In this paper, uncertainty has been measured in the form of fuzziness which arises due to imprecise boundaries of fuzzy sets Uncertainty caused due to human's cognition can be decreased by the use of fuzzy soft sets There are different approaches to deal with the measurement of uncertainty The method we proposed uses fuzzified evidence theory to calculate total degree of fuzziness of the parameters It consists of mainly four parts The first part is to measure uncertainties of parameters using fuzzy soft sets and then to modulate the uncertainties calculated Afterward, the appropriate basic probability assignments with respect to each parameter are produced In the last, we use Dempster's rule of combination to fuse independent parameters into integrated one To validate the proposed method, we perform an experiment and compare our outputs with grey relational analysis method Also, a medical diagnosis application in reference to COVID-19 has been given to show the effectiveness of advanced method by comparing with other method © 2018 Tsinghua University Press
An advanced uncertainty measure using fuzzy soft sets: Application to decision-making problems