Machine Learning Algorithms under Uncertainty: Real-world Systems

in Special Issue   Posted on January 25, 2021 

Information for the Special Issue

Submission Deadline: Fri 30 Jul 2021
Journal Impact Factor : 4.406
Journal Name : International Journal of Fuzzy Systems
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Website for the Special Issue:
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Special Issue Call for Papers:

Special Issue Editors

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

Dr. Ahmad Taher AzarPrince Sultan University, Saudi Arabia

Dr. Soheil Salahshour|Bahcesehir University, Turkey

Dr. Shun-Feng SuChair Professor, EE, NTUST, Taiwan

Special Issue Information

Due to the existence of uncertainty in the structure of modelling, uncertainties play a major role in the dynamical processes. So, appearance of such uncertainties in the data is inevitable, and consequently, applying uncertain differential equations is a natural way to respond to the situations. Because of the presence of uncertainty, obtaining an exact solution for such systems is not applicable. For responding to this essential restriction, several numerical methods were applied to derive the approximate solutions. However, working with large systems need to be applied some adaptive approach like using machine learning algorithms.

Machine learning (ML) algorithms focus on separating hyperplane to maximizes the margin between two classes in this space.  For real-world applications, the input of the system should be considered under uncertainty. For such restriction in comparison to the deterministic ML, we need to provide the membership with uncertainty to each input point of ML and reformulates ML into uncertain ML (FML). Using this realization, the ML algorithms will be more applicable, global minima of the original problem will be determined better, as well.

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):

  • Uncertain Machine learning algorithms
  • Fuzzy support vector machines 
  • Integral differential equations with uncertainty
  • Machine learning hybrid approach for accurate mathematical modelling under uncertainty
  • Fractional back-propagation neural networks with uncertain parameters
  • Neural networks with fuzzy parameters
  • Uncertain Machine learning algorithms for solving real-world systems with vagueness

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

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