Determination of Schizophrenia Using Electronic Nose via Support Vector Machine

2019 ◽  
Author(s):  
Rainier V. Leal ◽  
Alyssa Xyra C. Quiming ◽  
Jocelyn Flores Villaverde ◽  
Analyn N. Yumang ◽  
Noel B. Linsangan ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Electronic Nose ◽  
Support Vector

Determination of Pineapple Ripeness Using Support Vector Machine for Philippine Standards

2021 ◽  
Author(s):  
Erin Jelacio L. Aguilar ◽  
Giann Karlo P. Borromeo ◽  
Jocelyn Flores Villaverde
Keyword(s):  
Support Vector Machine ◽  
Support Vector

Utilization of Classification Techniques for the Determination of Liquefaction Susceptibility of Soils

2016 ◽  
pp. 124-160 ◽  
Author(s):  
J. Jagan ◽  
Prabhakar Gundlapalli ◽  
Pijush Samui
Keyword(s):  
Support Vector Machine ◽  
Earthquake Engineering ◽  
Relevance Vector Machine ◽  
Least Square ◽  
Support Vector ◽  
Ground Acceleration ◽  
Geotechnical Earthquake Engineering ◽  
Liquefaction Susceptibility ◽  
Input Variables

The determination of liquefaction susceptibility of soil is a paramount project in geotechnical earthquake engineering. This chapter adopts Support Vector Machine (SVM), Relevance Vector Machine (RVM) and Least Square Support Vector Machine (LSSVM) for determination of liquefaction susceptibility based on Cone Penetration Test (CPT) from Chi-Chi earthquake. Input variables of SVM, RVM and LSSVM are Cone Resistance (qc) and Peak Ground Acceleration (amax/g). SVM, RVM and LSSVM have been used as classification tools. The developed SVM, RVM and LSSVM give equations for determination of liquefaction susceptibility of soil. The comparison between the developed models has been carried out. The results show that SVM, RVM and LSSVM are the robust models for determination of liquefaction susceptibility of soil.

scholarly journals Determination Of Gas Mixture Components Using Fluctuation Enhanced Sensing And The LS-SVM Regression Algorithm

2015 ◽  
Vol 22 (3) ◽  
pp. 341-350 ◽  
Author(s):  
Łukasz Lentka ◽  
Janusz M. Smulko ◽  
Radu Ionescu ◽  
Claes G. Granqvist ◽  
Laszlo B. Kish
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Temperature Change ◽  
Nonlinear Regression ◽  
Ultraviolet Irradiation ◽  
Support Vector ◽  
Gas Mixture ◽  
Gas Concentration ◽  
Single Sensor

Abstract This paper analyses the effectiveness of determining gas concentrations by using a prototype WO3 resistive gas sensor together with fluctuation enhanced sensing. We have earlier demonstrated that this method can determine the composition of a gas mixture by using only a single sensor. In the present study, we apply Least-Squares Support-Vector-Machine-based (LS-SVM-based) nonlinear regression to determine the gas concentration of each constituent in a mixture. We confirmed that the accuracy of the estimated gas concentration could be significantly improved by applying temperature change and ultraviolet irradiation of the WO3 layer. Fluctuation-enhanced sensing allowed us to predict the concentration of both component gases.

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