Detection of counterfeit banknotes using genetic fuzzy system
Due to developments in printing technology, the number of counterfeit banknotes is increasing every year. Finding an effective method to detect counterfeit banknotes is an important task in business. Finding a reliable method to detect counterfeit banknotes is a crucial challenge in the world of eco...
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| Format: | Article |
| Language: | English |
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Ayandegan Institute of Higher Education,
2022-10-01
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| Series: | Journal of Fuzzy Extension and Applications |
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| Online Access: | https://www.journal-fea.com/article_154622_1194de5a86b581c9416be7aaf0bcab25.pdf |
| _version_ | 1797787094367600640 |
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| author | Mahmut Dirik |
| author_facet | Mahmut Dirik |
| author_sort | Mahmut Dirik |
| collection | DOAJ |
| description | Due to developments in printing technology, the number of counterfeit banknotes is increasing every year. Finding an effective method to detect counterfeit banknotes is an important task in business. Finding a reliable method to detect counterfeit banknotes is a crucial challenge in the world of economic transactions. Due to technological development, counterfeit banknotes may pass through the counterfeit banknote detection system based on physical and chemical properties undetected. In this study, an intelligent counterfeit banknote detection system based on a Genetic Fuzzy System (GFS) is proposed to detect counterfeit banknotes efficiently. GFS is a hybrid system that uses a network architecture to fine-tune the membership functions of a fuzzy inference system. The learning algorithms Fuzzy Classification, Genetic Fuzzy Classification, ANFIS Classification, and Genetic ANFIS Classification were applied to the dataset in the UCI machine learning repository to detect the authenticity of banknotes. The developed model was evaluated based on Accuracy (ACC), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Error Mean, Error STD, and confusion matrix. The experimental results and statistical analysis showed that the classification performance of the proposed model was evaluated as follows: Fuzzy = 97.64%, GA_Fuzzy = 98.60%, ANFIS = 80.83%, GA_ANFIS = 97.72% accuracy (ACC). This shows the significant potential of the proposed GFS models for fraud detection. |
| first_indexed | 2024-03-13T01:18:07Z |
| format | Article |
| id | doaj.art-0cc781e1a47244d1ba9e838ec241f4af |
| institution | Directory Open Access Journal |
| issn | 2783-1442 2717-3453 |
| language | English |
| last_indexed | 2024-03-13T01:18:07Z |
| publishDate | 2022-10-01 |
| publisher | Ayandegan Institute of Higher Education, |
| record_format | Article |
| series | Journal of Fuzzy Extension and Applications |
| spelling | doaj.art-0cc781e1a47244d1ba9e838ec241f4af2023-07-05T06:42:19ZengAyandegan Institute of Higher Education,Journal of Fuzzy Extension and Applications2783-14422717-34532022-10-013430231210.22105/jfea.2022.345344.1223154622Detection of counterfeit banknotes using genetic fuzzy systemMahmut Dirik0Department of Computer Engineering, Sirnak University, Turkey.Due to developments in printing technology, the number of counterfeit banknotes is increasing every year. Finding an effective method to detect counterfeit banknotes is an important task in business. Finding a reliable method to detect counterfeit banknotes is a crucial challenge in the world of economic transactions. Due to technological development, counterfeit banknotes may pass through the counterfeit banknote detection system based on physical and chemical properties undetected. In this study, an intelligent counterfeit banknote detection system based on a Genetic Fuzzy System (GFS) is proposed to detect counterfeit banknotes efficiently. GFS is a hybrid system that uses a network architecture to fine-tune the membership functions of a fuzzy inference system. The learning algorithms Fuzzy Classification, Genetic Fuzzy Classification, ANFIS Classification, and Genetic ANFIS Classification were applied to the dataset in the UCI machine learning repository to detect the authenticity of banknotes. The developed model was evaluated based on Accuracy (ACC), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Error Mean, Error STD, and confusion matrix. The experimental results and statistical analysis showed that the classification performance of the proposed model was evaluated as follows: Fuzzy = 97.64%, GA_Fuzzy = 98.60%, ANFIS = 80.83%, GA_ANFIS = 97.72% accuracy (ACC). This shows the significant potential of the proposed GFS models for fraud detection.https://www.journal-fea.com/article_154622_1194de5a86b581c9416be7aaf0bcab25.pdfanfiscounterfeit banknotesfuzzy inference systemgenetic fuzzy systemgenetic algorithm |
| spellingShingle | Mahmut Dirik Detection of counterfeit banknotes using genetic fuzzy system Journal of Fuzzy Extension and Applications anfis counterfeit banknotes fuzzy inference system genetic fuzzy system genetic algorithm |
| title | Detection of counterfeit banknotes using genetic fuzzy system |
| title_full | Detection of counterfeit banknotes using genetic fuzzy system |
| title_fullStr | Detection of counterfeit banknotes using genetic fuzzy system |
| title_full_unstemmed | Detection of counterfeit banknotes using genetic fuzzy system |
| title_short | Detection of counterfeit banknotes using genetic fuzzy system |
| title_sort | detection of counterfeit banknotes using genetic fuzzy system |
| topic | anfis counterfeit banknotes fuzzy inference system genetic fuzzy system genetic algorithm |
| url | https://www.journal-fea.com/article_154622_1194de5a86b581c9416be7aaf0bcab25.pdf |
| work_keys_str_mv | AT mahmutdirik detectionofcounterfeitbanknotesusinggeneticfuzzysystem |