A novel decision-making approach for the selection of best deep learning techniques under logarithmic fractional fuzzy set information

Deep learning (DL), which is a branch of machine learning (ML) and artificial intelligence (AI), has become a fundamental element of contemporary technological advancements. To facilitate such processes, data representation is crucial, often transitioning from crisp sets to generalized forms like fuzzy sets (FS), introduced by Zadeh. This paper extends the concept by defining a special class of FS known as Logarithmic Fractional Fuzzy Sets (Log-FFS). Moreover, a set of aggregation operators (AoPs) is formulated using logarithmic operational principles, such as the Logarithmic Fractional Fuzzy Weighted Average (Log-FFWA) and its variations, with their core properties thoroughly detailed. The study also incorporates known methodologies such as the Complex Proportional Assessment (COPRAS) and an extended TOPSIS method under Log-FFS information. Finally, the proposed approaches are confidently applied to selecting deep learning techniques, demonstrating their capability to yield optimal results.