Composition Analysis and Identification of Ancient Glass Products
DOI:
https://doi.org/10.54097/hset.v39i.6630Keywords:
Glass Composition Identification; Neural Network Algorithm; Random Forest Algorithm; K-Means Clustering Algorithm.Abstract
During the weathering process of ancient glass, due to the large amount of exchange between the internal elements and the environmental elements, the composition ratio of the glass changes, which affects the correct judgment of its category. In this paper, statistical analysis and classification of known data are carried out, and K-means algorithm of random forest and unsupervised learning is adopted to analyze and identify the components of glass products, and reasonable prediction is made for unknown types of glass products.
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