<title>Application of least-squares support vector machine (LS-SVM) to determination of deep level defect centers parameters in semi-insulating GaAs</title>

Application of adaptive Neuro-fuzzy interference system, fuzzy interference system and least squares support vector machine for rapid simultaneous spectrophotometric determination of antipsychotic drugs in binary mixtures and biological fluid

Optik ◽

10.1016/j.ijleo.2021.166569 ◽

2021 ◽

Vol 232 ◽

pp. 166569

Author(s):

Mojdeh Alibakhshi ◽

Mahmoud Reza Sohrabi ◽

Mehran Davallo

Keyword(s):

Support Vector Machine ◽

Least Squares ◽

Spectrophotometric Determination ◽

Antipsychotic Drugs ◽

Biological Fluid ◽

Support Vector ◽

Fuzzy Interference System ◽

Interference System ◽

Neuro Fuzzy

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

Metrology and Measurement Systems ◽

10.1515/mms-2015-0039 ◽

2015 ◽

Vol 22 (3) ◽

pp. 341-350 ◽

Cited By ~ 29

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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Determination of Protein Content of Auricularia Auricula Using Spectroscopy and Least Squares-Support Vector Machine

2009 Fifth International Conference on Natural Computation ◽

10.1109/icnc.2009.495 ◽

2009 ◽

Author(s):

Fei Liu ◽

Guangming Sun ◽

Yong He

Keyword(s):

Support Vector Machine ◽

Least Squares ◽

Protein Content ◽

Support Vector ◽

Auricularia Auricula

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Determination of citric acid of lemon vinegar using visible/near infrared spectroscopy and least squares-support vector machine

10.1117/12.821895 ◽

2008 ◽

Author(s):

Fei Liu ◽

Li Wang ◽

Yong He

Keyword(s):

Support Vector Machine ◽

Infrared Spectroscopy ◽

Citric Acid ◽

Least Squares ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Support Vector

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Determination of generators' contributions to, loads in pool based power system using Least Squares Support Vector Machine

2010 4th International Power Engineering and Optimization Conference (PEOCO) ◽

10.1109/peoco.2010.5559183 ◽

2010 ◽

Cited By ~ 1

Author(s):

M. W. Mustafa ◽

M. H. Sulaiman ◽

H. Shareef ◽

S. N. Abd. Khalid

Keyword(s):

Support Vector Machine ◽

Power System ◽

Least Squares ◽

Support Vector

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Determination of the speed of sound in ionic liquids using a least squares support vector machine group contribution method

Fluid Phase Equilibria ◽

10.1016/j.fluid.2014.01.021 ◽

2014 ◽

Vol 367 ◽

pp. 188-193 ◽

Cited By ~ 11

Author(s):

Mehdi Sattari ◽

Farhad Gharagheizi ◽

Poorandokht Ilani-Kashkouli ◽

Amir H. Mohammadi ◽

Deresh Ramjugernath

Keyword(s):

Support Vector Machine ◽

Ionic Liquids ◽

Least Squares ◽

Speed Of Sound ◽

Group Contribution ◽

Support Vector ◽

Group Contribution Method ◽

Machine Group

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Determination of acetic acid of fruit vinegars using near infrared spectroscopy and least squares-support vector machine

2008 International Conference on Machine Learning and Cybernetics ◽

10.1109/icmlc.2008.4620593 ◽

2008 ◽

Author(s):

Fei Liu ◽

Li Wang ◽

Yong He

Keyword(s):

Support Vector Machine ◽

Acetic Acid ◽

Infrared Spectroscopy ◽

Least Squares ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Support Vector

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Spectroscopic Determination of Aboveground Biomass in Grasslands Using Spectral Transformations, Support Vector Machine and Partial Least Squares Regression

Sensors ◽

10.3390/s130810027 ◽

2013 ◽

Vol 13 (8) ◽

pp. 10027-10051 ◽

Cited By ~ 32

Author(s):

Miguel Marabel ◽

Flor Alvarez-Taboada

Keyword(s):

Support Vector Machine ◽

Least Squares ◽

Partial Least Squares ◽

Aboveground Biomass ◽

Partial Least Squares Regression ◽

Spectroscopic Determination ◽

Support Vector ◽

Least Squares Regression ◽

Spectral Transformations

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The application of continuous wavelet transform and least squares support vector machine for the simultaneous quantitative spectrophotometric determination of Myricetin, Kaempferol and Quercetin as flavonoids in pharmaceutical plants

Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy ◽

10.1016/j.saa.2015.07.073 ◽

2016 ◽

Vol 152 ◽

pp. 443-452 ◽

Cited By ~ 13

Author(s):

Mahmoud Reza Sohrabi ◽

Golnaz Darabi

Keyword(s):

Support Vector Machine ◽

Wavelet Transform ◽

Least Squares ◽

Spectrophotometric Determination ◽

Continuous Wavelet Transform ◽

Support Vector ◽

Continuous Wavelet

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Determination of Sugar Content of Instant Milk-Tea Using Effective Wavelengths and Least Squares-Support Vector Machine

Lecture Notes in Electrical Engineering - Advances in Neural Network Research and Applications ◽

10.1007/978-3-642-12990-2_104 ◽

2010 ◽

pp. 893-900

Author(s):

Fei Liu ◽

Yong He

Keyword(s):

Support Vector Machine ◽

Least Squares ◽

Sugar Content ◽

Support Vector

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