scholarly journals Support Vector Machines-Based Quantitative Structure−Property Relationship for the Prediction of Heat Capacity

2004 ◽  
Vol 44 (4) ◽  
pp. 1267-1274 ◽  
Author(s):  
C. X. Xue ◽  
R. S. Zhang ◽  
H. X. Liu ◽  
M. C. Liu ◽  
Z. D. Hu ◽  
...  
Keyword(s):  
Heat Capacity ◽  
Support Vector Machines ◽  
Support Vector ◽  
Quantitative Structure ◽  
Structure Property ◽  
Quantitative Structure Property Relationship ◽  
Property Relationship ◽  
Vector Machines ◽  
Structure Property Relationship

scholarly journals CMC of diverse Gemini surfactants modelling using a hybrid approach combining SVR-DA

2020 ◽  
pp. 48-48
Author(s):  
Maamar Laidi ◽  
Abdallah Abdallah el Hadj ◽  
Cherif Si-Moussa ◽  
Othmane Benkortebi ◽  
Mohamed Hentabli ◽  
...  
Keyword(s):  
Least Squares ◽  
Support Vector Regression ◽  
Gemini Surfactants ◽  
Hybrid Approach ◽  
Support Vector ◽  
Quantitative Structure ◽  
Structure Property ◽  
Quantitative Structure Property Relationship ◽  
Property Relationship ◽  
Structure Property Relationship

As quantitative structure-property relationship (QSPR) technique provides a suitable tool to predict the critical micelle concentration (CMC) of Gemini surfactants from their structure descriptors. In this study, a comparative work was conducted to model the CMC property of 211 diverse Gemini surfactants based on their structural characteristics using linear and non-linear quantitative structure-property relationship models. Least squares model (OLS) and partial least squares (PLS) against k-nearest neighbours regression model (KNN), artificial neural network (ANN) and support vector regression (SVR) have been developed to model the CMC. Molecular descriptors were calculated and screened to remove unsuitable descriptors and improve the learning. Results indicate that the improved performance of support vector regression when the hyper-parameters are optimized using Dragonfly algorithm (SVR-DA) was highly capable of predicting the pCMC (-logCMC) values with an average absolute relative deviation (AARD) of 0.666 and coefficient of determination (R?) of 0.9971 for the global dataset.

scholarly journals Artificial Neural Network and Support Vector Regression Applied in Quantitative Structure-property Relationship Modelling of Solubility of Solid Solutes in Supercritical CO2

2020 ◽  
Vol 69 (11-12) ◽  
pp. 611-630
Author(s):  
Mohammed Moussaoui ◽  
Maamar Laidi ◽  
Salah Hanini ◽  
Mohamed Hentabli
Keyword(s):  
Support Vector ◽  
Quantitative Structure ◽  
Structure Property ◽  
Qspr Model ◽  
Quantitative Structure Property Relationship ◽  
Qsar Studies ◽  
Property Relationship ◽  
Proposed Model ◽  
Structure Property Relationship ◽  
Better Than

In this study, the solubility of 145 solid solutes in supercritical CO<sub>2</sub> (scCO<sub>2</sub>) was correlated using computational intelligence techniques based on Quantitative Structure-Property Relationship (QSPR) models. A database of 3637 solubility values has been collected from previously published papers. Dragon software was used to calculate molecular descriptors of 145 solid systems. The genetic algorithm (GA) was implemented to optimise the subset of the significantly contributed descriptors. The overall average absolute relative deviation MAARD of about 1.345 % between experimental and calculated values by support vector regress SVR-QSPR model was obtained to predict the solubility of 145 solid solutes in supercritical CO<sub>2</sub>, which is better than that obtained using ANN-QSPR model of 2.772 %. The results show that the developed SVR-QSPR model is more accurate and can be used as an alternative powerful modelling tool for QSAR studies of the solubility of solid solutes in supercritical carbon dioxide (scCO<sub>2</sub>). The accuracy of the proposed model was evaluated using statistical analysis by comparing the results with other models reported in the literature.

Estimation of the Heat Capacity of Ionic Liquids: A Quantitative Structure–Property Relationship Approach

2013 ◽  
Vol 52 (36) ◽  
pp. 13217-13221 ◽  
Author(s):  
Mehdi Sattari ◽  
Farhad Gharagheizi ◽  
Poorandokht Ilani-Kashkouli ◽  
Amir H. Mohammadi ◽  
Deresh Ramjugernath
Keyword(s):  
Heat Capacity ◽  
Ionic Liquids ◽  
Quantitative Structure ◽  
Structure Property ◽  
Quantitative Structure Property Relationship ◽  
Property Relationship ◽  
Structure Property Relationship
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