Accurate gender classification is useful in speech and speaker recognition as well as speech emotion classification, because a better performance has been reported when separate acoustic models are employed for males and females. Gender classification is also apparent in face recognition, video summarization, human-robot interaction, etc. Although gender classification is rather mature in appli...

An important problem in bioinformatics is the inference of gene regulatory networks (GRN) from temporal expression profiles. In general, the main limitations faced by GRN inference methods is the small number of samples with huge dimensionalities and the noisy nature of the expression measurements. In face of these limitations, alternatives are needed to get better accuracy on the GRNs inferenc...

Although self-paced Brain-Computer Interface (BCI) is desirable from the users’ point of view, it brings about great technological challenges. This thesis aims to provide novel solutions to several important aspects in developing a realistic self-paced Electroencephalogram (EEG) based BCI. A Sequential Floating Forward Search (SFFS) based method is developed for feature selection, followed by t...

Elevated mental workload (MWL) experienced by pilots can result in increased reaction times or incorrect actions, potentially compromising flight safety. This study aims to develop a functional system assist administrators identifying and detecting pilots’ real-time MWL evaluate its effectiveness using designed airfield traffic pattern tasks within realistic simulator. The perceived various sit...

in this study, a prediction model based on support vector machines (svm) improved by introducing a volume weighted penalty function to the model was introduced to increase the accuracy of forecasting short term trends on the stock market to develop the optimal trading strategy. along with vw-svm classifier, a hybrid feature selection method was used that consisted of f-score as the filter part ...

In this research, a new method for automatic detection and classification of suspected breast cancer lesions using ultrasound images is proposed. In this fully automated method, de-noising using fuzzy logic and correlation among ultrasound images taken from different angles is used. Feature selection using combination of sequential backward search, sequential forward search and distance-based m...

In the paper, we present an empirical evaluation of five feature selection methods: ReliefF, random forest feature selector, sequential forward selection, sequential backward selection, and Gini index. Among the evaluated methods, the random forest feature selector has not yet been widely compared to the other methods. In our evaluation, we test how the implemented feature selection can affect ...

In order to accurately measure the gene expression levels in microarray experiments, it is crucial to design unique, highly specific and highly sensitive oligonucleotide probes for the identification of biological agents such as genes in a sample. Unique probes are difficult to obtain for closely related genes such as the known strains of HIV genes. The non-unique probe selection problem is to ...

MOTIVATION High-throughput technologies for rapid measurement of vast numbers of biological variables offer the potential for highly discriminatory diagnosis and prognosis; however, high dimensionality together with small samples creates the need for feature selection, while at the same time making feature-selection algorithms less reliable. Feature selection must typically be carried out from ...