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 ...

This paper proposes two-stage hybrid feature selection algorithms to build the stable and efficient diagnostic models where a new accuracy measure is introduced to assess the models. The two-stage hybrid algorithms adopt Support Vector Machines (SVM) as a classification tool, and the extended Sequential Forward Search (SFS), Sequential Forward Floating Search (SFFS), and Sequential Backward Flo...

The identification of image acquisition sources is an important problem in digital image forensics. This paper introduces a new feature-based method for digital camera identification. The method, which is based on an analysis of the imaging pipeline and digital camera processing operations, employs bi-coherence and wavelet coefficient features extracted from digital images. The sequential forwa...

Japanese Encephalitis is the most important cause of epidemic encephalitis worldwide. From the reports by various sources, about 68,000 cases of Japanese encephalitis (JE) are estimated to occur each year [1]. A vaccine is available for Japanese encephalitis, which utilizes effectively killed inoculated bacteria, but it is expensive and requires a primary vaccination followed by two successive ...

The rapid rise in hacking and computer network assaults throughout the world has highlighted need for more effective intrusion detection prevention solutions. system (IDS) is critical identifying abnormalities on network, which have grown size scope. IDS prevents intruders from gaining access to information field of security as a result. use detecting various types attacks. Because traffic data...

In this paper a computer-aided diagnostic system for the classification of hepatic lesions from Computed Tomography (CT) images is presented. Regions of Interest (ROI’s) taken from non-enhanced CT images of normal liver, hepatic cysts, hemangiomas, and hepatocellular carcinomas (a total of 147 samples), have been used as input to the system. The system consists of two levels: the feature extrac...

This paper focuses on enhancing the effectiveness of filter feature selection models from two aspects. First, feature-searching engine is modified based on optimization theory. Second, a point injection strategy is designed to improve the regularization capability of feature selection. The second topic is important, because overfitting is usually experienced. To evaluate the proposed strategies...

Handwriting is individualistic. The uniqueness of shape and style of handwriting can be used to identify the significant features in authenticating the author of writing. Acquiring these significant features leads to an important research in Writer Identification domain where to find the unique features of individual which also known as Individuality of Handwriting. This paper proposes an impro...

A large number of algorithms have been proposed for feature subset selection. Our experimental results show that the sequential forward oating selection (SFFS) algorithm, proposed by Pudil et al., dominates the other algorithms tested. We study the problem of choosing an optimal feature set for land use classi cation based on SAR satellite images using four di erent texture models. Pooling feat...