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

[M Tanveer]

*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

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

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
----------------------------------------------------------
Dr. M. Tanveer (General Chair - ICONIP 2022)
Associate Professor and Ramanujan Fellow
Department of Mathematics
Indian Institute of Technology Indore
Email: mtanveer at iiti.ac.in
Mobile: +91-9413259268
Homepage: http://iiti.ac.in/people/~mtanveer/

Associate Editor: IEEE TNNLS (IF: 10.45).

Associate Editor: Pattern Recognition, Elsevier (IF: 7.74).

Action Editor: Neural Networks, Elsevier  (IF: 8.05).

Board of Editors: Engineering Applications of AI, Elsevier (IF: 6.21).

Associate Editor: Neurocomputing, Elsevier  (IF: 5.72).

Editorial Board: Applied Soft Computing, Elsevier  (IF: 6.72).

Associate Editor: Cognitive Computation, Springer (IF: 5.42).

Associate Editor: International Journal of Machine Learning & Cybernetics
(IF: 4.012).
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.srv.cs.cmu.edu/pipermail/connectionists/attachments/20220318/1bbcede9/attachment.html>