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

Yanru Wang1

  • 1School of Art, Dance Studies, Wuhan Sports University, Wuhan, 430079, Hubei, China. yanrudance@163.com.

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|October 23, 2025
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Summary

This study introduces a new hybrid framework using circular Fermatean fuzzy sets (CFFS) to improve automated dance classification. The CFF-MARCOS approach enhances model selection accuracy and decision clarity for recognizing diverse dance styles.

Keywords:
Circular Fermatean fuzzy setsDance classificationDeep learning modelsMARCOS approachMulti-criteria decision-making

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Robotics

Background:

  • Deep learning models are used for automated dance classification via movement analysis.
  • Transfer learning enhances recognition accuracy across datasets.
  • Selecting optimal models for dance classification presents challenges due to uncertainty.

Purpose of the Study:

  • To propose a hybrid framework for identifying and classifying dance styles using pretrained deep learning models.
  • To introduce a novel circular Fermatean fuzzy measurement of alternatives and ranking based on the compromise solution (CFF-MARCOS) approach.
  • To address uncertainty and ambiguity in expert evaluations for model selection.

Main Methods:

  • A hybrid framework combining pretrained deep learning models with a novel decision-making approach.
  • Integration of circular Fermatean fuzzy sets (CFFS) into the MARCOS method.
  • Evaluation using a case study with ten pretrained models, seven criteria, and three experts.

Main Results:

  • The proposed CFF-MARCOS method demonstrated superiority in ranking pretrained models for dance classification.
  • The framework generated robust and interpretable rankings, enhancing decision reliability.
  • Improved clarity in selecting the best models for automated dance classification tasks was achieved.

Conclusions:

  • The hybrid framework with CFF-MARCOS offers a more refined approach to automated dance classification.
  • This method effectively handles hesitation and ambiguity in expert decision-making.
  • The study highlights advancements in automated recognition of diverse dance styles.