The paper describes an application of the tree classification method Random Forest (RF), as used in the analysis of data from the ground-based gamma telescope MAGIC. In such telescopes, cosmic γ-rays are observed and have to be discrimi-

This study aims to examine the ability of free floating aquatic plants to remove phosphorus and to predict the reduction of phosphorus from rice mill wastewater using soft computing techniques. A mesocosm study was conducted at the mill premises under normal conditions, and reliable results were obtained. Four aquatic plants, namely water hyacinth, water lettuce, salvinia, and duckweed were use...

Mapping and monitoring forest extent is a common requirement of regional forest inventories and public land natural resource management, including in Australia. The state of Victoria, Australia, has approximately 7.2 million hectares of mostly forested public land, comprising ecosystems that present a diverse range of forest structures, composition and condition. In this paper, we evaluate the ...

Resource selection functions (RSFs) are tremendously valuable for ecologists and resource managers because they quantify spatial patterns in resource utilization by wildlife, thereby facilitating identification of critical habitat areas and characterizing specific habitat features that are selected or avoided. RSFs discriminate between known-use resource units (e.g., telemetry locations) and av...

Medium spatial resolution biomass is a crucial link from the plot to regional and global scales. Although remote-sensing data-based methods have become a primary approach in estimating forest above ground biomass (AGB), many difficulties remain in data resources and prediction approaches. Each kind of sensor type and prediction method has its own merits and limitations. To select the proper est...

OBJECTIVE It is essential to develop predictive algorithms for Amyotrophic Lateral Sclerosis (ALS) disease progression to allow for efficient clinical trials and patient care. The best existing predictive models rely on several months of baseline data and have only been validated in clinical trial research datasets. We asked whether a model developed using clinical research patient data could b...

MOTIVATION We developed an EM-random forest (EMRF) for Haseman-Elston quantitative trait linkage analysis that accounts for marker ambiguity and weighs each sib-pair according to the posterior identical by descent (IBD) distribution. The usual random forest (RF) variable importance (VI) index used to rank markers for variable selection is not optimal when applied to linkage data because of corr...

The objective of this paper is to compare different classifiers’ recognition accuracy for the 28 Arabic alphabet letters gestured by participants as Sign Language and captured by two depth sensors. The accuracy results of three individual classifiers: (1) the support vector machine (SVM), (2) random forest (RF), and (3) nearest neighbour (kNN), using the original gestured dataset were compared ...

Land-cover maps are important tools for monitoring large-scale environmental change and can be regularly updated using free satellite imagery data. A key challenge with constructing these is missing data in the images on which they based. To address this challenge, we created a Spatial Random Forest (S-RF) model that accurately interpolate based modest training set of observed image interest. W...