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- W4387110629 abstract "The hesitant bi-fuzzy set (HBFS) proved to be a remarkable extension to fuzzy set theory as it allows the decision makers to deal with every hesitancy index without any restrictions. Distance and similarity measures have a vital role in day-to-day life such as decision making, supply selection problem, medical diagnosis, pattern recognition, and soft computation. In this study, we have introduced a novel series of distance and similarity measures of HBFS. An algorithm for hesitant bi-fuzzy TOPSIS has also been developed for the validation of these measures in multicriteria decision-making (MCDM). To test the efficacy of the developed method, a sensitivity analysis along with comparative analysis is carried out. The HBF framework of the established method is more flexible and superior to the existing method, because of extending the range of addresses and disagreement. Finally, a comparative study has also been done to examine the applicability of defined measures in pattern recognition problems." @default.
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- W4387110629 date "2023-01-01" @default.
- W4387110629 modified "2023-09-28" @default.
- W4387110629 title "Distance and Similarity Measures of Hesitant Bi-Fuzzy Set and Its Applications in Pattern Recognition Problem" @default.
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- W4387110629 doi "https://doi.org/10.1007/978-981-99-4577-1_7" @default.
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