A Generalisation of Fuzzy Soft Max-Min Decision-Making Method and Its Application to a Performance-Based Value Assignment in Image Denoising

English (OpenAlex)

Latterly, the fuzzy soft max-min decision-making method denoted by FSMmDM and provided in [Çağman, N., Enginoǧlu, S., Fuzzy soft matrix theory and its application in decision making, Iranian Journal of Fuzzy Systems, 2012, 9(1), 109-119] has been configured via fuzzy parameterized fuzzy soft matrices (fpfs -matrices) by Enginoğlu and Memiş [A configuration of some soft decision-making algorithms via fpfs -matrices, Cumhuriyet Science Journal, 2018, 39(4), 871-881], faithfully to the original. Although this configured method denoted by CE12 and constructed by and-product/or-product (CE12a/CE12o) is useful in decision-making, the method should be made more attractive in terms of time and complexity in the event that a large amount of data is processed. In this paper, we propose two algorithms EMC19a and EMC19o that accept CE12a and CE12o as a special case, respectively, in the event that the first rows of the fpfs -matrices are binary. That is, EMC19a and EMC19o are new generalized forms of FSMmDM. Afterwards, we compare the running times of these algorithms. The results show that EMC19a and EMC19o outperform CE12a and CE12o, respectively, in any number of data. We then apply EMC19o to a decision-making problem in image denoising. Finally, we discuss the need for further research