**6. Comparison**

The RS describes a target set by a lower and upper approximation based on single granulation. However, the multiple granulation with approximations of a target set is needed in many real world problems as well. For example, Qian et al. [41,42] built a framework of OMGRS and PMGRS by getting inspiration of multi-source datasets and multiple granulation is needed by multi-scale data for set approximations [73]. Many things are different when comparing our work with existing theories. Mainly, we make a note on the differences of our work and existing ones, such as angle of thinking, MGRS environment and research objective. Our research with respect to the angle of thinking is different from other existing theories. For a comparative study, our proposed model transforms decision-making systems into a formal decision context. Our study is different from the existing ones in [41,63,74] in terms of MGRS because our work is about IFSs which are useful in dealing with uncertainty. In [63], Shabir et al. used crisp sets to present MGRS model based on soft relations. Later, they used a FS instead of a crisp set and presented OMGFRS [64]. We extended the OMGFRS model in terms of IFS and proposed OMGIFRS model based on soft binary relations to make better decision in decision making-problems. An IFS is better than a crisp set or a FS to discuss the uncertainty. In IFS, an element is described with membership degree as well as non-membership degree but in FS, an element is described with membership degree only. That is why our proposed model has more capability to reveal the uncertainty because of IFS. Furthermore, we have used soft relations which have many applications in dealing with uncertainty because of its parameterized collection of binary relations.
