This study examined the value of automated and manual feature selection, when applied to machine learning and object-based image analysis (OBIA), for the differentiation of crops in a Mediterranean climate. Five Landsat8 images covering the phenological stages of seven major crops types in the study area (Cape Winelands, South Africa) were acquired and processed. A statistical image fusion tech...

Abstract In view of the low accuracy and poor processing capacity traditional power equipment image recognition methods, this paper proposes a method based on dual-channel convolutional neural network (DC-CNN) model random forest (RF) classification. aspect feature extraction, DC-CNN extracts characteristics through two independent CNN models. algorithm, by referring to advantages machine learn...

BACKGROUND AND OBJECTIVE A crucial step in a classification of electroencephalogram (EEG) records is the feature selection. The feature selection problem is difficult because of the complex structure of EEG signals. To classify the EEG signals with good accuracy, most of the recently published studies have used high-dimensional feature spaces. Our objective is to create a low-dimensional featur...

Many maximum power point tracking (MPPT) algorithms have been developed in recent years to maximize the produced PV energy. These algorithms are not sufficiently robust because of fast-changing environmental conditions, efficiency, accuracy at steady-state value, and dynamics of the tracking algorithm. Thus, this paper proposes a new random forest (RF) model to improve MPPT performance. The RF ...

The accurate methods for the forecasting of hydrological characteristics are significantly important water resource management and environmental aspects. In this study, a novel approach daily streamflow discharge data is proposed. Streamflow discharge, temperature, precipitation were used feature extraction which systematically employed studies. While correlation-based selection (CFS) was selec...

Accurately predicting the Remaining Useful Life (RUL) of lithium-ion batteries is key to battery health management system. However, problems unstable model output and extensive calculation limit prediction accuracy. This article proposes a Particle Swarm Optimization Random Forest (PSO-RF) method improve RUL First, capacity extracted from data set National Aeronautics Space Administration (NASA...

Currently, Chemoinformatic methods are used to perform the prediction for FBPase inhibitory activity. A genetic algorithm-random forest coupled method (GA-RF) was proposed to predict fructose 1,6-bisphosphatase (FBPase) inhibitors to treat type 2 diabetes mellitus using the Mold(2) molecular descriptors. A data set of 126 oxazole and thiazole analogs was used to derive the GA-RF model, yielding...

Forest disturbances caused by pest insects are threatening ecosystem stability, sustainable forest management and economic return in boreal forests. Climate change and increased extreme weather patterns can magnify the intensity of forest disturbances, particularly at higher latitudes. Due to rapid responses to elevating temperatures, forest insect pests can flexibly change their survival, disp...