common spatial pattern (csp) is a method commonly used to enhance the effects of event‑related desynchronization and event‑related synchronization present in multichannel electroencephalogram‑based brain‑computer interface (bci) systems. in the present study, a novel csp sub‑band feature selection has been proposed based on the discriminative information of the features. besides, a distinction ...

Blood donor selection is a cornerstone for blood transfusion safety, designed to safeguard the health of both donors and recipients. In the Service du Sang, Belgian Red Cross, French and German-speaking part of Belgium (SFS), health professionals (HPs) are allowed to interview donors on their own after formal qualification. This qualification is afterward evaluated by means of two complementary...

This paper describes an automatic feature selection algorithm integrated into a classification framework developed to discriminate between sleep and wake states during the night. The feature selection algorithm proposed in this paper uses the Mahalanobis distance and the Spearman’s ranked-order correlation as selection criteria to restrict search in a large feature space. The algorithm was test...

Power quality signal feature selection is an effective method to improve the accuracy and efficiency of power quality (PQ) disturbance classification. In this paper, an entropy-importance (EnI)-based random forest (RF) model for PQ feature selection and disturbance classification is proposed. Firstly, 35 kinds of signal features extracted from S-transform (ST) with random noise are used as the ...

1. Language Independent Recognition of Human Emotion using Artificial Neural Networks Waqas Bhatti, The University of Sydney, Australia Yongjin Wang, University of Toronto, Canada Ling Guan, Ryerson University,Canada This article presents a language-independent emotion recognition system for the identification of human affective state in the speech signal. A group of potential features are firs...

This article presents a language-independent emotion recognition system for the identification of human affective state in the speech signal. A group of potential features are first identified and extracted to represent the characteristics of different emotions. To reduce the dimensionality of the feature space, whilst increasing the discriminatory power of the features, we introduce a systemat...

Even though a great attention has been given on the cervical cancer diagnosis, it is a tuff task to observe the pap smear slide through microscope. Image Processing and Machine learning techniques helps the pathologist to take proper decision. In this paper, we presented the diagnosis method using cervical cell image which is obtained by Pap smear test. Image segmentation performed by multi-thr...

Cyber bullying detection that are prevailing commonly in social networks like Twitter is one of the focussed research area. Text mining and detecting cyber bullying has several research challenges and lot of research scope to work with. This research work makes use of supervised feature selection by ranking method in order to choose the features from the tweets. After that extreme learning mach...

The features are the main entries in text summarization. Treating all features equally causes poor summary generation. In this paper, we investigate the effect of the feature structure on the features selection using particle swarm optimization. The particle swarm optimization is trained using DUC 2002 data to learn the weight of each feature. The features used are different in terms of the str...

Visual attention is a very important task in autonomous robotics, but, because of its complexity, the processing time required is significant. We propose an architecture for feature selection using foveated images that is guided by visual attention tasks and that reduces the processing time required to perform these tasks. Our system can be applied in bottom–up or top–down visual attention. The...