In this article, we propose a novel algorithm for the recognition of complex activities in multimedia streams. The algorithm consists of a discriminative feature classifier based on random forests and a generative classifier, for which we use the hierarchical hidden Markov model. The discriminative feature classifier checks the existence or absence of the steps required for the execution of an ...

This study aims to improve classification accuracy by transforming continuous attributes into categories randomly generating percentile values as categorization limits. Four algorithms were compared for the generation of and selected based on small variability distribution highest revenue expectations. The testing training data becomes second consideration. Random forest (RF) is modeled from pe...

This study examines the clinical decision support systems in healthcare, in particular about the prevention, diagnosis and treatment of respiratory diseases, such as Asthma and chronic obstructive pulmonary disease. The empirical pulmonology study of a representative sample (n = 132) attempts to identify the major factors that contribute to the diagnosis of these diseases. Machine learning resu...

Many methods have been proposed for combining multiple classifiers in pattern recognition such as Random Forest which uses decision trees for problem solving. In this paper, we propose a weighted vote-based classifier ensemble method. The proposed method is similar to Random Forest method in employing many decision trees and neural networks as classifiers. For evaluating the proposed weighting ...

Electrocardiogram (ECG) signals sensed from mobile devices pertain the potential for biometric identity recognition applicable in remote access control systems where enhanced data security is demanding. In this study, we propose a new algorithm that consists of a two-stage classifier combining random forest and wavelet distance measure through a probabilistic threshold schema, to improve the ef...

Clinical data which includes data of patients and their symptoms is growing largely these days. Detection of a disease in some cases is expensive in terms of money and amount of effort spent. Predictive modeling aids in the early detection of a disease by using health records (HRs). By applying such techniques on an available clinical dataset, a prediction of the current state of a patient’s di...

We present a fully automatic method employing convolutional neural networks based on the 2D U-net architecture and random forest classifier to solve the automatic liver lesion segmentation problem of the ISBI 2017 Liver Tumor Segmentation Challenge (LiTS). In order to constrain the ROI in which the tumors could be located, a liver segmentation is performed first. For the organ segmentation an e...