IEEE

Predictive Energy Management Using Random Forests for Fuel Cell-Powered UAVs

Abstract: Unmanned Aerial Vehicles (UAVs) have gained significant growth and demand across various applications in recent years. Battery-powered UAVs, however, face challenges due to limited endurance ...
IEEE

Uncertainty Estimation of Lake Ice Cover Maps From a Random Forest Classifier Using MODIS TOA Reflectance Data

Abstract: This article presents a method to improve the usability of lake ice cover (LIC) maps generated from moderate resolution imaging spectroradiometer (MODIS) top-of-atmosphere reflectance data ...
IEEE

Optimizing Land Use and Land Cover Classification Using Random Forests and Spectral Features

Abstract: Land use and land cover (LULC) analysis is a key application in remote sensing, supported by advanced methodologies. This study introduces a machine learning-based approach using normalized ...
Scientific Research Publishing

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

ABSTRACT: Accurate land cover classification is essential for environmental monitoring, urban planning, and resource management. Conventional classifiers trained on raw spectral bands are often ...
IEEE

EEG-Based Stress Detection Using Random Forest Algorithm

Abstract: This paper aims to investigate the efficacy of EEG-based stress detection using a Random Forest classifier during the Stroop Test, a key psychological assessment probing cognitive functions ...
IEEE

Machine Learning-Based Flood Risk Prediction: An Analysis of the Effectiveness of Random Forest Models

Abstract: Floods are becoming one of the most catastrophic natural catastrophes in the globe due to the frequency of severe weather occurrences. This work suggests a flood risk prediction approach ...
IEEE

Obesity Health Risk Prediction using Random Forest and SVM Algorithms

Abstract: Obesity-related health dangers have become a serious global issue that requires adequately pinpointing and quickly preventing health problems. One of the research studies in the article ...
IEEE

A Fusion Framework Combining DNN and Random Forest for 5G Millimeter-Wave Antenna Design and Optimization

Abstract: This article presents a fusion framework combining deep neural network (DNN) and random forest (RF) to enhance the design of a compact C-shaped patch antenna (CSPA) for 5G millimeter-wave ...
IEEE

Enhancing Prediction Accuracy Through Random Forest in Classification and Regression

Abstract: Random Forest is a well-known type of ensemble learning, which combines a number of decision trees to improve the prediction ability and reduce the risk of overfitting. This paper aims at ...
IEEE

Collating Random Forest Classifier and Artificial Neural Networks for the Risk Detection of Maternal Health

Abstract: This paper compares the performance of ANN models to Random Forests on benchmarking a test dataset against the risk levels of maternal health as low, mid, or high. The features considered ...
IEEE

Enhancing User Safety Through URL-Based Phishing Detection with Random Forest And Decision Tree

Abstract: One of the most common and severe types of cyber security attacks is phishing, which attempts to get private information of individuals and organizations alike. Leveraging the power of ...