Immovable Cultural Relics Disease Prediction Based on Relevance Vector Machine

Abstract

The preventive cultural relics protection is one of the most concerned contents in archaeology, which includes environmental monitoring and accurate prediction of cultural relics diseases. In view of the deficiency of the analysis of cultural relics data and the prediction of cultural relics diseases, a prediction model of immovable cultural relics diseases based on relevance vector machine (RVM) is proposed. The key factors affecting the disease of immovable cultural relics are found out by the principal component analysis method, and the dimension reduction of data is realized; then, the RVM model under the framework of Bayesian theory is constructed, and the super parameters are estimated by the maximum edge likelihood method; finally, the prediction accuracy of the model is compared with the traditional diseases prediction methods. The experiment results demonstrate that the proposed RVM-based immovable cultural relics disease prediction approach not only has the advantages of more sparse model but also has better prediction accuracy than the traditional radial basis function neural network-based and support vector machine-based methods.

Based on Virtual Reality Technology Research on Innovation and Design of Ceramic Painting Products

“…Ceramics originated in the late Neolithic Age. After a long period of development and accumulation, ceramics developed rapidly in the Tang and Song dynasties and reached prosperity in the Ming and Qing Dynasties [12]. According to the records, the Ming Dynasty pottery production had been "initially divided into eight processes" and firing technology and product quality were greatly improved.…”

Research on the Identification and Application of Immovable Cultural Relics in the Historic City of Macau Based on Condition of Intelligent Remote Sensing Technology

“…Figure 8 represents the training and validation data for the machine learning algorithm that has been developed. The study findings stated that implementation of RVM-DP and SVM-DP methods in predicting immovable relics has generated 90% of accuracy [39]. This study showed 99% accuracy in the process of evaluation.…”

A color prediction model for mending materials of the Yuquan Iron Pagoda based on machine learning

“…Deep Neural Network (DNN) and Support Vector Machine (SVM) have been used to classify architectural heritage [14]. Machine learning aids in the conservation of immovable artifacts, with models like Relevance Vector Machine (RVM) predicting diseases and the Gray Model (GM) and Verhulst model forecasting crack trends in immovable artifacts [15][16][17]. In architectural heritage conservation, machine learning models, including ANN and logistic regression, have been employed to predict the aging degree and remaining life of heritage buildings [18,19].…”