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Preserving India's Rich Dance Heritage: A Classification of Indian Dance Forms and Innovative Digital Management Solutions for Cultural Heritage Conservation

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dc.contributor.author Tiwari, Raj Gaurang
dc.contributor.author Gautam, Vinay
dc.contributor.author Sharma, Vikrant
dc.contributor.author Jain, Anuj Kumar
dc.contributor.author Trivedi, Naresh Kumar
dc.date.accessioned 2024-01-16T04:44:02Z
dc.date.available 2024-01-16T04:44:02Z
dc.date.issued 2023
dc.identifier.citation Tiwari Raj Gaurang; Gautam Vinay; Sharma Vikrant; Jain Anuj Kumar; Trivedi Naresh Kumar (2023), Preserving India's Rich Dance Heritage: A Classification of Indian Dance Forms and Innovative Digital Management Solutions for Cultural Heritage Conservation, International Research Conference on Smart Computing and Systems Engineering (SCSE 2023), Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka. Page 16 en_US
dc.identifier.uri http://repository.kln.ac.lk/handle/123456789/27354
dc.description.abstract Deep connections exist between dance and cultural heritage. Dance is frequently passed down through generations as an essential component of a culture's identity and as a means of maintaining and honoring that culture's distinctive traditions and customs. A culture's history, beliefs, and values can be powerfully expressed via dance, which can also be used for communication and storytelling. For the purpose of protecting and promoting India's cultural legacy, it is crucial to comprehend how Indian dance styles are categorized. This paper proposes a hybrid Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) deep learning approach for the accurate classification of Indian dance style categories. The proposed model combines the strengths of both CNN and RNN to leverage spatial and temporal information, respectively, resulting in enhanced performance and improved accuracy. Extensive experiments were conducted to evaluate the performance of the proposed approach. The results demonstrate that the hybrid CNN-RNN model achieved an impressive accuracy of 97.74%, outperforming traditional methods and single-model architectures. en_US
dc.publisher Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka en_US
dc.subject CNN, computer vision, cultural heritage, dance classification, deep learning, RNN en_US
dc.title Preserving India's Rich Dance Heritage: A Classification of Indian Dance Forms and Innovative Digital Management Solutions for Cultural Heritage Conservation en_US


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