this study investigates the detection of the drowsiness state for a future application such as in the reduction ofthe road traffic accidents. the electroencephalography(eeg), electrooculography (eog), driving quality (dq), and karolinska sleepiness scale (kss) data of 7 male during approximately 20 hours of sleep deprivation were recorded. to reduce the eye blink artifact, an automatic mechanis...

In this paper, principles and existing feature selection methods for classifying and clustering data be introduced. To that end, categorizing frameworks for finding selected subsets, namely, search-based and non-search based procedures as well as evaluation criteria and data mining tasks are discussed. In the following, a platform is developed as an intermediate step toward developing an intell...

Background: In the current study, a hybrid feature selection approach involving filter and wrapper methods is applied to some bioscience databases with various records, attributes and classes; hence, this strategy enjoys the advantages of both methods such as fast execution, generality, and accuracy. The purpose is diagnosing of the disease status and estimating of the patient survival. Method...

Feature selection can significantly be decisive when analyzing high dimensional data, especially with a small number of samples. Feature extraction methods do not have decent performance in these conditions. With small sample sets and high dimensional data, exploring a large search space and learning from insufficient samples becomes extremely hard. As a result, neural networks and clustering a...

premature ventricular contraction (pvc) is one of the common cardiac arrhythmias. the occurrence of pvc is dangerous in people who have recently undergone heart. a pvc beat can easily be diagnosed by a doctor based on the shape of the electrocardiogram signal. but in automatic detection, extracting several important features from each beat is required. in this paper, a method for automatic dete...

Fault classification in distance protection of transmission lines, with considering the wide variation in the fault operating conditions, has been very challenging task. This paper presents a probabilistic neural network (PNN) and new feature selection technique for fault classification in transmission lines. Initially, wavelet transform is used for feature extraction from half cycle of post-fa...

In this modern era, due to demand for cloud environments in business, the size, complexity, and chance of attacks virtual network (VCN) are increased. The protection VCN is required maintain faith users. Intrusion detection essential secure any network. existing approaches that use conventional neural cannot utilize all information identifying intrusions. paper, anomaly-based NIDS proposed. For...