A Bayesian and SVM-based model for compositional analysis and identification of glass artifacts

Zaixin Lin; Huiyan He; Zihui Huang; Shijie Wang; Guangshun Chen; Tianyuan Zhu

doi:10.1117/12.2686271

28 July 2023 A Bayesian and SVM-based model for compositional analysis and identification of glass artifacts

Zaixin Lin, Huiyan He, Zihui Huang, Shijie Wang, Guangshun Chen, Tianyuan Zhu

Proceedings Volume 12756, 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023); 127561O (2023) https://doi.org/10.1117/12.2686271
Event: 2023 3rd International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2023), 2023, Tangshan, China

Abstract

In order to better target the protection of glass artifacts, this paper proposes a composition analysis and identification model of glass artifacts based on Bayesian and SVM, adopts a variety of analysis methods to extract and pre-process the data, establishes a composition analysis and identification model of artifacts and predicts the results for the next composition of glass artifacts, and finally validates the model, and the experimental results show that the SVM model obtains a high performance with high generalizability

Citation Download Citation

Zaixin Lin, Huiyan He, Zihui Huang, Shijie Wang, Guangshun Chen, and Tianyuan Zhu "A Bayesian and SVM-based model for compositional analysis and identification of glass artifacts", Proc. SPIE 12756, 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023), 127561O (28 July 2023); https://doi.org/10.1117/12.2686271

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