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.

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

Study about Recognition of Digital Meter Dial Reading Based on SVM

2014 ◽  
Vol 615 ◽  
pp. 194-197
Author(s):  
Zhen Yuan Tu ◽  
Fang Hua Ning ◽  
Wu Jia Yu
Keyword(s):  
Support Vector Machine ◽  
Cross Validation ◽  
Recognition Rate ◽  
Classification Model ◽  
Support Vector ◽  
Feature Points ◽  
Svm Classification ◽  
Performance Factors ◽  
Model Combining ◽  
Fast Recognition

In practice, it is difficult for Support Vector Machine (SVM) to have a relatively high recognition rate as well as a quite fast recognition speed. In order to resolve this defect, in this paper we build a SVM classification model combining numerical characteristics. We use readings of rotary natural meters as the test temple, do positioning, preprocessing, feature points extracting, classifying and other series of operations to the numeric region of the dial. Then with the idea of cross-validation, we keep doing parameter optimation to SVM. At last, after making a comprehensive contrast of the effects which numerous performance factors make on the experimental outputs, we try to give our explanation of the outputs from different perspectives.

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.

scholarly journals Automatic Identification of Sedimentary Facies Based on a Support Vector Machine in the Aryskum Graben, Kazakhstan

2019 ◽  
Vol 9 (21) ◽  
pp. 4489 ◽  
Author(s):  
Ai ◽  
Wang ◽  
Sun
Keyword(s):  
Support Vector Machine ◽  
Oil And Gas ◽  
Amplitude Ratio ◽  
Sedimentary Facies ◽  
Field Application ◽  
Classification Model ◽  
Support Vector ◽  
Automatic Identification ◽  
Depositional Facies ◽  
Svm Classification

The Aryskum Depression in the South Turgay Basin has shown improving exploration prospects for subtle reservoirs, due to investment in the exploration workload and more comprehensive geological research. Among them, lithologic stratigraphic reservoirs have gradually become one of the focuses of oil and gas exploration. At present, deduction of the sedimentary characteristics of the target layer through core wells using artificial exploration has become an urgent problem to be solved. We selected 16 artificially interpreted coring wells in the Aryskum Graben for this study. Using the parameters of the gamma-ray (GR) curve of coring wells and support vector machine (SVM) classification algorithms, we developed an automatic identification model of sedimentary facies in the study area. The application of the SVM includes the following steps: Firstly, using the GR curve of 16 coring wells, six quantitative indexes defined as standard deviation, relative gravity, curve amplitude ratio, average median, average slope, and mutation amplitude, are selected to quantify the logging curve in the study area, thus realizing the description of the logging curve form. Secondly, training samples are selected to establish an SVM classification model. Finally, a quantitative discrimination model based on the SVM algorithm is established to realize the classification of depositional facies. Field application shows that this solution can be effectively used in uncored wells to identify depositional facies with a rate of accuracy approaching 70%. Our results provide new methods for the identification of sedimentary facies in the study area. The results will also provide a theoretical basis, as well as data basis, for further fine division of microfacies in the study area.

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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