Analysis of the Composition of Ancient Glass and Its Identification Based on the Daen-LR, ARIMA-LSTM and MLR Combined Process
The glass relics are precious material evidence of the early trade and cultural exchange between the East and the West. To explore the cultural differences and trade development between early China and foreign countries, it is extremely important to classify glass cultural relics. Despite their simi...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-05-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/11/6639 |
| _version_ | 1797597879031824384 |
|---|---|
| author | Zhi-Xing Li Peng-Sen Lu Guang-Yan Wang Jia-Hui Li Zhen-Hao Yang Yun-Peng Ma Hong-Hai Wang |
| author_facet | Zhi-Xing Li Peng-Sen Lu Guang-Yan Wang Jia-Hui Li Zhen-Hao Yang Yun-Peng Ma Hong-Hai Wang |
| author_sort | Zhi-Xing Li |
| collection | DOAJ |
| description | The glass relics are precious material evidence of the early trade and cultural exchange between the East and the West. To explore the cultural differences and trade development between early China and foreign countries, it is extremely important to classify glass cultural relics. Despite their similar appearances, Chinese glass contains more lead, while foreign glass contains more potassium. In view of this, this paper proposes a joint Daen-LR, ARIMA-LSTM, and MLR machine learning algorithm (JMLA) for the analysis and identification of the chemical composition of ancient glass. We separate the sampling points of ancient glass into two systems: lead-barium glass and high-potassium glass. Firstly, an improved logistic regression model based on a double adaptive elastic network (Daen-LR) is used to select variables with both Oracle and adaptive classification characteristics. Secondly, the ARIMA-LSTM model was used to establish the correlation curve of chemical composition before and after weathering and to predict the change in chemical composition with weathering. Thirdly, combining the data processed by the above two methods, a multiple linear regression model (MLR) is used to classify unknown glass products. It was shown that the sample obtained by this processing method has a very good fit. In comparison with other similar types of models like Decision Trees (DT), Random Forests (RF), Support Vector Machines (SVM), and Random Forests based on classification and regression trees (CART-RF), the classification accuracy of JMLA is 97.9% on the train set. The accuracy rate on the test set reached 97.6%. The results of the research demonstrate that JMLA can improve the accuracy of the glass type classification problem, greatly enhance the research efficiency of archaeological staff, and gain a more reliable result. |
| first_indexed | 2024-03-11T03:11:37Z |
| format | Article |
| id | doaj.art-931818cee15242608cf1cca7a120de75 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T03:11:37Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-931818cee15242608cf1cca7a120de752023-11-18T07:34:47ZengMDPI AGApplied Sciences2076-34172023-05-011311663910.3390/app13116639Analysis of the Composition of Ancient Glass and Its Identification Based on the Daen-LR, ARIMA-LSTM and MLR Combined ProcessZhi-Xing Li0Peng-Sen Lu1Guang-Yan Wang2Jia-Hui Li3Zhen-Hao Yang4Yun-Peng Ma5Hong-Hai Wang6School of Information Engineering, Tianjin University of Commerce, Tianjin 300134, ChinaSchool of Information Engineering, Tianjin University of Commerce, Tianjin 300134, ChinaSchool of Information Engineering, Tianjin University of Commerce, Tianjin 300134, ChinaSchool of Information Engineering, Tianjin University of Commerce, Tianjin 300134, ChinaSchool of Information Engineering, Tianjin University of Commerce, Tianjin 300134, ChinaSchool of Information Engineering, Tianjin University of Commerce, Tianjin 300134, ChinaSchool of Chemical Engineering and Technology, National-Local Joint Engineering Laboratory for Energy Conservation in Chemical Process Integration and Resources Utilization, Hebei University of Technology, Tianjin 300130, ChinaThe glass relics are precious material evidence of the early trade and cultural exchange between the East and the West. To explore the cultural differences and trade development between early China and foreign countries, it is extremely important to classify glass cultural relics. Despite their similar appearances, Chinese glass contains more lead, while foreign glass contains more potassium. In view of this, this paper proposes a joint Daen-LR, ARIMA-LSTM, and MLR machine learning algorithm (JMLA) for the analysis and identification of the chemical composition of ancient glass. We separate the sampling points of ancient glass into two systems: lead-barium glass and high-potassium glass. Firstly, an improved logistic regression model based on a double adaptive elastic network (Daen-LR) is used to select variables with both Oracle and adaptive classification characteristics. Secondly, the ARIMA-LSTM model was used to establish the correlation curve of chemical composition before and after weathering and to predict the change in chemical composition with weathering. Thirdly, combining the data processed by the above two methods, a multiple linear regression model (MLR) is used to classify unknown glass products. It was shown that the sample obtained by this processing method has a very good fit. In comparison with other similar types of models like Decision Trees (DT), Random Forests (RF), Support Vector Machines (SVM), and Random Forests based on classification and regression trees (CART-RF), the classification accuracy of JMLA is 97.9% on the train set. The accuracy rate on the test set reached 97.6%. The results of the research demonstrate that JMLA can improve the accuracy of the glass type classification problem, greatly enhance the research efficiency of archaeological staff, and gain a more reliable result.https://www.mdpi.com/2076-3417/13/11/6639Daen-LRARIMA-LSTMMLRmachine learningcultural heritageancient glass classification |
| spellingShingle | Zhi-Xing Li Peng-Sen Lu Guang-Yan Wang Jia-Hui Li Zhen-Hao Yang Yun-Peng Ma Hong-Hai Wang Analysis of the Composition of Ancient Glass and Its Identification Based on the Daen-LR, ARIMA-LSTM and MLR Combined Process Applied Sciences Daen-LR ARIMA-LSTM MLR machine learning cultural heritage ancient glass classification |
| title | Analysis of the Composition of Ancient Glass and Its Identification Based on the Daen-LR, ARIMA-LSTM and MLR Combined Process |
| title_full | Analysis of the Composition of Ancient Glass and Its Identification Based on the Daen-LR, ARIMA-LSTM and MLR Combined Process |
| title_fullStr | Analysis of the Composition of Ancient Glass and Its Identification Based on the Daen-LR, ARIMA-LSTM and MLR Combined Process |
| title_full_unstemmed | Analysis of the Composition of Ancient Glass and Its Identification Based on the Daen-LR, ARIMA-LSTM and MLR Combined Process |
| title_short | Analysis of the Composition of Ancient Glass and Its Identification Based on the Daen-LR, ARIMA-LSTM and MLR Combined Process |
| title_sort | analysis of the composition of ancient glass and its identification based on the daen lr arima lstm and mlr combined process |
| topic | Daen-LR ARIMA-LSTM MLR machine learning cultural heritage ancient glass classification |
| url | https://www.mdpi.com/2076-3417/13/11/6639 |
| work_keys_str_mv | AT zhixingli analysisofthecompositionofancientglassanditsidentificationbasedonthedaenlrarimalstmandmlrcombinedprocess AT pengsenlu analysisofthecompositionofancientglassanditsidentificationbasedonthedaenlrarimalstmandmlrcombinedprocess AT guangyanwang analysisofthecompositionofancientglassanditsidentificationbasedonthedaenlrarimalstmandmlrcombinedprocess AT jiahuili analysisofthecompositionofancientglassanditsidentificationbasedonthedaenlrarimalstmandmlrcombinedprocess AT zhenhaoyang analysisofthecompositionofancientglassanditsidentificationbasedonthedaenlrarimalstmandmlrcombinedprocess AT yunpengma analysisofthecompositionofancientglassanditsidentificationbasedonthedaenlrarimalstmandmlrcombinedprocess AT honghaiwang analysisofthecompositionofancientglassanditsidentificationbasedonthedaenlrarimalstmandmlrcombinedprocess |