Complex linear Diophantine fuzzy sets and their cosine similarity measures with applications

Abstract

In this paper, the concept of complex linear Diophantine fuzzy set (CLDFS), which is obtained by integrating the phase term into the structure of the linear Diophantine fuzzy set (LDFS) and thus is an extension of LDFS, is introduced. In other words, the ranges of grades of membership, non-membership, and reference parameters in the structure of LDFS are extended from the interval [0, 1] to unit circle in the complex plane. Besides, this set approach is proposed to remove the conditions associated with the grades of complex-valued membership and complex-valued non-membership in the framework of complex intuitionistic fuzzy set (CIFS), complex Pythagorean fuzzy set (CPyFS), and complex q-rung orthopair fuzzy set (Cq-ROFS). It is proved that each of CIFS, CPyFS, and Cq-ROFS is a CLDFS, but not vice versa. In addition, some operations and relations on CLDFSs are derived and their fundamental properties are investigated. The intuitive definitions of cosine similarity measure (CSM) and cosine distance measure (CDM) between two CLDFSs are introduced and their characteristic principles are examined. An approach based on CSM is proposed to tackle medical diagnosis issues and its performance is tested by dealing with numerical examples. Finally, a comparative study of the proposed approach with several existing approaches is created and its advantages are discussed.

Intelligent search system for resume and labor law

“…In practice, many methods can be used for matching CVs and JDs ( Tejaswini et al, 2022 ; Kostis et al, 2022 ). However, Moreover, the three baseline methods, including Jaro–Winkler distance ( Boudjedar et al, 2021 ), cosine similarity ( Kamacı, 2022 ), and Levenshtein distance ( Arockiya Jerson & Preethi, 2023 ), are usually applied in practical systems because their effectiveness and profit. Cosine similarity is a metric used to measure how similar two vectors are.…”

Optimization Algorithms of PERT/CPM Network Diagrams in Linear Diophantine Fuzzy Environment

“…1 provides a brief overview of the comparison between IFS, PyFS, q-ROFS, and LDFS. LDFS has its unique feature by incorporating the reference parameter and got many real-life applications with the help of different algorithms and operators like Dijkstra algorithm in LDFS environment [36], Einstein aggregation operators for multi-criteria decision-making [37], TOPSIS, VIKOR and Aggregation Operators [38], q-linear Diophantine fuzzy emergency decision support system [39], cosine similarity measures [40]. Also when we consider CPM/PERT got their one results and optimizes the project.…”

Analyzing of optimal classifier selection for EEG signals of depression patients based on intelligent fuzzy decision support systems

“…Previous research includes LDFW geometric aggregation (LDFWGA) operator by Raiz and Hashmi 23 , LDF Einstein aggregation operators developed by Lampan et. al, 25 , q-LDF weighted averaging and geometric aggregation operators presented by Almagrabi et.al 27 , and several more DM problems 22 , 24 , 26 . By solving the information data with some previous operators and coming to the same optimal decision, we equate our findings.…”

“…Lampan et al 25 developed LDF Einstein's aggregation to solve decision-making problems in LDFS settings. In 26 , Kamaci extended the LDFS concept to algebraic structures and produced a large number of algebraic structures under LDFS. The authors of 27 created a new concept called q-rung linear Diophantine fuzzy set (q-RLDFS) and applied it to COVID19.…”