Latent phenotypes extracted from these growth curves and their first derivatives informed the development of advanced machine learning models, specifically random forest and eXtreme ... Figure 2 ...

Flowchart of the validation method ... model’s ability to preserve spatial coherence in groundwater dynamics. In this study, a Random Forest (RF)-based algorithm was developed to downscale GRACE TWS ...

Understanding how cities grow is vital for shaping sustainable urban futures—but mapping the true extent of urban expansion ...

However, as the data are being generated explosively in this big data era, many machine learning algorithms, including the random forest algorithm, may face the difficulty in maintaining and ...

This repository contains implementations of the Random Cut Forest (RCF) probabilistic data structure. RCFs were originally developed at Amazon to use in a nonparametric anomaly detection algorithm for ...

and Random Forest Classifier to enhance classification accuracy and efficiency. The NSGRF approach, a key component of the system, significantly outperforms in terms of precision, F-score, recall, and ...

This project focuses on detecting insurance fraud using machine learning techniques, specifically Decision Tree and Random Forest classifiers. The goal is to build and optimize predictive models to ...