Connectionists: (Impact Factor=6.2) EAAI Journal Special Issue on "randomization-based learning methods"

[Ponnuthurai Nagaratnam Suganthan]

Call for Papers

Journal:  Engineering Applications of Artificial Intelligence, Elsevier (IF: 6.2)

Special Issue Title:  Randomized Deep and Shallow Learning Algorithms

We welcome submissions on non-randomized algorithms with significant comparisons against randomized algorithms too.

Aim and Scope:

Randomization-based learning algorithms have received considerable attention from academics, researchers, and domain workers because randomization-based neural networks can be trained by non-iterative approaches possessing closed-form solutions. Those methods are in general computationally faster than iterative solutions and less sensitive to parameter settings. Even though randomization-based non-iterative methods have attracted much attention in recent years, their deep structures have not been sufficiently developed nor benchmarked. This special issue aims to bridge this gap.

The first target of this special issue is to present the recent advances of randomization- based learning methods. Randomization based neural networks usually offer non-iterative closed form solutions. Secondly, the focus is on promoting the concepts of non-iterative optimization with respect to counterparts, such as gradient-based methods and derivative-free iterative optimization techniques. Besides the dissemination of the latest research results on randomization-based and/or non-iterative algorithms, it is also expected that this special issue will cover some practical applications, present some new ideas and identify directions for future studies.

Original contributions as well as comparative studies among randomization-based and non-randomized-based methods are welcome with unbiased literature review and comparative studies. Typical deep/shallow paradigms include (but not limited to) random vector functional link (RVFL), echo state networks (ESN), liquid state networks (LSN), kernel ridge regression (KRR) with randomization, extreme learning machines (ELM), random forests (RF), CNN with randomization, broad learning system (BLS), stochastic configuration network (SCN) and so on. All contributions must include sufficient application contents.

Topics:

The topics of the special issue include (with randomization-based methods), but are not limited to:

l  Randomized convolutional neural networks

l  Randomized internal representation learning

l  Regression, classification and time series analysis by randomization-based methods

l  Kernel methods such as kernel ridge regression, kernel adaptive filters, etc. with randomization

l  Feedforward, recurrent, multilayer, deep and other structures with randomization

l  Ensemble learning with randomization

l  Moore-Penrose pseudo inverse, SVD and other solution procedures.

l  Gaussian process regression

l  Randomization-based methods using novel fuzzy approaches

l  Randomization-based methods for large-scale problems with and without kernels

l  Theoretical analysis of randomization-based methods

l  Comparative studies with competing methods without randomization

l  Applications of randomized methods and information fusion in areas such as power systems, biomedical, finance, economics, signal processing, big data and all other relevant areas


Submission Guideline:

Papers should be submitted using EAAI’s online submission system: https://www.journals.elsevier.com/engineering-applications-of-artificial-intelligence. When submitting your manuscript please select the article type "SI: Randomization-based learning algorithms".
Important Dates

·       Manuscript submission due: April 30, 2022

·       First review completed: July 15, 2022

·       Revised manuscript due: Aug 15, 2022

·       Final decisions to authors: Oct 30, 2022

Guest Editors:

Dr. P.N. Suganthan, IEEE Fellow
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
Email: epnsugan at ntu.edu.sg<mailto:epnsugan at ntu.edu.sg>
Website: https://www3.ntu.edu.sg/home/epnsugan/

Dr. M. Tanveer, IEEE Senior Member
Department of Mathematics, Indian Institute of Technology Indore
Email: mtanveer at iiti.ac.in<mailto:mtanveer at iiti.ac.in>
Website: ​http://iiti.ac.in/people/~mtanveer/

Prof. Chin-Teng Lin, IEEE Fellow, IFSA Fellow
Director, Computational Intelligence and Brain Computer Interface Centre
Co-Director, Centre for AI (CAI)
University of Technology Sydney, Australia
Email: Chin-Teng.Lin at uts.edu.au<mailto:Chin-Teng.Lin at uts.edu.au>



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