Least Square Support Vector Machine Analysis for the Classification of Paddy Seeds by Harvest Year

2008 ◽  
Vol 51 (5) ◽  
pp. 1793-1799 ◽  
Author(s):  
X. L. Li ◽  
Y. He ◽  
C. Q. Wu
Keyword(s):  
Support Vector Machine ◽  
Least Square ◽  
Support Vector ◽  
Harvest Year ◽  
Machine Analysis

scholarly journals Classification of degenerative parkinsonism subtypes by support-vector-machine analysis and striatal 123I-FP-CIT indices

2019 ◽  
Vol 266 (7) ◽  
pp. 1771-1781 ◽  
Author(s):  
Nicolas Nicastro ◽  
Jennifer Wegrzyk ◽  
Maria Giulia Preti ◽  
Vanessa Fleury ◽  
Dimitri Van de Ville ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Machine Analysis ◽  
Degenerative Parkinsonism

Detection and Classification of Watermelon Seeds Exterior Quality Based on LS-SVM Using Machine Vision

2013 ◽  
Vol 278-280 ◽  
pp. 727-730
Author(s):  
Xiai Chen ◽  
Shuang Ke ◽  
Ling Wang
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Machine Vision ◽  
Vision System ◽  
Least Square ◽  
Support Vector ◽  
Machine Vision System ◽  
High Quality ◽  
Surface Irregularities

A machine vision system was developed to investigate the detection of watermelon seeds exterior quality. The main characteristics of watermelon seeds appearance including area, perimeter, roughness and minimum enclosing rectangle were calculated by image analysis. Least square support vector machine optimized by genetic algorithm was applied for the classification of watermelon seeds exterior quality, and the broken seeds, normal seeds and high-quality seeds were distinguished finally. The surface irregularities defects of watermelon seeds were detected by machine vision grid laser. The experimental results show that the watermelon seeds exterior quality could be well detected and classified by machine vision based on least squares support vector machine.

scholarly journals Individual Classification of Mild Cognitive Impairment Subtypes by Support Vector Machine Analysis of White Matter DTI

2012 ◽  
Vol 34 (2) ◽  
pp. 283-291 ◽  
Author(s):  
S. Haller ◽  
P. Missonnier ◽  
F.R. Herrmann ◽  
C. Rodriguez ◽  
M.-P. Deiber ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
White Matter ◽  
Support Vector ◽  
Machine Analysis

scholarly journals Phishing Website Classification using Least Square Twin Support Vector Machine

2019 ◽  
Vol 9 (1) ◽  
pp. 2063-2068
Keyword(s):  
Support Vector Machine ◽  
Cyber Security ◽  
Classification Accuracy ◽  
Personal Information ◽  
Classification Problem ◽  
Least Square ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Security Issue

Phishing is one among the luring procedures used by phishing attackers in the means to abuse the personal details of clients. Phishing is earnest cyber security issue that includes facsimileing legitimate website to apostatize online users so as to purloin their personal information. Phishing can be viewed as special type of classification problem where the classifier is built from substantial number of website's features. It is required to identify the best features for improving classifiers accuracy. This study, highlights on the important features of websites that are used to classify the phishing website and form the legitimate ones by presenting a scheme Decision Tree Least Square Twin Support Vector Machine (DT-LST-SVM) for the classification of phishing website. UCI public domain benchmark website phishing dataset was used to conduct the experiment on the proposed classifier with different kernel function and calculate the classification accuracy of the classifiers. Computational results show that DT-LST-SVM scheme yield the better classification accuracy with phishing websites classification dataset

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