This project implements an Enhanced Gaussian Mixture Model (GMM) for clustering and trend analysis of financial data. The project uses historical S&P 500 data to demonstrate the model's capabilities.

This project demonstrates the implementation of a Gaussian Mixture Model (GMM) from scratch in Python and its application to the task of segmenting a brain MRI scan into three primary tissue types: ...

Objectives: This study aims to investigate the efficacy of unsupervised machine learning algorithms, specifically the Gaussian Mixture Model (GMM), K-means clustering, and Otsu automatic threshold ...

Abstract: Ensemble models for network anomaly detection are often challenged by class imbalance, a condition known to substantially degrade predictive accuracy. To address this limitation, a novel ...

Abstract: After large-scale electric vehicles are connected to the power distribution network, the disorderly charging behavior of users with significant uncertainty seriously affects the power ...