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Mathematics
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Fuzzy Set Theory and Its Application to Machine Learning
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Fuzzy Set Theory and Its Application to Machine Learning

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Fuzzy Sets, Systems and Decision Making".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 757

Special Issue Editor

Prof. Dr. Bahram Farhadinia

E-Mail Website
Guest Editor
School of Engineering, RMIT University, Melbourne, Australia
Interests: decision making; artificial intelligence; data mining; fuzzy optimization

Special Issue Information

Dear Colleagues,

Fuzzy Set Theory is a dynamic and thriving field in computing today. It has become an essential part of our daily lives, effectively being used to tackle real-world problems.

When applying Fuzzy Set Theory to practical scenarios, we encounter additional challenges, such as handling distorted, missing, large, and uncertain data. Furthermore, achieving interpretability is crucial for utilizing fuzzy methods. Interpretability allows us to comprehend the behavior of fuzzy models, boosting our confidence in the obtained results.

This Special Issue, titled "Fuzzy Set Theory and its Application to Machine Learning", covers a wide range of topics, including basic and applied research, artificial intelligence, decision-making, data analysis, data mining, finance and management, information systems, pattern recognition, operational research, and image processing. We anticipate that the submitted papers will showcase both the theoretical advancements and practical applications of Fuzzy Set Theory and Machine Learning. We welcome innovative approaches exploring the intersection of Fuzzy Set Theory and Machine Learning.

Prof. Dr. Bahram Farhadinia
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • fuzzy method
  • machine learning
  • soft computing
  • fuzzy systems in data processing
  • pattern recognition
  • artificial intelligence
  • intelligent health systems

Published Papers (1 paper)

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Research

19 pages, 1037 KiB  
Article
Utilizing Generative Adversarial Networks Using a Category of Fuzzy-Based Structural Similarity Indices for Constructing Datasets in Meteorology
by Bahram Farhadinia, Mohammad Reza Ahangari and Aghileh Heydari
Mathematics 2024, 12(6), 797; https://doi.org/10.3390/math12060797 - 8 Mar 2024
Viewed by 449
Abstract
Machine learning and image processing are closely related fields that have undergone major development and application in recent years. Machine learning algorithms are being used to develop sophisticated techniques for analyzing and interpreting images, such as object detection, image classification, and image segmentation. [...] Read more.
Machine learning and image processing are closely related fields that have undergone major development and application in recent years. Machine learning algorithms are being used to develop sophisticated techniques for analyzing and interpreting images, such as object detection, image classification, and image segmentation. One important aspect of image processing is the ability to compare and measure the similarity between different images by providing a way to quantify the similarity between images using various features such as contrast, luminance, and structure. Generally, the flexibility of similarity measures enables fine-tuning the comparison process to achieve the desired outcomes. This is while the existing similarity measures are not flexible enough to address diverse and comprehensive practical aspects. To this end, we utilize triangular norms (t-norms) to construct an inclusive class of similarity measures in this article. As is well-known, each t-norm possesses distinctive attributes that allow for novel interpretations of image similarities. The proposed class of t-norm-based structural similarity measures offers numerous options for decisionmakers to consider various issues and interpret results more broadly in line with their objectives. For more details, in the Experiments section, the proposed method is applied to grayscale and binarized images and a specific experiment related to meteorology. Eventually, the presented diverse case studies confirm the efficiency and key features of the t-norm-based structural similarity. Full article
(This article belongs to the Special Issue Fuzzy Set Theory and Its Application to Machine Learning)
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