A binary logarithm similarity measure with roughness approximation of rough neutrosophic set for Covid 19 cases / Muhammad Naim Haikal Yaso' and Hazlin Shahira Ramlee

The findings of the similarity measure between two or more expert-provided information are categorized as either a strong or a weak relationship. As a result, getting the results for the similarity measure as the best conclusion for the information relationship is important. Based on the previous studies, the binary logarithm similarity measure was chosen as the similarity measure approach in this study. In addition, a rough neutrosophic set was chosen as the uncertainty set theory information, which includes the upper and lower approximation with a boundary set was chosen as the set theory application. The objectives of this study are to define binary logarithm similarity measure for rough neutrosophic sets, to formulate the properties satisfied the binary logarithm similarity measure of rough and to develop a decision making model by using a binary logarithm similarity measure for case study (COVID 19). The roughness approximation is used in the definition of the binary logarithm similarity measures. Following that, the derivation algorithm for identifying the most important priority group for COVID 19 vaccine is presented. The roughness approximation for a rough neutrosophic set is used to compare the similarity results. The proving result is finalised. Then, the derivation of binary logarithm similarity measures of rough neutrosophic set is well defined. As a validation process, the similarity properties for identifying the most important priority group for COVID 19 vaccine used such as age, health state, women and job kinds. Finally, if either value of the similarity measure is close to one, a strong relationship between the information given or vice versa is defined.