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.

中文翻译：

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使用由蒲公英算法优化的堆叠式自动编码器内核 ELM 检测欺诈交易

随着数字支付的兴起，欺诈活动的风险显着增加。为了解决这个问题，需要可靠的实时欺诈检测技术。这项研究引入了一种创新方法，称为堆叠式自动编码器内核极限学习机，通过蒲公英算法优化（S-AEKELM-DA）来检测欺诈交易。主要目标是通过将蒲公英技术集成到堆叠式自动编码器内核 ELM 架构中来增强内核极限学习机 (KELM) 的性能。本研究旨在通过优化正则化参数（c）和核参数（σ）来提高该方法在欺诈检测中的整体有效性。评估 S-AEKELM-DA 方法；使用四个信用卡数据集进行模拟和实验。结果显示出卓越的性能，我们的方法在检测欺诈交易方面实现了实时的高精度、召回率、精确度和 F1 分数。这些发现强调了所建议方法的有效性和可靠性。通过将蒲公英算法纳入S-AEKELM框架，该研究提高了欺诈检测能力，从而确保了数字交易的安全。