Prediction of Glass Chemical Composition and Type Identification Based on Machine Learning Algorithms

Prediction of Glass Chemical Composition and Type Identification Based on Machine Learning Algorithms

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Ancient glass artifacts were susceptible to weathering from the environment, causing changes in their chemical composition, which pose significant obstacles to the identification of glass products.Analyzing the chemical composition of ancient glass has been beneficial for evaluating their weathering status and proposing measures to reduce glass weathering.The objective of this study was to explore the weekend friend juneshine optimal machine learning algorithm for glass type classification based on chemical composition.A set of glass artifact data including color, emblazonry, weathering, and chemical composition was employed and various methods including logistic regression and machine learning techniques were used.

The results indicated that a significant correlation (p 2O, BaO, SiO2, Al2O3, and P2O5.The random forest model presented a superior ability to hp victus 15-fa0031dx identify glass types and weathering status, with a global accuracy of 96.3%.This study demonstrates the great potential of machine learning for glass chemical component estimation and glass type and weathering status identification, providing technical guidance for the appraisal of ancient glass artifacts.