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相关概念视频

Prosopagnosia01:24

Prosopagnosia

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Prosopagnosia, also known as face blindness, is the inability to recognize faces. In severe cases, individuals with prosopagnosia may not recognize close family members, including parents and spouses, by their faces. For instance, someone with prosopagnosia might walk past their child in a crowd, only realizing their mistake upon noticing their child's distinctive backpack or favorite jacket. Prosopagnosia specifically impairs facial recognition, while the recognition of other objects or...
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相关实验视频

Updated: May 16, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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图形卷积网络用于比特币交易中的欺诈检测.

Ahmad Asiri1, K Somasundaram2

  • 1Department of Mathematics, Applied College at Mahail Aseer, King Khalid University, Abha, Saudi Arabia.

Scientific reports
|April 1, 2025
PubMed
概括
此摘要是机器生成的。

检测非法加密货币交易对于反洗钱工作至关重要. 这项研究表明,图形卷积网络 (GCN) 模型在识别可疑的比特币交易方面明显优于其他机器学习算法.

关键词:
比特币 (BITCOIN) 是一个比特币.加密货币 加密货币.深度学习是一种深度学习.图形卷积网络中的图形卷积网络.机器学习 机器学习

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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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科学领域:

  • 计算机科学 计算机科学
  • 网络安全 网络安全
  • 金融犯罪学 金融犯罪学

背景情况:

  • 反洗钱 (AML) 受到加密货币交易的伪名性质的挑战.
  • 犯罪分子利用加密货币进行非法金融活动,需要先进的检测方法.
  • 机器学习和深度学习为识别区块链数据中的异常提供了有希望的方法.

研究的目的:

  • 评估各种机器学习算法的有效性,以检测非法加密货币交易.
  • 具体评估圆比特币数据集上的图形卷积网络 (GCN) 的性能.
  • 将GCN模型的有效性与现有的AML检测模型进行比较.

主要方法:

  • 利用圆比特币数据集,这是一个标记和未标记区块链交易的图形数据集.
  • 实现并比较了后勤回归,长短期记忆 (LSTM),支持向量机 (SVM),随机森林和图形卷积网络 (GCN) 模型.
  • 使用精度,接收器运行特征曲线 (AUC) 下的面积和根平均平方误差 (RMSE) 评估模型性能.

主要成果:

  • 拟议的GCN模型实现了[公式:参见文本]的准确性,AUC为0.9444,RMSE为0.1123.
  • 与其他评估的机器学习算法相比,GCN模型表现出卓越的性能.
  • 结果表明,GCN模型比以前提出的模型更有效,包括韦伯等人提出的模型. (2019年) 的时间.

结论:

  • 图形卷积网络 (GCN) 在检测非法加密货币交易方面非常有效.
  • 开发的GCN模型在反洗钱的背景下为金融取证提供了强大的解决方案.
  • 这项研究强调了基于图形的深度学习在保护区块链生态系统方面的潜力.