Special Issue "Recent Developments on Fuzzy Sets Extensions"

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Mathematics and Symmetry/Asymmetry".

Deadline for manuscript submissions: 15 May 2023 | Viewed by 683

Special Issue Editor

Prof. Dr. Cengız Kahraman
E-Mail
Guest Editor
Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey
Interests: engineering economics; quality control and management; statistical decision making; multicriteria decision making; fuzzy decision making
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue covers symmetry and asymmetry phenomena occurring in recent developments in fuzzy research problems. We invite authors to submit their theoretical or experimental research presenting engineering models under fuzziness dealing with the symmetry or asymmetry of different types of information.

This Special Issue is focused on the recent theoretical developments of ordinary fuzzy set extensions for modeling under vague and imprecise conditions. Topics of interest include, but are not limited to, the following theoretical and/or practical developments for modeling under fuzziness:

  • Type-2 fuzzy sets;
  • Hesitant fuzzy sets;
  • Intuitionistic fuzzy sets;
  • Spherical fuzzy sets;
  • Picture fuzzy sets;
  • Pythagorean fuzzy sets;
  • Q-rung orthopair fuzzy sets;
  • Neutrosophic sets;
  • Fermatean fuzzy sets;
  • Circular intuitionistic fuzzy sets.

Prof. Dr. Cengız Kahraman
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. Symmetry is an international peer-reviewed open access monthly 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 1800 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.

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Article
Supervised Machine Learning–Based Detection of Concrete Efflorescence
Symmetry 2022, 14(11), 2384; https://doi.org/10.3390/sym14112384 - 11 Nov 2022
Viewed by 416
Abstract
The development of automated systems for detecting defects in and damage to buildings is ongoing in the construction industry. Remaining aware of the surface conditions of buildings and making timely decisions regarding maintenance are crucial. In recent years, machine learning has emerged as [...] Read more.
The development of automated systems for detecting defects in and damage to buildings is ongoing in the construction industry. Remaining aware of the surface conditions of buildings and making timely decisions regarding maintenance are crucial. In recent years, machine learning has emerged as a key technique in image classification methods. It can quickly handle large amounts of symmetry and asymmetry in images. In this study, three supervised machine learning models were trained and tested on images of efflorescence, and the performance of the models was compared. The results indicated that the support vector machine (SVM) model achieved the highest accuracy in classifying efflorescence (90.2%). The accuracy rates of the maximum likelihood (ML) and random forest (RF) models were 89.8% and 87.0%, respectively. This study examined the influence of different light sources and illumination intensity on classification models. The results indicated that light source conditions cause errors in image detection, and the machine learning field must prioritize resolving this problem. Full article
(This article belongs to the Special Issue Recent Developments on Fuzzy Sets Extensions)
Show Figures

Figure 1

Back to TopTop