Volume 14, Issue 1, 2021, Pages 1700 - 1713
Improved Knowledge Measures for q-Rung Orthopair Fuzzy Sets
Authors
Muhammad Jabir Khan1, Poom Kumam1, 2, 3, *, Meshal Shutaywi4, Wiyada Kumam5
1KMUTT Fixed Point Research Laboratory, Science Laboratory Building, Department of Mathematics, Faculty of Science, King Mongkut’s University of Technology Thonburi (KMUTT), 126 Pracha-Uthit Road, Bang Mod, Thrung Khru, Bangkok, 10140, Thailand
2Center of Excellence in Theoretical and Computational Science (TaCS-CoE), SCL 802 Fixed Point Laboratory, Science Laboratory Building, King Mongkut’s University of Technology Thonburi (KMUTT), 126 Pracha-Uthit Road, Bang Mod, Thrung Khru, Bangkok, 10140, Thailand
3Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, 40402, Taiwan
4Department of Mathematics, College of Science and Arts, King Abdulaziz University, P. O. Box 344, Rabigh, 21911, Saudi Arabia
5Applied Mathematics for Science and Engineering Research Unit (AMSERU), Program in Applied Statistics, Department of Mathematics and Computer Science, Faculty of Science and Technology, Rajamangala University of Technology Thanyaburi (RMUTT), Thanyaburi, Pathumthani, 12110, Thailand
*Corresponding author. Email: poom.kum@kmutt.ac.th
Corresponding Author
Poom Kumam
Received 30 October 2020, Accepted 30 April 2021, Available Online 11 June 2021.
- DOI
- 10.2991/ijcis.d.210531.002How to use a DOI?
- Keywords
- Knowledge measure; q-rung orthopair fuzzy sets; Entropy; MAGDM
- Abstract
The q-rung orthopair fuzzy set (qROFS) defined by Yager is a generalization of Atanassov intuitionistic fuzzy set (IFS) and Pythagorean fuzzy sets (PyFSs). In this paper, we define the knowledge measure for qROFS by using the cosine inverse function. The information precision and information content are two facets of knowledge measure. Both facets of knowledge measure are considered. The properties of knowledge measure and their graphical explanations are discussed. An application of the knowledge measure in multi-attribute group decision-making (MAGDM) problem under the confidence level approach is given. A numerical example of the selection of renewable energy sources is discussed.
- Copyright
- © 2021 The Authors. Published by Atlantis Press B.V.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
Download article (PDF)
View full text (HTML)
Cite this article
TY - JOUR AU - Muhammad Jabir Khan AU - Poom Kumam AU - Meshal Shutaywi AU - Wiyada Kumam PY - 2021 DA - 2021/06/11 TI - Improved Knowledge Measures for q-Rung Orthopair Fuzzy Sets JO - International Journal of Computational Intelligence Systems SP - 1700 EP - 1713 VL - 14 IS - 1 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.d.210531.002 DO - 10.2991/ijcis.d.210531.002 ID - Khan2021 ER -