Frontiers

A robust deep learning framework for predicting carbon dioxide-water alternating gas injection performance and optimization

Figure 1. Schematic diagram of CO 2-WAG technique and CCUS aims. Table 3. CO 2-WAG parameters range. The script then automatically runs numerical simulations number-by-number using the created data ...
GitHub

rskworld/fraud-detection-ml

Advanced fraud detection system using machine learning to identify fraudulent transactions and activities. This project implements multiple machine learning algorithms including Random Forest, XGBoost ...
GitHub

Predicting Amsterdam house / real estate prices using Ordinary Least Squares-, XGBoost-, KNN-, Lasso-, Ridge-, Polynomial-, Random Forest-, and Neural Network MLP Regression ...

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Scientific Research Publishing

Breiman, L. (2001) Random Forests. Machine Learning, 45, 5-32.

ABSTRACT: Atrial fibrillation (AF) is a leading cardiac arrhythmia associated with elevated mortality risk, particularly in low-resource settings where early risk stratification remains challenging.
IEEE

Classification of Heart Disease: Comparative Analysis using KNN, Random Forest, Gaussian Naive Bayes, XGBoost, SVM, Decision Tree, and Logistic Regression

Abstract: The heart is the center of the human body, and when it is not functioning well, the body cannot operate effectively. This research aims to compare some of the classifier models to find the ...
IEEE

Classification of Non-Small Cell Lung Cancer with the Histopathology Images based on Random Forest, Decision Tree and XGBoost Algorithm

Abstract: Among all types of cancer, Lung cancer has more mortality rate. Hence early detection of it may help in in increasing the life span of the patient. Histopathological images with computerised ...
EurekAlert!

Ensemble AI unlocks hidden tree crown structures in dense forests

The left side of the flowchart illustrates the process of constructing spatial structure units, while the right side shows the process of building the coupling model using ensemble learning. The ...
Scientific Research Publishing

Breiman, L. (2001) Random Forests. Machine Learning, 45, 5-32.

ABSTRACT: Biogas is gaining prominence as a renewable energy source with significant potential to reduce greenhouse gas emissions and mitigate environmental impacts associated with fossil fuels. This ...
Frontiers

Interpretability Versus Accuracy: A Comparison of Machine Learning Models Built Using Different Algorithms, Performance Measures, and Features to Predict E. coli Levels in ...

Existing datasets collected in 2018 (Weller et al., 2020b) and 2017 (Weller et al., 2020c) were used as the training and testing data, respectively, in the analyses reported here. Although the present ...