We investigated the use of multi-spectral Landsat OLI imagery for delineating mangrove, lowland evergreen, upland evergreen and mixed deciduous forest types in Myanmar’s Tanintharyi Region and estimated the extent of degraded forest for each unique forest type. We mapped a total of 16 natural and human land use classes using both a Random Forest algorithm and a multivariate Gaussian model while...

Reducing the impact of the siricid wasp, Sirex noctilio is crucial for the future productivity and sustainability of commercial pine resources in South Africa. In this study we present a machine learning model that serves as a spatial guide and allows forest managers to focus their existing detection and monitoring efforts on key areas and proactively adopt the most appropriate course of interv...

A quantitative assessment of forest cover change in the Moulouya River watershed (Morocco) was carried out by means of an innovative approach from atmospherically corrected reflectance Landsat images corresponding to 1984 (Landsat 5 Thematic Mapper) and 2013 (Landsat 8 Operational Land Imager). An object-based image analysis (OBIA) was undertaken to classify segmented objects as forested or non...

The functioning of a protein in biological reactions crucially depends on its threedimensional structure. Prediction of the three-dimensional structure of a protein (tertiary structure) from its amino acid sequence (primary structure) is considered as a challenging task for bioinformatics and molecular biology. Recently, due to tremendous advances in the pattern recognition field, there has bee...

Research efforts to develop malicious application detection algorithms have been a priority ever since the discovery of the first “viruses”. Fourier transform is used to extract features from binary files. These features are then reduced by random projection algorithm to create a set of low-dimensional features that are used to classify whether the application is malicious or not. A 99.6% accur...

Statistical classification is a critical component of utilizing metabolomics data for examining the molecular determinants of phenotypes. Despite this, a comprehensive and rigorous evaluation of the accuracy of classification techniques for phenotype discrimination given metabolomics data has not been conducted. We conducted such an evaluation using both simulated and real metabolomics datasets...

—We present the development and evaluation of a hand tracking algorithm based on single depth images captured from an overhead perspective for use in the COACH prompting system. We train a random decision forest body part classifier using approximately 5,000 manually labeled, unbalanced, partially labeled training images. The classifier represents a random subset of pixels in each depth image w...

This work investigates the use of Random Forests for class based pixel-wise segmentation of images. The contribution of this paper is three-fold. First, we show that apparently quite dissimilar classifiers (such as nearest neighbour matching to texton class histograms) can be mapped onto a Random Forest architecture. Second, based on this insight, we show that the performance of such classifier...

One class classification is a binary classification task for which only one class of samples is available for learning. In some preliminary works, we have proposed One Class Random Forests (OCRF), a method based on a random forest algorithm and an original outlier generation procedure that makes use of classifier ensemble randomization principles. In this paper, we propose an extensive study of...

This paper presents a new computational strategy for predicting Nuclear Export Signals (NESs) in proteins of the model plant Arabidopsis thaliana based on a random forest classifier. NESs are amino acid sequences that enable a protein to interact with a nuclear receptor and in this way to be exported from the nucleus to the