# Dance classification using pretrained deep learning models integrated with the circular Fermatean fuzzy MARCOS method

## TL;DR

This paper introduces a new method combining deep learning and fuzzy logic to classify dance styles more accurately and reliably.

## Contribution

The novel integration of circular Fermatean fuzzy sets with the MARCOS method improves model selection for dance classification.

## Key findings

- The proposed CFF-MARCOS method outperforms existing approaches in model ranking for dance classification.
- Improved decision reliability and clarity are achieved through refined handling of uncertainty in expert evaluations.
- A case study with ten models and three experts validates the effectiveness of the hybrid framework.

## Abstract

Pretrained deep learning models offer a method for classifying dance by analyzing movement behaviors in videos or using sensors to assess the degree of automation in recognizing different types of dances. These models utilize transfer learning strategies to enhance recognition accuracy across various datasets. This study presents a hybrid framework for identifying and classifying dance styles using pretrained deep learning models, evaluated under multiple performance criteria. To address the inherent uncertainty and complexity in selecting the best-performing models, a novel circular Fermatean fuzzy measurement of alternatives and ranking based on the compromise solution (CFF-MARCOS) approach is proposed. Unlike existing methods, the integration of circular Fermatean fuzzy sets (CFFS) into the MARCOS framework enables more refined handling of hesitation and ambiguity in expert evaluations. A case study involving ten pretrained models, seven criteria, and three experts demonstrates the superiority of the proposed method in generating robust and interpretable rankings. Results highlight improved decision reliability and clarity in model selection for automated dance classification tasks.