Fast brain control systems for electric wheelchair using support vector machine

2016 ◽  
Author(s):  
Ivan Halim Parmonangan ◽  
Jennifer Santoso ◽  
Widodo Budiharto ◽  
Alexander Agung Santoso Gunawan
Keyword(s):  
Support Vector Machine ◽  
Control Systems ◽  
Support Vector ◽  
Electric Wheelchair

BI-FEATURE VERIFICATION FOR PALMPRINT IMAGES CAPTURED IN PEGLESS SCENARIOS

2013 ◽  
Vol 27 (05) ◽  
pp. 1356007 ◽  
Author(s):  
CHIH-LUNG LIN ◽  
HSU-YUNG CHENG ◽  
KUO-CHIN FAN ◽  
CHUN-WEI LU ◽  
CHANG-JUNG JUAN ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Access Control ◽  
Control Systems ◽  
Low Cost ◽  
Experimental Results ◽  
Support Vector ◽  
Low Resolution ◽  
Classification Rate ◽  
Histogram Of Oriented Gradient ◽  
Feature Information

This paper presents a reliable and robust palmprint verification approach that involves using a bi-feature, biometric, palmprint feature-point number (FPN) and a histogram of oriented gradient (HOG). The bi-feature was fused and verified using a support vector machine (SVM) at the feature level. The approach has the advantages of capturing palm images in pegless scenarios with a low cost and low-resolution (100 dpi) digital scanner, and one sensor can capture palmprint bi-feature information. The low-resolution images result in a smaller database. Nine thousand palmprint images were collected from 300 people to verify the validity of the proposed approach. The results showed an accurate classification rate of 99.04%. The experimental results demonstrated that the proposed approach is feasible and effective in palmprint verification. Our findings will help extend palmprint verification technology to security access control systems.

Palmprint Verification for Images Captured in Peg-Less Scenarios

2013 ◽  
Vol 284-287 ◽  
pp. 3178-3183 ◽  
Author(s):  
Chun Wei Lu ◽  
Chih Lung Lin ◽  
Kuo Chin Fan ◽  
Hsu Yung Cheng ◽  
Chang Jung Juan
Keyword(s):  
Support Vector Machine ◽  
Access Control ◽  
Control Systems ◽  
Low Cost ◽  
Experimental Results ◽  
Feature Point ◽  
Support Vector ◽  
Low Resolution ◽  
Point Number ◽  
Database Size

This paper presents a reliable and robust palmprint verification approach using palmprint feature point number (FPN). The feature verified by support vector machine (SVM). It has the advantages of capturing palm images in peg-less scenarios and by a low cost and low-resolution (100dpi) digital scanner. The low-resolution images lead a less database size. There are 4800 palmprint images were collected from 160 persons to verify the validity of the proposed approach and the results are satisfactory with 98.30% classification correct rate (CCR). Experimental results demonstrate that the proposed approach is feasible and effective in palmprint verification. Our findings will help to extend palmprint verification technologies to security access control systems.

scholarly journals Prediction of student’s performance through educational data mining techniques

2021 ◽  
Vol 22 (3) ◽  
pp. 1708
Author(s):  
Nibras Z. Salih ◽  
Walaa Khalaf
Keyword(s):  
Support Vector Machine ◽  
Control Systems ◽  
Student Performance ◽  
Undergraduate Students ◽  
Confusion Matrix ◽  
Educational Data Mining ◽  
General Information ◽  
Support Vector ◽  
Academic Education ◽  
Academic Information

Many educators have worried about the failures of students through academic education. Thus, a variety of predictions have been applied to general information including culture, social, and economic information which wasn’t related to student performance. We have gathered an actual dataset from three years of academic stages of Mustansiriyah University in Iraq. The dataset consists of academic information without any socioeconomic data, it includes forty-four undergraduate students with thirteen attributes. We have proposed a model that explains the correlation between two main subjects which are, mathematics, and control systems. This study aimed to identify student failure of the control systems subject in the third year depending on the academic features of the mathematics subjects in the first and second years. Three algorithms were applied to the dataset including Naïve Bayes, support vector machine, and multilayer perceptron. Since the dataset was imbalanced, this leads to appear overfitting problem in the results so the synthetic minority oversampling technique was utilized to solve this problem. Our results show that the support vector machine algorithm proves an efficient classification after applied synthetic minority oversampling technique. The accuracy of the classifiers was measured from the confusion matrix using the Waikato environment for knowledge analysis (WEKA) tool and its related metrics.

Support vector machine? – not only for MRI-images

2020 ◽  
Author(s):  
V Vasilevska ◽  
K Schlaaf ◽  
H Dobrowolny ◽  
G Meyer-Lotz ◽  
HG Bernstein ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

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

An Accurate Packer Identification Method Using Support Vector Machine

2014 ◽  
Vol E97.A (1) ◽  
pp. 253-263 ◽  
Author(s):  
Ryoichi ISAWA ◽  
Tao BAN ◽  
Shanqing GUO ◽  
Daisuke INOUE ◽  
Koji NAKAO
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Identification Method
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