Machine learning-based authentication of banknotes: a comprehensive analysis
This research investigates the utilization of machine learning techniques for the identification and classification of counterfeit currency. The study utilizes a dataset consisting of authentic and counterfeit banknotes, employing various classification algorithms to construct a robust model for aut...
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| Format: | Article |
| Language: | English |
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REA Press
2024-03-01
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| Series: | Big Data and Computing Visions |
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| Online Access: | https://www.bidacv.com/article_197120_03eff6036b03cdd0d0b1fa6d97326e74.pdf |
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| _version_ | 1832579295244976128 |
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| author | Nadia Ghasem Abadi |
| author_facet | Nadia Ghasem Abadi |
| author_sort | Nadia Ghasem Abadi |
| collection | DOAJ |
| description | This research investigates the utilization of machine learning techniques for the identification and classification of counterfeit currency. The study utilizes a dataset consisting of authentic and counterfeit banknotes, employing various classification algorithms to construct a robust model for automated detection. Key features, including texture, color distribution, and security attributes, are extracted to train the model, enabling a thorough analysis of banknote authenticity. The proposed system exhibits promising accuracy in distinguishing genuine currency from counterfeits, thereby enhancing security measures in financial transactions and mitigating economic fraud. |
| format | Article |
| id | doaj-art-685b64bf12654b189dc9d954453d4b9f |
| institution | Kabale University |
| issn | 2783-4956 2821-014X |
| language | English |
| publishDate | 2024-03-01 |
| publisher | REA Press |
| record_format | Article |
| series | Big Data and Computing Visions |
| spelling | doaj-art-685b64bf12654b189dc9d954453d4b9f2025-01-30T12:23:16ZengREA PressBig Data and Computing Visions2783-49562821-014X2024-03-0141223010.22105/bdcv.2024.197120197120Machine learning-based authentication of banknotes: a comprehensive analysisNadia Ghasem Abadi0Department of Computer Engineering, Ayandegan Institute of Higher Education, Tonekabon, Iran.This research investigates the utilization of machine learning techniques for the identification and classification of counterfeit currency. The study utilizes a dataset consisting of authentic and counterfeit banknotes, employing various classification algorithms to construct a robust model for automated detection. Key features, including texture, color distribution, and security attributes, are extracted to train the model, enabling a thorough analysis of banknote authenticity. The proposed system exhibits promising accuracy in distinguishing genuine currency from counterfeits, thereby enhancing security measures in financial transactions and mitigating economic fraud.https://www.bidacv.com/article_197120_03eff6036b03cdd0d0b1fa6d97326e74.pdffake currencycounterfeit detectionmachine learningbanknote authenticityeconomic fraud prevention |
| spellingShingle | Nadia Ghasem Abadi Machine learning-based authentication of banknotes: a comprehensive analysis Big Data and Computing Visions fake currency counterfeit detection machine learning banknote authenticity economic fraud prevention |
| title | Machine learning-based authentication of banknotes: a comprehensive analysis |
| title_full | Machine learning-based authentication of banknotes: a comprehensive analysis |
| title_fullStr | Machine learning-based authentication of banknotes: a comprehensive analysis |
| title_full_unstemmed | Machine learning-based authentication of banknotes: a comprehensive analysis |
| title_short | Machine learning-based authentication of banknotes: a comprehensive analysis |
| title_sort | machine learning based authentication of banknotes a comprehensive analysis |
| topic | fake currency counterfeit detection machine learning banknote authenticity economic fraud prevention |
| url | https://www.bidacv.com/article_197120_03eff6036b03cdd0d0b1fa6d97326e74.pdf |
| work_keys_str_mv | AT nadiaghasemabadi machinelearningbasedauthenticationofbanknotesacomprehensiveanalysis |