Information measures based on similarity under neutrosophic fuzzy environment and multi-criteria decision problems
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10253791" target="_blank" >RIV/61989100:27240/23:10253791 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0952197623002105?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0952197623002105?via%3Dihub</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.engappai.2023.106026" target="_blank" >10.1016/j.engappai.2023.106026</a>
Alternative languages
Result language
angličtina
Original language name
Information measures based on similarity under neutrosophic fuzzy environment and multi-criteria decision problems
Original language description
The neutrosophic fuzzy set (NF-set) is a unique hybrid structure that combines the essence of the fuzzy and neutrosophic sets. It is an effective mathematical tool for dealing with indeterminate and inconsistent information in situations where the data are imprecise or vague. This paper defines the concepts of similarity -based information measures, including entropy and cross-entropy, between NF-sets. It is the first time these concepts have been introduced since the NF-sets were defined. We provide their interesting properties through well-proven theorems. In addition, we also propose a novel and efficient algorithm to make multi-criteria decision(s) using these information measures with a clear step-by-step illustration. It overcomes the limitations of the original algorithm when evaluating the criteria in two aspects: qualitative and quantitative. A real-world experiment is then conducted to assist students in choosing the optimal subject group(s) for the Vietnamese national high school graduation examination. Experimental results show that our method outperforms the previous original method when giving recommendations with sixteen correct cases, three acceptable cases, and one noisy case out of twenty real-life cases. Finally, the experimental results are presented visually, analyzed rigorously, and discussed carefully, intending to verify the validity and feasibility of the proposed algorithm. The Python source code for experiments is publicly available on Github.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10200 - Computer and information sciences
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Engineering Applications of Artificial Intelligence
ISSN
0952-1976
e-ISSN
1873-6769
Volume of the periodical
122
Issue of the periodical within the volume
červen 2023
Country of publishing house
GB - UNITED KINGDOM
Number of pages
16
Pages from-to
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UT code for WoS article
000955173500001
EID of the result in the Scopus database
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