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

Abstract

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.

Cumulative Drug Release Modelling of PCL-PVP Encapsulated Tramadol by DA-SVM, MLR, PLS, and OLS Regression Techniques

“…Partial least squares (PLS) regression is a statistical method that transforms matrix of independent variables X into dependent variable Y. In our case, X is an [n_m] matrix of reflectance, 8 where n is the number of inputs, m is the number of observations, and Y is the matrix containing cumulative drug release values. PLS regression decomposes X and Y by projecting them in new directions with the restriction that the decomposition describes how the variables change together as much as possible.…”

“…The objective of this model is to construct a mathematical model that can be utilised to predict the dependent variable based on the inputs of independent variables or the predictors. 8,22 Multiple linear regression (MLR)model has been used to obtain the significant relationship as well as correlation between the input variable and the output.…”

“…The results of all phases are summarised in Table 5, and presented graphically in Fig. (8). 9) is a graphical representation of the residual errors in estimated compared to the experimental cumulative drug release data.…”

“…Wide margin support vector machines are a set of learning techniques designed to solve the discrimination and regression problems. [7][8][9][10][11] Support vector machines are widely utilised in many fields (bioinformatics, information research, computer vision, pharmaceuticals, etc. ), mainly due to their fast training, better accuracy, and robustness.…”

Dragonfly Support Vector Machine Modelling of the Adsorption Phenomenon of Certain Phenols by Activated Carbon Fibres

“…In five iterations, the steps starting with the data division and up to the development of the SVM model are repeated, and the minimum value of RMSE obtained is saved as the best value. [23][24][25][26] The DA then generates a new population of parameter hyperplanes for the SVM algorithm, and the same set of steps is repeated in order to obtain a new best RMSE, among which the minimum RMSE corresponds to the DA-SVM model optimal result.…”