Abstract
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.
Highlights
The fuzzy logics have emerged as a very important and useful topic in past recent years
Zadeh[1] presented the concept of fuzzy set theory in 1965 as a transformation of classical set theory
Various theories like classical set theory[2], fuzzy set theory[1], probability theory, possibility theory[3], and Dempster–Shafer evidence theory[4,5] have been given to deal with certain types of uncertainties
Summary
The fuzzy logics have emerged as a very important and useful topic in past recent years It has aroused as an important mathematical tool to deal with uncertainties and vagueness of data. Zadeh[1] presented the concept of fuzzy set theory in 1965 as a transformation of classical set theory It can solve the problems of decision-making and deal with the problem of vagueness, uncertainty, and imprecision of data. We have used fuzzified evidence theory[21] along with D–S theory to solve the problem of decision making. The first part involves the measurement of uncertainty of parameters in the form of total degree of fuzziness, the second part is the brief description of steps involved to solve the decision making problem, the third part performs an experiment (Example 3) to solve the problem, and the fourth part is a practical application of our proposed work to handle decision making problem in real-life situation (medical diagnosis).
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