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Optimized deep learning for Indian Classical Dance Classification a novel application based on a refined version of

Di Zhu1, Jinliang Sun2, Yanwen Lu1

  • 1Department of Physical Education, Shandong University of Political Science and Law, Jinan, 250000, Shandong, China.

Scientific Reports
|December 25, 2025
PubMed
Summary

This study introduces a hybrid Deep Belief Network and Refined Chameleon Swarm Algorithm (DBN/RCSA) model for classifying Indian Classical Dance (ICD) styles. The DBN/RCSA model achieves 95% accuracy, offering a reliable solution for cultural preservation and digital heritage.

Keywords:
Deep belief networkDeep learningImage classificationIndian classical danceOptimizationRefined chameleon swarm algorithm

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Cultural Heritage Studies

Background:

  • Indian Classical Dance (ICD) classification from visual data is challenging due to subtle variations and complex pose representations.
  • Existing deep learning models often suffer from suboptimal parameter tuning, limiting their performance in ICD style classification.

Purpose of the Study:

  • To develop and validate a novel hybrid model for accurate classification of Indian Classical Dance styles.
  • To optimize Deep Belief Network (DBN) performance using an improved metaheuristic algorithm for enhanced ICD classification.

Main Methods:

  • A Refined Chameleon Swarm Algorithm (RCSA) was developed with a non-linear adaptive weight mechanism and Bernoulli chaotic map to optimize DBN parameters.
  • The hybrid DBN/RCSA model was trained and validated on the Indian Dance form Classification (ICD) and Bharatnatyam Dance Poses (BDP) datasets.
  • Performance was evaluated using 5-fold cross-validation against state-of-the-art methods, including DCNN, PointNet, and Transfer Learning.

Main Results:

  • The DBN/RCSA model achieved superior performance with 95% accuracy, 94% precision, sensitivity, specificity, and F1 score.
  • Ablation studies confirmed RCSA's crucial role in enhancing DBN performance.
  • Confusion matrix analysis demonstrated the model's robust distinguishing ability across different ICD classes.

Conclusions:

  • The DBN/RCSA model represents a significant advancement in automated Indian Classical Dance style classification.
  • The model's high accuracy and reliability make it suitable for applications in cultural preservation, dance pedagogy, and digital heritage studies.