Bipolar fuzzy attribute implications

A Knowledge-Based System for the Classification of Cancer Diseases in Human Body Using Hybrid Approaches Based on Bipolar Complex Fuzzy Information

“…In this scenario, BFSs include massive data than FSs, which have more value. Certain scholars introduced various structures in the environment of BFS, like Jana et al [9], Jana et al [10], Yager and Rybalov [11], and Mesiar et al [12] investigated various AOs for BFS, Jana [13] propounded MABAC approach for extending BFS, Singh [14] investigated bipolar fuzzy (BF) attribute implications, and Guterrez et al [15] introduced BF measures. Also, certain scholars generalized the theory of BFS such as Naz et al [16] described 2-tuple linguistic BF, Liu et al [17] defined bipolar hesitant fuzzy linguistic, Mandal [18], propounded bipolar Pythagorean FS, Riaz and Tehrim [19] investigated geometric AOs for cubic BFS (CBFS), Riaz et al [20] studied Einstein averaging AOs for CBFS.…”

Identification and Prioritization of DevOps Success Factors Using Bipolar Complex Fuzzy Setting With Frank Aggregation Operators and Analytical Hierarchy Process

“…With the help of BFS, experts very easily determined their main target, which was impossible under the consideration of classical information. Certain applications of the BFS have been done by distinct intellectuals, for instance, hybrid aggregation operators [16,17], utilization of soft sets [18,19], simple aggregation operators [20][21][22], uniforms [23], 2tuple linguistic sets [24], ordered weighted averaging [25], decision-making [26][27][28][29], Bipolar fuzzy (BF) graph [30,31], multi-criteria DM (MCDM) in the setting of BF theory [32], BF TOPSIS and ELECTRE-I methods [33], and BF hypergraphs [34]. Noticed from decision-making scenario and described information available in the above paragraph, mentioned that the existing theories are limited features to handle awkward and unworthy information, but continuously unsuccessful to handle its fluctuations at a provided phase of time.…”