Special Issue “Fuzzy Machine Learning Algorithms with Applications Arising in Physical Problems”

Special Issue Editors

Dr. Ali Ahmadian (Lead Guest Editor)
University Mediterranea of Reggio Calabria, Reggio Calabria, Italy

Dr. Ahmad Taher Azar
Prince Sultan University, Saudi Arabia

Dr. Soheil Salahshour|
Bahcesehir University, Turkey

Dr. Shun-Feng Su
Chair Professor, EE, NTUST, Taiwan

Special Issue Information

One of the important topics in the applied science is dynamic systems.  If these systems are involved with complex-uncertain data, then they will be more important and practical. Because the real-life problems, uncertainties play a dominant role in the process and hence if as such data is used to process then it will give some incorrect results. Thus, it is very useful to handle such uncertainties in the data to achieve the desired results. In this regard, the role of employing differential equations with uncertain parameters is inevitable. To this address, a concept of a fuzzy differential equation has been considered.  However, in recent years, scientists found the applicability of these significant notion measure uncertainties in mathematical modeling with uncertain parameters. Therefore, a number of researches have been done in this regard to analyze the mathematical systems based on the fuzzy/interval parameters and study the real-world systems based on fuzzy mathematical modeling. As a matter of fact, fuzzy computing is a branch of fuzzy settings theory that we deal with the uncertain parameters from the first step of modeling or numerical algorithm that can reduce the complexity and computational difficulties compared with stochastic or random-based systems.

On the other hand, in the majority of machine learning (ML) algorithms, a mapping between the input points into a high-dimensional feature space and find a separating hyperplane that maximizes the margin between two classes in this space.  However, in many applications, some input points may not be exactly assigned to one of these two classes. Some are more important to be fully assigned to one class so that algorithm can separate these points more correctly. Some data points corrupted by noises are less meaningful and the machine should better discard them. Hence, ML lacks this kind of ability. Fuzzy membership to each input point of ML and reformulates ML into fuzzy ML (FML) such that different input points can make different contributions to the learning of decision surface.  Then, it can make the algorithms more efficient due to solid fuzzy theory, high generalization capability and ability to find global minima for solutions.

This special issue will provide a systematic overview and state-of-the-art research in the field of Intelligent Decision systems with machine learning applications and will outline new and important developments in fundamentals, approaches, models, methodologies, and applications in this area.

Specific topics of interest include (but are not limited to):

  • Foundation of fuzzy machine learning algorithms
  • Fuzzy and interval support vector machines 
  • Interval integral differential equations
  • Fuzzy Machine learning hybrid approach for accurate mathematical modeling
  • Prediction of behaviours of dynamical systems using fuzzy K-mean clustering
  • Evolutionary process of fuzzy fractional back-propagation neural networks
  • Artificial life simulations based on fuzzy neural networks
  • Optimum fuzzy support vector machines using error distribution 
  • Fuzzy machine learning algorithms for solving fuzzy life science systems
  • Classification problems using interval type-2 fuzzy support vector machines

Manuscript Submission Information

All manuscripts must be submitted through the manuscripts system at https://www.editorialmanager.com/ijfs/default.aspx 

Please select the designated special issue (SI) in the additional information Questionnaire (the fourth step). 

A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page.


Submission Deadline: 30 July 2021
Authors Notification: 20 September 2021
Revised Papers Deadline: 25 December 2021
Final Notification: 31 March 2022