineering at the University of the District of Columbia (UDC), Washington, D.C., USA. She received her Ph.D. degree in Computer Engineering from Missouri University of Science & Technology, USA. Her research interests include computational intelligence, machine learning, deep learning, supervised and unsupervised learning, classification, clustering, and optimization, neurodynamic optimization, and various application fields including big data science, time series prediction, biomedical engineering, and autonomous robot navigation. She was awarded numerous federal grants from the National Science Foundation, Department of Defense, and National Institutes of Health as the PI/Co-PI, accumulating over $4.5 million in research grant funds. Dr. Zhang serves as an Associate Editor for the IEEE Transactions on Cybernetics, IEEE Transactions on Neural Networks and Learning Systems, and Knowledge-based Systems. She also serves on the Editorial Board for the Complex & Intelligent Systems. In addition, Dr. Zhang served as a Guest Editor for the International Journal of Computational Intelligence Systems, Complex & Intelligent Systems, Computational Intelligence and Neuroscience, Cybernetics and Systems, International Journal on Artificial Intelligence Tools, and International Journal of Systems, Control and Communications. Further, Dr. Zhang serves as the Vice Chair of the IEEE Computational Intelligence Society (CIS) Emergent Technologies Technical Committee and the Vice Chair of the IEEE CIS Adaptive Dynamic Programming and Reinforcement Learning Technical Committee. She regularly serves as the Program Chair, Publications Chair, and Special Sessions Chair of the IEEE technically co-sponsored conferences such as ICACI, ICICIP, ICIST, and ISNN. Dr. Zhang received the UDC’s faculty recognition awards for Excellence in Research Award, Excellence in Teaching Award, and Outstanding Undergraduate Research Mentorship Award in three consecutive years.
Dr. Zhigang Zeng received the Ph.D. degree in systems analysis and integration from the Huazhong University of Science and Technology, Wuhan, China, in 2003. He is currently a Professor with the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, and also with the Key Laboratory of Image Processing and Intelligent Control of the Education Ministry of China, Wuhan. He has authored or coauthored more than 200 international journal papers. His current research interests include the theory of functional differential equations and differential equations with discontinuous right-hand sides, and their applications to dynamics of neural networks, memristive systems, and control systems. Dr. Zeng has been a member of the Editorial Board of Neural Networks since 2012, Cognitive Computation since 2010, and Applied Soft Computing since 2013. He was an Associate Editor of the IEEE TRANSACTIONS ON NEURAL NETWORKS from 2010 to 2011. He has been an Associate Editor of the IEEE TRANSACTIONS ON CYBERNETICS since 2014 and the IEEE TRANSACTIONS ON FUZZY SYSTEMS since 2016. He is recognized as a Highly Cited Researcher 2017-2020 by the Web of Science Group. He is a Fellow of IEEE.
Dr. Yaochu Jin received the BSc, MSc, and PhD degrees from Zhejiang University, Hangzhou, China, in 1988, 1991, and 1996, respectively, and the Dr.-Ing. degree from Ruhr University Bochum, Germany, in 2001. He is currently a Distinguished Chair, Professor in Computational Intelligence, Department of Computer Science, University of Surrey, Guildford, U.K., where he heads the Nature Inspired Computing and Engineering Group. He was a “Finland Distinguished Professor” of University of Jyvaskyla, Finland, a “Changjiang Distinguished Visiting Professor”, Northeastern University, China, and “Distinguished Visiting Scholar”, University of Technology Sydney, Australia. His main research interests include data-driven surrogate-assisted evolutionary optimization, trustworthy machine learning, multi-objective evolutionary learning, swarm robotics, and evolutionary developmental systems. Dr. Jin is presently the Editor-in-Chief of the IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS and the Editor-in-Chief of Complex & Intelligent Systems. He was an IEEE Distinguished Lecturer, and Vice President of the IEEE Computational Intelligence Society. He is the recipient of the 2018 and 2020 IEEE Transactions on Evolutionary Computation Outstanding Paper Award, the 2014, 2016, and 2019 IEEE Computational Intelligence Magazine Outstanding Paper Award, and the Best Paper Award of the 2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology. He is recognized as a Highly Cited Researcher 2019 by the Web of Science Group. He is a Fellow of IEEE.