Special Issue "Extension, Modeling and Applications of Fuzzy Set Theory in Engineering and Science"

Submission Deadline: 15 February 2022 (closed)
Guest Editors
Prof. Jun Ye, Ningbo University, China
Prof. Yanhui Guo, University of Illinois at Springfield, USA


There exist the vagueness and uncertainty of human judgments and cognitions regarding complicated real-world problems. Aiming at the practical problems of uncertainty and incompleteness, the fuzzy set proposed by Zadeh has been widely used in various fields. Due to the need for better and detailed membership functions in real science and engineering problems, the classical fuzzy sets have been extended to type-2 fuzzy sets, hesitant fuzzy sets, multi-valued fuzzy sets, cubic sets, intuitionistic fuzzy sets, Pythagorean fuzzy sets, spherical fuzzy sets, neutrosophic sets, etc. Each of them is gaining significant attention in science and engineering areas. Then, these fuzzy set theories have been used in decision making, artificial intelligence, image processing, medical diagnosis, fault diagnosis, optimization design/programming, clustering analysis, big and small data mining, engineering modeling and analysis, etc. In recent years, various fuzzy set theories have made new progress and achievements in the engineering and scientific fields.

The focus of this special issue is the extension, modeling and applications of fuzzy set theory to solve engineering and scientific problems. Articles submitted to this special issue can also be concerned with various fuzzy set theories, modeling, and applications in decision making, artificial intelligence, big and small data mining, pattern recognition, information processing, medical diagnosis, faulty diagnosis, image processing, and many other practical modeling and analysis, etc. We invite researchers to contribute original research articles and review articles, which will stimulate continuous research on various fuzzy set theories, modeling and applications to evaluate/solve engineering and scientific problems.

Fuzzy set extension; Modeling and analysis; Engineering and scientific applications

Published Papers

  • On Soft Pre-Rough Approximation Space with Applications in Decision Making
  • Abstract A soft, rough set model is a distinctive mathematical model that can be used to relate a variety of real-life data. In the present work, we introduce new concepts of rough set based on soft pre-lower and soft pre-upper approximation space. These concepts are soft pre-rough equality, soft pre-rough inclusion, soft pre-rough belonging, soft predefinability, soft pre-internal lower, and soft pre-external lower. We study the properties of these concepts. Finally, we use the soft pre-rough approximation to illustrate the importance of our method in decision-making for Chikungunya medical illnesses. In reality, the impact factors of Chikungunya’s medical infection were determined.… More
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  • Interval-Valued Neutrosophic Soft Expert Set from Real Space to Complex Space
  • Abstract A fuzzy system is a novel computing technique that accesses uncertain information by fuzzy representation. In the decision-making process, fuzzy system and soft computing are effective tools that are tolerant to imprecision, uncertainty, and partial truths. Evolutionary fuzzy systems have been developed with the appearance of interval fuzzy, dual fuzzy, hesitant fuzzy, neutrosophic, plithogenic representations, etc. Moreover, by capturing compound features and convey multi-dimensional data, complex numbers are utilized to generalize fuzzy and neutrosophic fuzzy sets. In this paper, a representation of neutrosophic soft expert systems based on the real and complex numbers in the interval form is proposed. The… More
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  • A Personalized Comprehensive Cloud-Based Method for Heterogeneous MAGDM and Application in COVID-19
  • Abstract This paper proposes a personalized comprehensive cloud-based method for heterogeneous multi-attribute group decision-making (MAGDM), in which the evaluations of alternatives on attributes are represented by LTs (linguistic terms), PLTSs (probabilistic linguistic term sets) and LHFSs (linguistic hesitant fuzzy sets). As an effective tool to describe LTs, cloud model is used to quantify the qualitative evaluations. Firstly, the regulation parameters of entropy and hyper entropy are defined, and they are further incorporated into the transformation process from LTs to clouds for reflecting the different personalities of decision-makers (DMs). To tackle the evaluation information in the form of PLTSs and LHFSs, PLTS… More
  •   Views:882       Downloads:522        Download PDF

  • Research on Normal Pythagorean Neutrosophic Set Choquet Integral Operator and Its Application
  • Abstract We first propose the normal Pythagorean neutrosophic set (NPNS) in this paper, which synthesizes the distribution of the incompleteness, indeterminacy, and inconsistency of the Pythagorean neutrosophic set (PNS) and normal fuzzy number. We also define some properties of NPNS. For solving the decision-making problem of the non-strictly independent and interacting attributes, two kinds of NPNS Choquet integral operators are proposed. First, the NPNS Choquet integral average (NPNSCIA) operator and the NPNS Choquet integral geometric (NPNSCIG) operator are proposed. Then, their calculating formulas are derived, their properties are discussed, and an approach for solving the interacting multi-attribute decision making based on… More
  •   Views:499       Downloads:411        Download PDF