In recent times, effectively measuring uncertainty in data with fuzzy characteristics has become a notable challenge. To tackle this issue, researchers have extensively investigated several extensions of fuzzy logic, including fuzzy set (FS), intuitionistic fuzzy set (IFS), and neutrosophic set (NS). However, a fundamental issue persists in these models: the computation of the complement of truth or falsity becomes problematic in the presence of indeterminacy. In contrast to the indeterminacy described in neutrosophic set theory, some real-world situations can exhibit conditions that are entirely true, partially true, or partially false. To better model such nuanced situations, the theory of ambiguous set (AS) has been recently introduced. This study introduces comparative study of ambiguous set theory with respect to FS, IFS and NS.
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