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Top1. Introduction
In 1986, Atanassov developed the IFS theory, which is the extension of Zadeh's fuzzy set theory. Similarity and entropy measures are two essential tools for dealing with uncertainty through the IFS theory. Different similarity and entropy measures have been proposed and applied successfully in many areas. Similarity measures defined from the well-known distance measures are depicted by Smidh et al. (2000), Wang et al. (2005), Grzegorzewski (2004), Chen (2007), Hung et al. (2007). Li and Cheng (2002), Liang and Shi (2003), Hwang et al. (2012), Xu (2007), and Xu and Yager (2009) gave several new similarity measures for IFSs. Mitchell (2003) developed a statistical method of Dengfeng and Chuntian's similarity measure by giving some counterintuitive cases. Ye (2011) has compared the existing similarity measures and proposed new and weighted similarity measures using the cosine function. Xu and Chen (2008) developed a series of similarity measures by generalizing the weighted Hamming distance, the weighted Euclidean distance, and the weighted Hausdorff distance. Xia and Xu (2010) and Zeng and Guo (2008) worked on distance, similarity, and entropy measures and studied their relationship. A generalization of the existing entropy measures for IFSs is proposed by Wei et al. (2011). Boran and Akay (2014) introduced a new general type of similarity measure for IFS relating two parameters norm and the level of uncertainty. Li et al. (2012) studied both the similarity measure and entropy measure for IFSs by defining an axiomatic approach to the similarity measure. Entropy is an effective measure to give a picture of the fuzziness of a fuzzy set. Many researchers have defined many entropy measures. Bhandari and Pal (1993), Luca and Termini (1972), Fan and Ma (2002), Shore and Gray (1982), Zhang and Jiang (2008), Ye (2010), Verma and Sharma (2013), pal and pal (1989), Wei et al. (2012), Wang and Wang (2012), Liu and Ren (2014), Song et al. (2014), Szmidt and Kacprzyk (2001), Vlachos and Sergiadis (2007), Burillo and Bustince (1996), Zeng and Li (2006), Zhang and Zhang (2009), Farhadinia (2013), Liu (1992), Zeng and Li (2006), Zeng and Guo (2008), Li and Deng (2012), Zhang et al. (2014), Garge et al. (2011), Hung and Yang (2006) worked on entropy measure for IFSs with a different aspect. Along with the study of entropy measures, Li et al. (2010, 2011, 2014, 2015, 2016, 2017) have worked to develop many ranking processes of IFS and Interval Valued Intuitionistic Fuzzy Sets (IVIFSs). In the field of IVIFSs, Talukdar et al. (2019) have also proposed a novel ranking method to enhance the efficiency of the process. Some novel entropy measures have been developed by Thao (2021), Verma et al. (2017), Wei et al. (2019), Zhu et al. (2016), Wei et al. (2021), Li et al. (2015), Li et al. (2002), Li et al. (2004) with a lot of different perspective.