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Top1. Introduction
Zadeh (1965) introduced fuzzy set theory, which is a proper tool to process fuzzy information. Atanassov (1986) added a non-membership function to solve the problems which contain incomplete information and also generalized fuzzy sets to intuitionistic fuzzy sets. In IFSs, hesitancy degree handles uncertain information but it does not manage indeterminate information properly because of its dependency upon membership and non-membership functions. Therefore, to handle this problem, Smarandache (1998) introduced neutrosophic sets by adding an indeterminacy membership degree so that all membership degrees are independent of each other. NSs deal with incongruous, indeterminate and uncertain information but cannot be applied to real world situations. To overcome this problem, Wang, Smarandache, Zhang and Sunderraman (2010) introduced single-valued neutrosophic sets. Many researchers worked on applications of real-world problems using SVNSs (Nguyen et al., 2019; Zhang et al., 2018; Liu & Liu, 2018; Sahin & Liu, 2017; Deli & Subas, 2017; Ye, 2017b; Sahin & Liu, 2016; Wang & Li, 2018; Zhang et al., 2014). Ye (2014) combined SVNSs and interval-valued NSs and introduced simplified neutrosophic sets (SNSs) and there are many remarkable contributions on SNSs (Tian et al., 2018; Luo et al., 2017; Ye, 2017a; Peng et al., 2016; Wu et al., 2016; Peng et al., 2014).
Many extensions of neutrosophic sets such as interval neutrosophic set (Liu & Shi, 2015), bipolar neutrosophic set (Ulucay et al., 2018), single-valued neutrosophic set (Biswas et al., 2016), simplified neutrosophic sets (Akram et al., 2019; Ali et al. 2020; Edalatpanah & Smarandache, 2019; Wu et al., 2016; Ye, 2020), multi-valued neutrosophic set (Ji et al., 2018), and neutrosophic linguistic set (Wang et., 2018) have been introduced and applied in various fields to solve different problems (Abdel Basset et al. 2018; Umar & Saraswat, 2020a).