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Top1. Introduction
Zadeh (1965) introduced the theory of fuzzy sets to overcome the fuzziness in decision-making problems. Fuzzy set has a membership function, µ that assigns to each element of the universe of discourse, a number from the unit interval, [0,1] to indicate the degree of belongingness to the set under consideration. Nonetheless, fuzzy set theory could not precisely handle the imprecisions imbedded in decision-making. As a result, numerous generalizations of fuzzy sets such intuitionistic fuzzy sets (IFSs), etc. were introduced. Atanassov (1986, 1989) proposed the idea of IFSs by incorporating both the membership function, µ and non-membership function, ν with hesitation margin, π such that their sum is one (i.e., 𝜇+v+𝜋=1) with the property that 𝜇+v≤1. IFS provides a framework for reasonably curbing uncertainties and as such very applicable in modelling many real-life problems (Atanassov, 2012). Davvaz and Sadrabadi (2016) studied several distance measures between IFSs and applied them to medical diagnosis. Ejegwa and Modom (2015) proposed a new distance measure and showed its application in the diagnosis of hepatitis. Many authors have used several methods on IFSs with application to medical diagnosis as can be found in (Ejegwa et al., 2014d; Ejegwa and Onasanya, 2019; Ejegwa and Adamu, 2019; De et al., 2001; Szmidt and Kacprzyk, 2001). The determination of career choice has been extensively studied via the concept of IFSs (Ejegwa et al., 2014a, b). To enhance career choice of a larger population, an object oriented approach to the application of IFSs in competency based test evaluation was considered in (Ejegwa and Onyeke, 2019). The idea of max-min-max composite relation on IFSs was applied to appointment of positions as seen in (Ejegwa, 2015), and sundry problems such as research questionnaire, electoral system using the framework of IFSs have been studied (Ejegwa et al., 2014c; 2016). The concept of intuitionistic fuzzy linear programming and its application to multi-attribute decision-making has been studied in (Li and Wan, 2013; Wan and Li, 2015). Several applications of IFSs and interval-valued IFSs to decision-making problems were discussed in (Li and Wan, 2014; 2017; Li and Ren, 2015; Wei et al., 2019; Yu et al., 2018; 2019).
The study of aggregation operators on IFSs based on entropy weight, t-norm operations, and connection numbers set pair analysis have been carried out in (Garg and Kumar, 2019; Garg, 2019; Kaur and Garg, 2018) with some applications in MCDM problems. Garg (2018) proposed an improved cosine similarity measures for intuitionistic fuzzy sets and their applications to decision-making process, and some novel distance measures for cubic intuitionistic fuzzy sets were studied in (Garg and Kaur, 2018) with their applications in pattern recognitions and medical diagnosis, respectively. Numerous tools were proposed in IFS framework to enhance the applications of IFSs as discussed in (Boran and Akay, 2014; Hatzimichailidis et al., 2012; Li and Wan, 2017; Ye, 2011; Szmidt, 2014a, b; Szmidt and Kacprzyk, 2000; Wang and Xin, 2005; Liang and Shi, 2003; Iqbal and Rizwan, 2019).