Classification of Arrhythmia using Time-domain Features and Support Vector Machine

2019 ◽  
Author(s):  
Mohit Singh Dhaka ◽  
Poras Khetarpal
Keyword(s):  
Support Vector Machine ◽  
Time Domain ◽  
Support Vector

Optimization of support vector machine based multi-fault classification with evolutionary algorithms from time domain vibration data of gears

2013 ◽  
Vol 227 (11) ◽  
pp. 2428-2439 ◽  
Author(s):  
DJ Bordoloi ◽  
Rajiv Tiwari
Keyword(s):  
Support Vector Machine ◽  
Time Domain ◽  
Angular Speed ◽  
Fault Classification ◽  
Support Vector ◽  
Vibration Data ◽  
Bee Colony ◽  
Time Domain Data ◽  
Grid Search Method

In the present work, a multi-fault classification of gears has been attempted by the support vector machine learning technique using the vibration data in time domain. A proper utilization of the support vector machine is based on the selection of support vector machine parameters. The main focus of this article is to examine the performance of the multiclass ability of support vector machine techniques by optimizing its parameters using the grid-search method, genetic algorithm and artificial bee colony algorithm. Four fault conditions were considered. A group of statistical features were extracted from time domain data. The prediction of fault classification is attempted at the same angular speed as the measured data as well as innovatively at the intermediate and extrapolated angular speed conditions. This is due to the fact that it is not feasible to have measurement of vibration data at all continuous speeds of interest. The classification ability is noted and it shows an excellent prediction performance.

scholarly journals sEMG based Classification of Hand Gestures using EMD Algorithm and its Application on Control System

2019 ◽  
Vol 8 (11S) ◽  
pp. 1103-1105
Keyword(s):  
Support Vector Machine ◽  
Control System ◽  
Empirical Mode Decomposition ◽  
Time Domain ◽  
Support Vector ◽  
Hand Gestures ◽  
Mode Decomposition ◽  
Single Sensor ◽  
Mean Variance

Single sensor is employed for classifying four hand gestures from flexor carpum ulnaris. The first three IMFs that are obtained as a result of Empirical Mode Decomposition are taken into consideration. Time domain features like mean, variance, skewness, etc are taken for each IMFs. Support Vector Machine was used for classification task and the extracted model is used for making predictions

The Classification of Skateboarding Tricks by Means of Support Vector Machine: An Evaluation of Significant Time-Domain Features

2020 ◽  
pp. 125-132
Author(s):  
Muhammad Amirul Abdullah ◽  
Muhammad Ar Rahim Ibrahim ◽  
Muhammad Nur Aiman Shapiee ◽  
Anwar P. P. Abdul Majeed ◽  
Mohd Azraai Mohd Razman ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Time Domain ◽  
Support Vector ◽  
Significant Time

Classification of Fatigued Bills Based on a Support Vector Machine by Using the Amplitude Difference Features of Acoustic Signals

2011 ◽  
Vol 131 (8) ◽  
pp. 1495-1501
Author(s):  
Dongshik Kang ◽  
Masaki Higa ◽  
Hayao Miyagi ◽  
Ikugo Mitsui ◽  
Masanobu Fujita ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Acoustic Signals ◽  
Support Vector ◽  
Amplitude Difference

Classification of hyperspectral images based on multifractal features and support vector machine

2018 ◽  
Vol 62 (5) ◽  
pp. 558-562
Author(s):  
Uchaev D.V. ◽  
◽  
Uchaev Dm.V. ◽  
Malinnikov V.A. ◽  
◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Hyperspectral Images ◽  
Support Vector

Color Region Classification of Flue-cured Tobacco Leaves Based on the Image Processing and Support Vector Machine

2013 ◽  
Vol 38 (2) ◽  
pp. 374-379 ◽  
Author(s):  
Zhi-Li PAN ◽  
Meng QI ◽  
Chun-Yang WEI ◽  
Feng LI ◽  
Shi-Xiang ZHANG ◽  
...  
Keyword(s):  
Image Processing ◽  
Support Vector Machine ◽  
Support Vector ◽  
Tobacco Leaves ◽  
Region Classification
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