Faulty compressors must be detected in advance to speed up the quality control process of compressor's performance. Machine learning models have recently been used as fault classification distinguish between normal and abnormal compressors, facilitating more sophisticated detection methods than those past. However, very few studies conducted on accurate efficient feature selection, despite its ...

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...

Feature extraction is a very important preprocessing step for classification of hyperspectral images. The linear discriminant analysis (LDA) method fails to work in small sample size situations. Moreover, LDA has poor efficiency for non-Gaussian data. LDA is optimized by a global criterion. Thus, it is not sufficiently flexible to cope with the multi-modal distributed data. We propose a new fea...

Feature selection plays an important role in improving the classification accuracy by handling redundant or irrelevant features present in the dataset. Various soft computing based hybrid approaches like neuro-fuzzy, genetic-fuzzy, rough set-neuro etc. are proposed by researchers to perform feature selection. The existing approaches gives higher complexity and computational cost with low classi...

In this paper, we developed a diagnosis model based on support vector machines (SVM) with a novel hybrid feature selection method to diagnose erythemato-squamous diseases. Our proposed hybrid feature selection method, named improved F -score and Sequential Forward Search (IFSFS), combines the advantages of filter and wrapper methods to select the optimal feature subset from the original feature...

Due to the importance of intrusion detection system, which is considered supportive enhancing network security. Therefore, we seek increase efficiency systems through use deep learning mechanisms. However, algorithms still suffer from problems in process classification and determining presence type attack, causes a decrease rate, an number false alarms, reduces system performance. This due larg...