Connectionists: IEEE TFS SI - Advances in fuzzy deep learning algorithms for biomedical data (Last Date - Dec. 01, 2023)

[M Tanveer]

Special Issue: Advances in fuzzy deep learning algorithms for biomedical
data

Aim and Scope:

Deep learning is one of the most important revolutions in the field of
artificial intelligence over the last decade. Approaches under this family
of models have achieved great success in different tasks such as computer
vision, image processing, biomedical analysis and related fields.
Researchers in deep and shallow machine learning including those working in
computer vision, image processing, biomedical analysis and other fields
comprising multi-dimensional data. Fuzzy set theory is a branch of
artificial intelligence capable of analysing complex biomedical data, which
has been one of the state of the art methodologies, leading to the enhanced
performance in various medical applications to prevent, diagnose, and treat
diseases. Compared to the traditional data analytics and decision support
techniques, fuzzy sets and their extensions are effective white-box tools
for representing and explaining the complexity and vagueness of the
information, especially to reduce uncertainty. However, the relatively low
learning efficiency and performance also hinder their applications in the
medical domain. Therefore, in the last few years, integrating deep learning
and fuzzy systems has been an emerging and promising topic with
applications in healthcare.

When tied with experienced clinicians, researchers in fuzzy deep learning
can play a significant role in understanding and working on complex medical
data, which ultimately leads to improved patient care. Developing novel
fuzzy deep learning algorithms suited to deal with medical data still
remains a challenge. Healthcare and biomedical sciences have become
data-intensive fields, with a strong need for sophisticated data mining
methods to extract knowledge from the available information. Biomedical
data pose several challenges in data analysis, including high
dimensionality, class imbalance and scarcity of annotated data featuring
enough quality for modelling purposes. Although current research in this
field has shown promising results, several research issues need to be
explored, including novel feature selection methods to improve predictive
performance along with interpretation, and to explore large scale data in
biomedical sciences.

This special issue aims to bring together the current research progress
(from both academia and industry) on fuzzy deep learning algorithms to
address the challenges of biomedical complex data.  Special attention will
be devoted to novel contributions related to feature selection, class
imbalance, data fusion, explainability and biomedical use cases comprising
real-world data. This special issue aims at providing an opportunity for
collecting some advanced work in the fuzzy deep learning, including
compilation of the latest research, development, and practical experiences
as well as up-to-date issues, reviewing accomplishments, assessing future
directions and challenges in this field. It will bring both researchers
from academia and practitioners from industry to discuss the latest
progress, new research topics, and potential application domains.

Topics:

The topics relevant to the special issue include (but are not limited to):

   -

   Fuzzy deep learning for computer aided detection and diagnosis
   -

   Fuzzy deep learning for neuroimaging
   -

   Fuzzy deep learning for radiographic data
   -

   Fuzzy deep learning for biomedical image classification and ROI
   localization
   -

   Fuzzy deep learning for genomics
   -

   Explainable fuzzy deep learning for prediction of healthcare variations
   -

   Fuzzy deep learning for multimodality neuroimaging data fusion systems
   -

   Fusion of fuzzy deep learning and big data for future challenges
   -

   Explainability of fuzzy deep learning in all its forms (counterfactuals,
   local explanations, relevance attribution, etc)
   -

   Advanced fuzzy deep learning techniques for the risk prediction of
   COVID-19





Submission guidelines:

 All authors should read ‘Information for Authors’ before submitting a
manuscript http://cis.ieee.org/ieeetransactions-on-fuzzy-systems.html

Submissions should be through the IEEE TFS journal website
http://mc.manuscriptcentral.com/tfs-ieee. It is essential that your
manuscript is identified as a Special Issue contribution.

Ensure you choose ‘Special Issue’ when submitting.

A cover letter must be included which includes the title “Advances in fuzzy
deep learning algorithms for biomedical data”.


Important Dates:

Submission Deadline: December 01, 2023

Notification of the first round review: February 2024

Revised submission due: May 2024

Final notification: August 2024


Guest Editors:

M. Tanveer, Indian Institute of Technology Indore, India (Lead)

Email: mtanveer at iiti.ac.in,

Homepage: http://people.iiti.ac.in/~mtanveer/
<http://people.iiti.ac.in/~mtanveer/>

Google Scholar Citations: 4330 with h-index 35

Chin-Teng Lin, University of Technology Sydney, Australia

Email: Chin-Teng.Lin at uts.edu.au,

Homepage: https://www.uts.edu.au/staff/chin-teng.lin

Google Scholar Citations: 34400 with h-index 90

Yu-Dong Zhang, University of Leicester, UK

Email: yudongzhang at ieee.org

Homepage: https://le.ac.uk/people/yudong-zhang

Google Scholar Citations: 27000 with h-index 91




----------------------------------------------------------

Dr. M. Tanveer (Founding Chapter Chair, IEEE CIS Chapter - MP Section)

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 Transactions on Neural Networks & Learning Systems
(IF: 10.4).

Action Editor: Neural Networks, Elsevier  (IF: 7.8).

Associate Editor: Pattern Recognition, Elsevier (IF: 8.0).

Editorial Board: Applied Soft Computing, Elsevier  (IF: 8.7).

Board of Editors: Engineering Applications of AI, Elsevier (IF: 8.0).

Associate Editor: Neurocomputing, Elsevier  (IF: 6.0).

Associate Editor: Cognitive Computation, Springer (IF: 5.4).

Associate Editor: International Journal of Machine Learning & Cybernetics
(IF: 5.6).

Lead Guest Editor: IEEE Transactions on Fuzzy Systems (IF: 11.9)
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