Examining the Efficiency of Learning-Based Algorithms in the Process of Declaring Customs
Having the declarations used in customs procedures be submitted without errors is critical. In the face of the diversity, dynamism, and complexity of the methods used in creating this declaration, human-induced declaration files are produced erroneously. These cause many problems such as loss of lab...
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| Format: | Article |
| Language: | English |
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Istanbul University Press
2022-12-01
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| Series: | Acta Infologica |
| Subjects: | |
| Online Access: | https://cdn.istanbul.edu.tr/file/JTA6CLJ8T5/98C1855791714246A5D21D3D5CE71D87 |
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| _version_ | 1849315398569164800 |
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| author | Mustafa Günerkan Ender Şahinaslan Önder Şahınaslan |
| author_facet | Mustafa Günerkan Ender Şahinaslan Önder Şahınaslan |
| author_sort | Mustafa Günerkan |
| collection | DOAJ |
| description | Having the declarations used in customs procedures be submitted without errors is critical. In the face of the diversity, dynamism, and complexity of the methods used in creating this declaration, human-induced declaration files are produced erroneously. These cause many problems such as loss of labor, customers, and money, as well as legal problems such as contract and legal compliance. Intelligent structures supported by current information technologies are needed to solve these problems. For this purpose, being able to use learning algorithms over big data is important in the field of customs declaration creation in the logistics industry. This study evaluates the efficiency performances of learning-based algorithms regarding the customs declaration process over 4,005,343 pieces of declaration data. According to the performance measurement results, the maximum result was achieved in the Decision Tree (75.69%) and Bagging (75.70%) algorithms with respect to the Train-test split method at a test rate of 25%. Regarding the K-Fold method, which assumes K to be equal to 10, similar success rates were obtained for the Decision Tree (75.84%) and Bagging (75.83%) algorithms. These results reveal the use of machine learning algorithms to be an effective method for detecting notification errors. This can be a resource for improving customs declaration processes and developing smart control structures, as well as for new studies to be carried out in the field. |
| format | Article |
| id | doaj-art-e3f210ea25274e1daf25af27ab1b7e00 |
| institution | Kabale University |
| issn | 2602-3563 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | Istanbul University Press |
| record_format | Article |
| series | Acta Infologica |
| spelling | doaj-art-e3f210ea25274e1daf25af27ab1b7e002025-08-20T03:52:08ZengIstanbul University PressActa Infologica2602-35632022-12-016217518810.26650/acin.1057060123456Examining the Efficiency of Learning-Based Algorithms in the Process of Declaring CustomsMustafa Günerkan0https://orcid.org/0000-0002-4202-2801Ender Şahinaslanhttps://orcid.org/0000-0001-8519-7612Önder Şahınaslan1https://orcid.org/0000-0003-2695-5078Maltepe Üniversitesi, Istanbul, TurkiyeMaltepe Üniversitesi, Istanbul, TurkiyeHaving the declarations used in customs procedures be submitted without errors is critical. In the face of the diversity, dynamism, and complexity of the methods used in creating this declaration, human-induced declaration files are produced erroneously. These cause many problems such as loss of labor, customers, and money, as well as legal problems such as contract and legal compliance. Intelligent structures supported by current information technologies are needed to solve these problems. For this purpose, being able to use learning algorithms over big data is important in the field of customs declaration creation in the logistics industry. This study evaluates the efficiency performances of learning-based algorithms regarding the customs declaration process over 4,005,343 pieces of declaration data. According to the performance measurement results, the maximum result was achieved in the Decision Tree (75.69%) and Bagging (75.70%) algorithms with respect to the Train-test split method at a test rate of 25%. Regarding the K-Fold method, which assumes K to be equal to 10, similar success rates were obtained for the Decision Tree (75.84%) and Bagging (75.83%) algorithms. These results reveal the use of machine learning algorithms to be an effective method for detecting notification errors. This can be a resource for improving customs declaration processes and developing smart control structures, as well as for new studies to be carried out in the field.https://cdn.istanbul.edu.tr/file/JTA6CLJ8T5/98C1855791714246A5D21D3D5CE71D87customs declarationlearning algorithmslogisticsbig data |
| spellingShingle | Mustafa Günerkan Ender Şahinaslan Önder Şahınaslan Examining the Efficiency of Learning-Based Algorithms in the Process of Declaring Customs Acta Infologica customs declaration learning algorithms logistics big data |
| title | Examining the Efficiency of Learning-Based Algorithms in the Process of Declaring Customs |
| title_full | Examining the Efficiency of Learning-Based Algorithms in the Process of Declaring Customs |
| title_fullStr | Examining the Efficiency of Learning-Based Algorithms in the Process of Declaring Customs |
| title_full_unstemmed | Examining the Efficiency of Learning-Based Algorithms in the Process of Declaring Customs |
| title_short | Examining the Efficiency of Learning-Based Algorithms in the Process of Declaring Customs |
| title_sort | examining the efficiency of learning based algorithms in the process of declaring customs |
| topic | customs declaration learning algorithms logistics big data |
| url | https://cdn.istanbul.edu.tr/file/JTA6CLJ8T5/98C1855791714246A5D21D3D5CE71D87 |
| work_keys_str_mv | AT mustafagunerkan examiningtheefficiencyoflearningbasedalgorithmsintheprocessofdeclaringcustoms AT endersahinaslan examiningtheefficiencyoflearningbasedalgorithmsintheprocessofdeclaringcustoms AT ondersahınaslan examiningtheefficiencyoflearningbasedalgorithmsintheprocessofdeclaringcustoms |