What type of study is this?

This is a Quantitative Study study.

Prediction of money laundering with machine learning methods using chaotic features

View Full Paper

Ask AI

Bookmark

Authors

Study classifies Bitcoin transactions as licit or illicit using Lyapunov exponents, enhancing detection methods.

The model achieved 90.2% accuracy in classifying Bitcoin transactions, highlighting its effectiveness.
Utilizing feature vectors based on Lyapunov exponents improved precision to 86.6% in the classification task.
Analysis was conducted using the Elliptic dataset, transforming nodes into time series for more accurate predictions.
Enhanced performance through polynomial and interaction features showcases the importance of chaotic features in detection.

CENGİZ et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark