AI Summary & Key Takeaways
AI InsightThe results show that EMA18an and EMA18on outperform CE10an and CE10on, respectively.
📌 3 Key Takeaways
Abstract
The uni-int decision-making method, which selects a set of optimum elements from the alternatives, was defined by Çağman and Enginoğlu via soft sets and their soft products. Lately, this method constructed by and-product/or-product has been configured by Enginoğlu and Memiş via fuzzy parameterized fuzzy soft matrices (fpfs-matrices), faithfully to the original, because a more general form is needed for the method in the event that the parameters or objects have uncertainties. In this study, we configure the method via fpfs-matrices and andnot-product/ornot-product, faithfully to the original. However, in the case that a large amount of data is processed, the method still has a disadvantage regarding time and complexity. To deal with this problem and to be able to use this configured method effectively denoted by CE10, we suggest two new algorithms in this paper, i.e. EMA18an and EMA18on, and prove that CE10 constructed by andnot-product (CE10an) and constructed by ornot-product (CE10on) are special cases of EMA18an and EMA18on, respectively, if first rows of the fpfs-matrices are binary. We then compare the running times of these algorithms. The results show that EMA18an and EMA18on outperform CE10an and CE10on, respectively. Particularly in problems containing a large amount of parameters, EMA18an and EMA18on offer up to 99.9966% and 99.9964% of time advantage, respectively. Latterly, we apply EMA18on to a performance-based value assignment to the methods used in noise removal, so that we can order them in terms of performance. Finally, we discuss the need for further research.