An Email Classification Model Based on Rough Set and Support Vector Machine

2008 ◽  
Author(s):  
Zhiqing Zhu
Keyword(s):  
Support Vector Machine ◽  
Rough Set ◽  
Classification Model ◽  
Support Vector ◽  
Model Based ◽  
Email Classification

hERG Classification Model Based on a Combination of Support Vector Machine Method and GRIND Descriptors

2008 ◽  
Vol 5 (1) ◽  
pp. 117-127 ◽  
Author(s):  
Qiyuan Li ◽  
Flemming Steen Jørgensen ◽  
Tudor Oprea ◽  
Søren Brunak ◽  
Olivier Taboureau
Keyword(s):  
Support Vector Machine ◽  
Classification Model ◽  
Support Vector ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Model Based ◽  
Grind Descriptors

Support vector machine (SVM) classification model based rational design of novel tetronic acid derivatives as potent insecticidal and acaricidal agents

RSC Advances ◽  
2015 ◽  
Vol 5 (61) ◽  
pp. 49195-49203 ◽  
Author(s):  
Ting-Ting Yao ◽  
Jing-Li Cheng ◽  
Bing-Rong Xu ◽  
Min-Zhe Zhang ◽  
Yong-Zhou Hu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Rational Design ◽  
Classification Model ◽  
Support Vector ◽  
Svm Classification ◽  
Tetronic Acid ◽  
Model Based

A novel SVM classification model was constructed and applied in the development of novel tetronic acid derivatives as potent insecticidal and acaricidal agents.

scholarly journals A Comprehensive Support Vector Machine Binary hERG Classification Model Based on Extensive but Biased End Point hERG Data Sets

2011 ◽  
Vol 24 (6) ◽  
pp. 934-949 ◽  
Author(s):  
Meng-yu Shen ◽  
Bo-Han Su ◽  
Emilio Xavier Esposito ◽  
Anton J. Hopfinger ◽  
Yufeng J. Tseng
Keyword(s):  
Support Vector Machine ◽  
Classification Model ◽  
Support Vector ◽  
Data Sets ◽  
Model Based ◽  
End Point

scholarly journals ANALYSIS OF PHOTOPLETHYSMOGRAPHIC MORPHOLOGY IN SLEEP APNEA SYNDROME PATIENTS USING CURVE FITTING AND SUPPORT VECTOR MACHINE

2021 ◽  
pp. 2140019
Author(s):  
XINGE JIANG ◽  
SHOUSHUI WEI ◽  
LINA ZHAO ◽  
FEIFEI LIU ◽  
CHENGYU LIU
Keyword(s):  
Support Vector Machine ◽  
Sleep Apnea ◽  
Sleep Apnea Syndrome ◽  
Classification Model ◽  
Sleep Stages ◽  
Support Vector ◽  
Time Saving ◽  
Model Based ◽  
Apnea Syndrome ◽  
Healthy Persons

This paper develops a time-saving, simple, and comfortable method for detecting Sleep Apnea Syndrome (SAS). Seventy SAS patients and 17 healthy persons were randomly selected in this study, and nine analytical parameters (i.e., [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] of healthy persons and SAS patients during five sleep stages (i.e., W, R, N1, N2, and N3) were obtained to construct a SAS classification model based on logarithmic normal analytical parameters using the Support Vector Machine (SVM) method to fit Photoplethysmographic (PPG) signals. The results show that there were no statistical differences among the five sleep stages for either the healthy or SAS patients. However, there were significant differences in the measured logarithmic normal analytical parameters between the healthy persons and the SAS patients in each of the five sleep stages. The accuracies of the SAS classification model were 95.00%, 90.00%, 84.00%, 94.67%, and 90.77%, corresponding to the five sleep stages, respectively. The SAS classification model based on the SVM method of logarithmic normal analysis parameters can achieve higher classification accuracy for each of the five sleep stages. It can be considered to collect the patient’s pulse wave during the awake period, but not necessarily during the sleep period to classify and identify the SAS; it provides an idea for a convenient and comfortable SAS detection.

Fault Diagnosis Model Based on Support Vector Machine and Genetic Algorithm

2011 ◽  
Vol 130-134 ◽  
pp. 2535-2539 ◽  
Author(s):  
Wei Niu ◽  
Guo Qing Wang ◽  
Zheng Jun Zhai ◽  
Juan Cheng
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Parameters Optimization ◽  
Classification Model ◽  
Fault System ◽  
Support Vector ◽  
Diagnosis System ◽  
Intelligent Diagnosis ◽  
Model Based

Recently, the dominating difficulty that fault intelligent diagnosis system faces is terrible lack of typical fault samples, which badly prohibits the development of machinery fault intelligent diagnosis. Mainly according to the key problems of support vector machine need to resolve in fault intelligent diagnosis system, this paper makes more systemic and thorough researches in building fault classifiers, parameters optimization of kernel function. A decision directed acyclic graph fault diagnosis classification model based on parameters selected by genetic algorithm is proposed, abbreviated as GDDAG. Finally, GDDAG model is applied to rotor fault system, the testing results demonstrate that this model has very good classification precision and realizes the multi-faults diagnosis.

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