The application of artificial intelligence in the revitalization of intangible cultural heritage helps the cultural industry succeed

Abstract

The integration of artificial intelligence (AI) in revitalizing intangible cultural heritage (ICH) necessitates solutions to enhance participation and preserve culture, thereby contributing to the growth of the cultural industry. The objective of this research is to design an AI-driven model utilizing an Adaptive Donkey and Smuggler Algorithm-mutated Malleable Long Short-Term Memory (ADS-MLSTM) network to enhance the recognition, preservation, and revitalization of ICH, supporting cultural industry growth and sustainability. Data were collected from multiple ICH archives, including digital representations of cultural heritage. This collected data underwent preprocessing steps such as noise reduction and data cleaning to ensure robustness against the diverse representations of ICH. Utilizing the Term Frequency-Inverse Document Frequency (TF-IDF) method, features were extracted efficiently. The integration of ADS and MLSTM algorithms in the proposed ADS-MLSTM model demonstrates superior performance, achieving a precision of 98.70%, a mean squared error (MSE) of 0.73, a recall of 98.27%, an F1-score of 98.80%, an accuracy of 99%, and a root mean squared error (RMSE) of 0.57, further highlighting its effectiveness. The incorporation of deep learning significantly enhanced the model’s effectiveness, leading to better results in recognizing diverse ICH elements. AI plays an essential role in recovering intangible cultural assets, particularly through the ADS-MLSTM model. By improving ICH recognition and fostering user interaction, AI-driven approaches contribute to the growth of the cultural industry, offering an innovative solution for preserving and promoting heritage.