Decision Analysis Framework Based on Information Measures of T-Spherical Fuzzy Sets

T-spherical fuzzy sets (T-SFSs) have fascinated the desire of researchers in a wide range of domains. The striking framework of the T-SFS is keen to offer the larger inclination domain for the modeling of ambiguous information deploying the degrees of membership, neutral and non-membership. Further, T-SFSs prevail over the theories of picture fuzzy sets, spherical fuzzy sets, and Pythagorean fuzzy sets owing to their broader space, adjustable parameter, flexible structure, and influential design. The information measures, being significant part of the literature, are crucial and beneficial tools that are widely applied in making decision, mining data, diagnosis of the medical things and recognition of the pattern. This paper aims to expand the literature on T-SFSs by introducing many innovative T-spherical fuzzy set information measures, namely, distance measure, similarity measure, entropy measure, and inclusion measure. We investigate the relationship between distance, similarity, entropy, and inclusion measures for T-spherical fuzzy sets. Another achievement of this research is to establish a systematic transformation of information measures, measure distance, measure similarity, measure entropy, and measure inclusion for the T-SFSs. To accomplish this aim, new formula for information measure of T-SFSs have been provided. To demonstrate the criteria of the measures, we employ it to recognition pattern, building materials and diagnosis of the medical things. Additionally, a comparison between traditional and novel similarity measures is described in terms of counterintuitive cases. The outcomes demonstrate that the innovative information measures does not include any absurd cases.