Detecting Fraudulent Transactions Using Stacked Autoencoder Kernel ELM Optimized by the Dandelion Algorithm
The risk of fraudulent activity has significantly increased with the rise in digital payments. To resolve this issue there is a need for reliable real-time fraud detection technologies. This research introduced an innovative method called stacked autoencoder kernel extreme learning machine optimized...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Journal of Theoretical and Applied Electronic Commerce Research |
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| Online Access: | https://www.mdpi.com/0718-1876/18/4/103 |
| _version_ | 1827574412421890048 |
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| author | Fatima Zohra El Hlouli Jamal Riffi Mhamed Sayyouri Mohamed Adnane Mahraz Ali Yahyaouy Khalid El Fazazy Hamid Tairi |
| author_facet | Fatima Zohra El Hlouli Jamal Riffi Mhamed Sayyouri Mohamed Adnane Mahraz Ali Yahyaouy Khalid El Fazazy Hamid Tairi |
| author_sort | Fatima Zohra El Hlouli |
| collection | DOAJ |
| description | The risk of fraudulent activity has significantly increased with the rise in digital payments. To resolve this issue there is a need for reliable real-time fraud detection technologies. This research introduced an innovative method called stacked autoencoder kernel extreme learning machine optimized by the dandelion algorithm (S-AEKELM-DA) to detect fraudulent transactions. The primary objective was to enhance the kernel extreme learning machine (KELM) performance by integrating the dandelion technique into a stacked autoencoder kernel ELM architecture. This study aimed to improve the overall effectiveness of the proposed method in fraud detection by optimizing the regularization parameter (c) and the kernel parameter (σ). To evaluate the S-AEKELM-DA approach; simulations and experiments were conducted using four credit card datasets. The results demonstrated remarkable performance, with our method achieving high accuracy, recall, precision, and F1-score in real time for detecting fraudulent transactions. These findings highlight the effectiveness and reliability of the suggested approach. By incorporating the dandelion algorithm into the S-AEKELM framework, this research advances fraud detection capabilities, thus ensuring the security of digital transactions. |
| first_indexed | 2024-03-08T20:36:44Z |
| format | Article |
| id | doaj.art-4dc4d43a188f498cbc2c68ed2af86398 |
| institution | Directory Open Access Journal |
| issn | 0718-1876 |
| language | English |
| last_indexed | 2024-03-08T20:36:44Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Theoretical and Applied Electronic Commerce Research |
| spelling | doaj.art-4dc4d43a188f498cbc2c68ed2af863982023-12-22T14:20:12ZengMDPI AGJournal of Theoretical and Applied Electronic Commerce Research0718-18762023-11-011842057207610.3390/jtaer18040103Detecting Fraudulent Transactions Using Stacked Autoencoder Kernel ELM Optimized by the Dandelion AlgorithmFatima Zohra El Hlouli0Jamal Riffi1Mhamed Sayyouri2Mohamed Adnane Mahraz3Ali Yahyaouy4Khalid El Fazazy5Hamid Tairi6LISAC Laboratory, Faculty of Sciences, University Sidi Mohamed Ben Abdellah, Fes Atlas 30003, MoroccoLISAC Laboratory, Faculty of Sciences, University Sidi Mohamed Ben Abdellah, Fes Atlas 30003, MoroccoLISA Laboratory, National School of Applied Sciences, University Sidi Mohamed Ben Abdellah, Fes Atlas 30003, MoroccoLISAC Laboratory, Faculty of Sciences, University Sidi Mohamed Ben Abdellah, Fes Atlas 30003, MoroccoLISAC Laboratory, Faculty of Sciences, University Sidi Mohamed Ben Abdellah, Fes Atlas 30003, MoroccoLISAC Laboratory, Faculty of Sciences, University Sidi Mohamed Ben Abdellah, Fes Atlas 30003, MoroccoLISAC Laboratory, Faculty of Sciences, University Sidi Mohamed Ben Abdellah, Fes Atlas 30003, MoroccoThe risk of fraudulent activity has significantly increased with the rise in digital payments. To resolve this issue there is a need for reliable real-time fraud detection technologies. This research introduced an innovative method called stacked autoencoder kernel extreme learning machine optimized by the dandelion algorithm (S-AEKELM-DA) to detect fraudulent transactions. The primary objective was to enhance the kernel extreme learning machine (KELM) performance by integrating the dandelion technique into a stacked autoencoder kernel ELM architecture. This study aimed to improve the overall effectiveness of the proposed method in fraud detection by optimizing the regularization parameter (c) and the kernel parameter (σ). To evaluate the S-AEKELM-DA approach; simulations and experiments were conducted using four credit card datasets. The results demonstrated remarkable performance, with our method achieving high accuracy, recall, precision, and F1-score in real time for detecting fraudulent transactions. These findings highlight the effectiveness and reliability of the suggested approach. By incorporating the dandelion algorithm into the S-AEKELM framework, this research advances fraud detection capabilities, thus ensuring the security of digital transactions.https://www.mdpi.com/0718-1876/18/4/103kernel extreme learning machinestacked autoencoderdandelion algorithmcredit card fraud |
| spellingShingle | Fatima Zohra El Hlouli Jamal Riffi Mhamed Sayyouri Mohamed Adnane Mahraz Ali Yahyaouy Khalid El Fazazy Hamid Tairi Detecting Fraudulent Transactions Using Stacked Autoencoder Kernel ELM Optimized by the Dandelion Algorithm Journal of Theoretical and Applied Electronic Commerce Research kernel extreme learning machine stacked autoencoder dandelion algorithm credit card fraud |
| title | Detecting Fraudulent Transactions Using Stacked Autoencoder Kernel ELM Optimized by the Dandelion Algorithm |
| title_full | Detecting Fraudulent Transactions Using Stacked Autoencoder Kernel ELM Optimized by the Dandelion Algorithm |
| title_fullStr | Detecting Fraudulent Transactions Using Stacked Autoencoder Kernel ELM Optimized by the Dandelion Algorithm |
| title_full_unstemmed | Detecting Fraudulent Transactions Using Stacked Autoencoder Kernel ELM Optimized by the Dandelion Algorithm |
| title_short | Detecting Fraudulent Transactions Using Stacked Autoencoder Kernel ELM Optimized by the Dandelion Algorithm |
| title_sort | detecting fraudulent transactions using stacked autoencoder kernel elm optimized by the dandelion algorithm |
| topic | kernel extreme learning machine stacked autoencoder dandelion algorithm credit card fraud |
| url | https://www.mdpi.com/0718-1876/18/4/103 |
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