Deep Learning Based Improvement in Overseas Manufacturer Address Quality Using Administrative District Data
Validating and improving the quality of global address data are important tasks in a modern society where exchanges between countries are due to active Free Trade Agreements (FTAs) and e-commerce. Addresses may be constructed with different systems for each country; therefore, to verify and improve...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-11-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/21/11129 |
| _version_ | 1827647404382355456 |
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| author | Saravit Soeng Jin-Hyun Bae Kyung-Hee Lee Wan-Sup Cho |
| author_facet | Saravit Soeng Jin-Hyun Bae Kyung-Hee Lee Wan-Sup Cho |
| author_sort | Saravit Soeng |
| collection | DOAJ |
| description | Validating and improving the quality of global address data are important tasks in a modern society where exchanges between countries are due to active Free Trade Agreements (FTAs) and e-commerce. Addresses may be constructed with different systems for each country; therefore, to verify and improve the quality of the address data, it is necessary to understand the address system of each country in advance. In the event of food risk, it is important to identify the administrative district from the address in order to take safety measures, such as predicting the contaminated area by tracking the distribution of food in the area. In this study, we propose a method that applies a deep learning approach to verify and improve the quality of the global address data required for imported food-safety management. The address entered by the user is classified to the administrative division levels of the relevant country and the quality of the address data is verified and improved by converting them into a standardized address. Finally, the results show that the accuracy of the model is found to be approximately 90% and the proposed method is able to verify and evaluate the overseas address data quality significantly. |
| first_indexed | 2024-03-09T19:17:41Z |
| format | Article |
| id | doaj.art-fe2afa8bf59142b7b0473a00df82b94a |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T19:17:41Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-fe2afa8bf59142b7b0473a00df82b94a2023-11-24T03:38:38ZengMDPI AGApplied Sciences2076-34172022-11-0112211112910.3390/app122111129Deep Learning Based Improvement in Overseas Manufacturer Address Quality Using Administrative District DataSaravit Soeng0Jin-Hyun Bae1Kyung-Hee Lee2Wan-Sup Cho3Department of Bigdata, Chungbuk National University, Cheongju 28644, KoreaDepartment of Bigdata, Chungbuk National University, Cheongju 28644, KoreaDepartment of Bigdata, Chungbuk National University, Cheongju 28644, KoreaDepartment of Bigdata, Chungbuk National University, Cheongju 28644, KoreaValidating and improving the quality of global address data are important tasks in a modern society where exchanges between countries are due to active Free Trade Agreements (FTAs) and e-commerce. Addresses may be constructed with different systems for each country; therefore, to verify and improve the quality of the address data, it is necessary to understand the address system of each country in advance. In the event of food risk, it is important to identify the administrative district from the address in order to take safety measures, such as predicting the contaminated area by tracking the distribution of food in the area. In this study, we propose a method that applies a deep learning approach to verify and improve the quality of the global address data required for imported food-safety management. The address entered by the user is classified to the administrative division levels of the relevant country and the quality of the address data is verified and improved by converting them into a standardized address. Finally, the results show that the accuracy of the model is found to be approximately 90% and the proposed method is able to verify and evaluate the overseas address data quality significantly.https://www.mdpi.com/2076-3417/12/21/11129LSTMRNNdeep learningaddress verificationglobal address verification |
| spellingShingle | Saravit Soeng Jin-Hyun Bae Kyung-Hee Lee Wan-Sup Cho Deep Learning Based Improvement in Overseas Manufacturer Address Quality Using Administrative District Data Applied Sciences LSTM RNN deep learning address verification global address verification |
| title | Deep Learning Based Improvement in Overseas Manufacturer Address Quality Using Administrative District Data |
| title_full | Deep Learning Based Improvement in Overseas Manufacturer Address Quality Using Administrative District Data |
| title_fullStr | Deep Learning Based Improvement in Overseas Manufacturer Address Quality Using Administrative District Data |
| title_full_unstemmed | Deep Learning Based Improvement in Overseas Manufacturer Address Quality Using Administrative District Data |
| title_short | Deep Learning Based Improvement in Overseas Manufacturer Address Quality Using Administrative District Data |
| title_sort | deep learning based improvement in overseas manufacturer address quality using administrative district data |
| topic | LSTM RNN deep learning address verification global address verification |
| url | https://www.mdpi.com/2076-3417/12/21/11129 |
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