Varying combination of feature extraction and modified support vector machines based prediction of myocardial infarction

2022 ◽  
Author(s):  
A. Razia Sulthana ◽  
A. K. Jaithunbi
Keyword(s):  
Myocardial Infarction ◽  
Feature Extraction ◽  
Support Vector Machines ◽  
Support Vector ◽  
Vector Machines

MULTI-VIEW GENDER CLASSIFICATION USING MULTI-RESOLUTION LOCAL BINARY PATTERNS AND SUPPORT VECTOR MACHINES

2007 ◽  
Vol 17 (06) ◽  
pp. 479-487 ◽  
Author(s):  
HUI-CHENG LIAN ◽  
BAO-LIANG LU
Keyword(s):  
Feature Extraction ◽  
Support Vector Machines ◽  
Local Binary Pattern ◽  
Spatial Information ◽  
Local Binary Patterns ◽  
Support Vector ◽  
Gender Classification ◽  
Classification Rate ◽  
Vector Machines ◽  
Facial Images

In this paper, we present a novel method for multi-view gender classification considering both shape and texture information to represent facial images. The face area is divided into small regions from which local binary pattern (LBP) histograms are extracted and concatenated into a single vector efficiently representing a facial image. Following the idea of local binary pattern, we propose a new feature extraction approach called multi-resolution LBP, which can retain both fine and coarse local micro-patterns and spatial information of facial images. The classification tasks in this work are performed by support vector machines (SVMs). The experiments clearly show the superiority of the proposed method over both support gray faces and support Gabor faces on the CAS-PEAL face database. A higher correct classification rate of 96.56% and a higher cross validation average accuracy of 95.78% have been obtained. In addition, the simplicity of the proposed method leads to very fast feature extraction, and the regional histograms and fine-to-coarse description of facial images allow for multi-view gender classification.

scholarly journals Automatic Motorcycle Detection on Public Roads

2013 ◽  
Vol 16 (3) ◽  
Author(s):  
Romuere Silva ◽  
Kelson Aires ◽  
Rodrigo Veras ◽  
Thiago Santos ◽  
Kalyf Lima ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Support Vector Machines ◽  
Image Classification ◽  
Multilayer Perceptron ◽  
Support Vector ◽  
Economic Factors ◽  
The Past ◽  
Vector Machines ◽  
Traffic Violations ◽  
Motorcycle Accidents

Motorcycle accidents have been rapidly growing throughout the years in many countries. Due to various social and economic factors, this type of vehicle is becoming increasingly popular. Over the past years, automated mechanisms to inspect traffic violations such as radars and surveillance cameras are being used ever more. This paper’s goals are the study and implementation of some methods for automatic detection of motorcycles on public roads. Traffic images captured by cameras were used. For feature extraction of images, the algorithms SURF, HAAR, HOG and LBP were used as descriptors. For image classification, Multilayer Perceptron, Support Vector Machines and Radial-Bases Function Networks were used as classifiers. Finally, the results are presented and discussed.

scholarly journals Feature extraction from terahertz pulses for classification of RNA data via support vector machines

2006 ◽  
Author(s):  
Xiaoxia Yin ◽  
Brian W.-H. Ng ◽  
Bernd Fischer ◽  
Bradley Ferguson ◽  
Samuel P. Mickan ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Support Vector Machines ◽  
Support Vector ◽  
Terahertz Pulses ◽  
Vector Machines

Feature Extraction for Support Vector Machines Face Recognition

2005 ◽  
Vol 64 (11) ◽  
pp. 917-921
Author(s):  
Jose Ruiz-Pinales ◽  
Juan Jorge Acosta-Reyes ◽  
Rene Jaime-Rivas, M. Sc.
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Support Vector Machines ◽  
Support Vector ◽  
Vector Machines

Intelligent Fault Diagnosis in Power Plant Using Empirical Mode Decomposition, Fuzzy Feature Extraction and Support Vector Machines

2005 ◽  
Vol 293-294 ◽  
pp. 373-382 ◽  
Author(s):  
Qiao Hu ◽  
Zheng Jia He ◽  
Yanyang Zi ◽  
Zhou Suo Zhang ◽  
Yaguo Lei
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Support Vector Machines ◽  
Empirical Mode Decomposition ◽  
Support Vector ◽  
Intelligent Fault Diagnosis ◽  
Turbo Generator ◽  
Mode Decomposition ◽  
Vector Machines ◽  
Multi Classification

In this paper, a novel intelligent fault diagnosis method based on empirical mode decomposition (EMD), fuzzy feature extraction and support vector machines (SVM) is proposed. The method consists of two stages. In the first stage, intrinsic mode components are obtained with EMD from original signals and converted into fuzzy feature vectors, and then the mechanical fault can be detected. In the second stage, these extracted fuzzy feature vectors are input into the multi-classification SVM to identify the different abnormal cases. The proposed method is applied to the classification of a turbo-generator set under three different operating conditions. Testing results show that the classification accuracy of the proposed model is greatly improved compared with the multi-classification SVM without feature extraction and the multi-classification SVM with extracting the fuzzy feature from wavelet packets, and the faults of steam turbo-generator set can be correctly and rapidly diagnosed using this model.

Sign in / Sign up

Export Citation Format

Share Document