Composition Analysis and Identification Study of Ancient Glass Products

DOI

10.2991/978-2-38476-004-6_53How to use a DOI?

Keywords

Ancient glass analysis identification; BP neural network model; Cluster analysis model; Significance analysis; Correlation analysis; Mathematical model

Abstract

This paper addresses the problem of compositional analysis and classification identification of excavated ancient glass artifacts, using data visualization, chi-square test, support vector machine (SVM) algorithm, logistic regression algorithm and random forest algorithm to establish a mathematical model for artifact identification, as well as further refinement of the classification considering the expertise related to ancient glass artifacts, in order to provide a reference for the identification of ancient glass artifacts. The highlights of this paper are: firstly, this question uses a variety of models to train and predict the data, realizing the mutual test of the prediction results and satisfying the accuracy of the established model; secondly, a normal distribution test is conducted on the data chemical composition data, screening out a suitable test method for the subsequent data testing and analysis; finally, a clustering analysis model and the existing literature are combined to make the excavated ancient glass.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jingrui Yi
AU  - Liya Song
AU  - Jiapeng Meng
PY  - 2023
DA  - 2023/03/01
TI  - Composition Analysis and Identification Study of Ancient Glass Products
BT  - Proceedings of the 2nd International Conference on Education, Language and Art (ICELA 2022)
PB  - Atlantis Press
SP  - 420
EP  - 431
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-004-6_53
DO  - 10.2991/978-2-38476-004-6_53
ID  - Yi2023
ER  -