Background: Multiple sclerosis (MS) is a degenerative inflammatory disease which is most commonly diagnosed by magnetic resonance imaging (MRI). But, since the MRI device uses of a magnetic field, if there are metal objects in the patient's body, it can disrupt the health of the patient, the functioning of the MRI, and distortion in the images. Due to limitations of using MRI device, screening ...

This investigation developed models to estimate aspects of physical activity and sedentary behavior from three-axis high-frequency wrist-worn accelerometer data. The models were developed and tested on 20 participants (n = 10 males, n = 10 females, mean age = 24.1, mean body mass index = 23.9), who wore an ActiGraph GT3X+ accelerometer on their dominant wrist and an ActiGraph GT3X on the hip wh...

A new automatic method for multiple sclerosis (MS) lesion segmentation in multi-channel 3D MR images is presented. The main novelty of the method is that it learns the spatial image features needed for training a supervised classifier entirely from unlabeled data. This is in contrast to other current supervised methods, which typically require the user to preselect or design the features to be ...

There has been an increasing interest in applying machine learning methods in urban energy assessment. This research implemented six statistical learning methods in estimating domestic gas and electricity using both physical and socio-economic explanatory variables in London. The input variables include dwelling types, household tenure, household composition, council tax band, population age gr...

Bagging and boosting are among the most popular resampling ensemble methods that generate and combine a diversity of classifiers using the same learning algorithm for the base-classifiers. Boosting algorithms are considered stronger than bagging on noisefree data. However, there are strong empirical indications that bagging is much more robust than boosting in noisy settings. For this reason, i...

Ensemble learning algorithms such as AdaBoost and Bagging have been in active research and shown improvements in classification results for several benchmarking data sets with mainly decision trees as their base classifiers. In this paper we experiment to apply these Meta learning techniques with classifiers such as random forests, neural networks and support vector machines. The data sets are ...

In this paper, we propose a novel data mining technique involving random forests and random trees for energy efficiency for forest cover type classification. Novel machine learning and data mining techniques provide an unprecedented opportunity to monitor and characterize physical environments, such as forest cover type, using low cost wireless sensor networks. However, given the sheer amount o...

The problem of combining predictors to increase accuracy (often called ensemble learning) has been studied broadly in the machine learning community for both classification and regression tasks. The design of an ensemble is based on the individual accuracy of the predictors and also how different they are from one another. There is a significant body of literature on how to design and measure d...