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Full-Text Articles in Physical Sciences and Mathematics
Performance Comparison Of Support Vector Machine, Random Forest, And Extreme Learning Machine For Intrusion Detection, Iftikhar Ahmad, Muhammad Javed Iqbal, Mohammad Basheri, Aneel Rahim
Performance Comparison Of Support Vector Machine, Random Forest, And Extreme Learning Machine For Intrusion Detection, Iftikhar Ahmad, Muhammad Javed Iqbal, Mohammad Basheri, Aneel Rahim
Articles
Intrusion detection is a fundamental part of security tools, such as adaptive security appliances, intrusion detection systems, intrusion prevention systems, and firewalls. Various intrusion detection techniques are used, but their performance is an issue. Intrusion detection performance depends on accuracy, which needs to improve to decrease false alarms and to increase the detection rate. To resolve concerns on performance, multilayer perceptron, support vector machine (SVM), and other techniques have been used in recent work. Such techniques indicate limitations and are not efficient for use in large data sets, such as system and network data. The intrusion detection system is used …
Extended Correlated Principal Component Analysis With Svm-Puk In Opinion Mining, Kollimarla Anusha Devi, Deepak Chowdary Edara, Venkatrama Phani Kumar Sistla, Venkata Krishna Kishore Kolli
Extended Correlated Principal Component Analysis With Svm-Puk In Opinion Mining, Kollimarla Anusha Devi, Deepak Chowdary Edara, Venkatrama Phani Kumar Sistla, Venkata Krishna Kishore Kolli
Turkish Journal of Electrical Engineering and Computer Sciences
With the rapid growth of microblogs and online sites, an inordinate number of product reviews are available on the Internet. They not only help in analyzing, but also assist in making informed decisions about product quality. In the proposed work, an extended correlated principal component analysis (ECPCA) is used for dimensionality reduction. A comparative analysis is conducted on movie reviews (DB-1) and Twitter datasets (DB-2 and DB-3) in opinion mining extraction. The performance of naive Bayes, CHIRP, and support vector machine (SVM) with kernel methods such as radial basis function (RBF), polynomial, and Pearson (PUK) are compared and analyzed on …
Classification And Regression Analysis Using Support Vector Machine For Classifying And Locating Faults In A Distribution System, Sophi Shilpa Gururajapathy, Hazlie Mokhlis, Hazlee Azil Bin Illias
Classification And Regression Analysis Using Support Vector Machine For Classifying And Locating Faults In A Distribution System, Sophi Shilpa Gururajapathy, Hazlie Mokhlis, Hazlee Azil Bin Illias
Turkish Journal of Electrical Engineering and Computer Sciences
Various fault location methods have been developed in the past to identify the faulty phase, fault type, faulty section, and distance. However, this identification is commonly conducted in a separate manner. An effective fault location should be able to identify all of these at the same time. Therefore, in this work, a method using a support vector machine (SVM) to identify the fault type, faulty section, and distance considering the faulty phase is proposed. The proposed method uses voltage sag magnitude of the distribution system as the main feature for the SVM to identify faults. The fault type is classified …
Compact Local Gabor Directional Number Pattern For Facial Expression Recognition, Zhengyan Zhang, Guanming Lu, Jingjie Yan, Haibo Li, Ning Sun, Xia Li
Compact Local Gabor Directional Number Pattern For Facial Expression Recognition, Zhengyan Zhang, Guanming Lu, Jingjie Yan, Haibo Li, Ning Sun, Xia Li
Turkish Journal of Electrical Engineering and Computer Sciences
This paper explores a novel method to represent face images for facial expression recognition; it is named compact local Gabor directional number pattern (CLGDNP). By convolving the face images with Gabor filters, we encode the magnitude and phase response images in each scale, and calculate the histograms in several nonoverlapping regions of each encoded image. Finally, we obtain two spatial histogram sequences by the aid of the mean pooling technology and concatenate them to form the facial descriptor. Moreover, for evaluating the performance of the proposed method, we employ a support vector machine to conduct some extensive classification experiments on …
Classification Of Surface Electromyogram Signals Based On Directed Acyclic Graphs And Support Vector Machines, Xinhui Hu, Jiangming Kan, Wenbin Li
Classification Of Surface Electromyogram Signals Based On Directed Acyclic Graphs And Support Vector Machines, Xinhui Hu, Jiangming Kan, Wenbin Li
Turkish Journal of Electrical Engineering and Computer Sciences
This paper presents a novel classification approach for surface electromyogram (sEMG) signals. The proposed classification approach involves two steps: (1) feature extraction from an sEMG, in which a 7-dimensional feature vector is extracted from 27 types of features of the sEMG by linear discriminant analysis (LDA), and (2) a novel classifier, DAGSVMerr, based on a directed acyclic graph (DAG) and support vector machine (SVM), in which a separability measure function based on erroneous recognition rates (ERRs) is defined to determine the initial operation list. The proposed approach takes advantage of the feedback idea to improve the performance of the classification. …