## 6 Conclusions  
This research introduces a technique designed to parse and verify address quality correctly and efficiently. The proposed model architecture is based on a deep learning type of recurrent neural network. The model is built for classifying the address components that have been parsed from the full address for verification. Our technique does not use the full address. Instead, we work on each address component parsed from the full address string. Figure 9: Location for the manufacturer address provided by Google Map service. In this study, a deep learning model that verifies the addresses of foreign food manufacturers in the field of food safety was constructed and its quality accuracy was evaluated. In the field of food safety, the accuracy of the address quality itself is important, but recognizing the administrative district to which the company belongs for the address string is important for a prompt response in case of harm. Administrative district datasets for each country were constructed manually based on the open-address data from Postcode Query website for each country to create the deep learning classification model. A classification model was distinctively created for each country in consideration of the different address systems. When the address string is input, the address is parsed into address components using Google geocoding service and the deep learning model classifies each address component into an appropriate level of administrative district level. In addition, the input address in this process is also converted into a standardized address. After building and evaluating a model based on the Chinese address data, the accuracy of the classification model is greater than 90% and its precision, recall, and F1 score are all above 90%. For the experiments, the manufacturers' addresses of three countries, including China, Japan, and Cambodia, were examined and evaluated. Based on the examination of three distinct nations, the experimental findings demonstrate that the suggested method is significantly capable of verifying and evaluating the quality of overseas address data. The results show that the address data quality accuracy was 87.31%, 89.77%, and 77.30% for China, Japan, and Cambodia, respectively. By configuring and preprocessing the training dataset well, it is possible to overcome problems, such as errors, abbreviations, and synonyms, due to differences in English pronunciation in address, which are known to be difficult in address verification. The deep-learning-based technique in this research is also able to assist any organization or business to build their own address quality verification system without depending on a commercial service. However, in this study, there is a limitation in terms of scope of research. We only focused on three different overseas countries, such as China, Japan, and Cambodia. The research is expected to extend to more countries in the future. The research can also be continued in order to improve the performance and accuracy of the model. The technique in the proposed method, such as address parsing, will be considered to be updated. Currently, we utilize the service of Google that offers the limitation of the usage. Therefore, we plan to make our own way in parsing the full address string into components. Conceptualization, methodology and software, K.-H.L. ; software, validation, and investigation, S.S.; writing\(-\)original draft preparation, W.-S.C.; formal analysis, J.-H.B. ; supervision and project administration, K.-H.L. All authors have read and agreed to the published version of the manuscript. This research was supported by a grant (21163 MFDS517-1) from the Ministry of Food and Drug Safety of South Korea in 2022. Not applicable.