Created by W.Langdon from gp-bibliography.bib Revision:1.7906

- @Article{KEYVAN:2021:SAPAMBS,
- author = "Kiarash Keyvan and Mahmoud Reza Sohrabi and Fereshteh Motiee",
- title = "An intelligent method based on feed-forward artificial neural network and least square support vector machine for the simultaneous spectrophotometric estimation of anti hepatitis C virus drugs in pharmaceutical formulation and biological fluid",
- journal = "Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy",
- volume = "263",
- pages = "120190",
- year = "2021",
- ISSN = "1386-1425",
- DOI = "doi:10.1016/j.saa.2021.120190",
- URL = "https://www.sciencedirect.com/science/article/pii/S1386142521007678",
- keywords = "genetic algorithms, genetic programming, Spectrophotometry, Artificial neural network, Least square support vector machine, Sofosbuvir, Daclatasvir",
- abstract = "This study proposed simple and reliable spectrophotometry method for simultaneous analysis of hepatitis C antiviral binary mixture containing sofosbuvir (SOF) and daclatasvir (DAC). This technique is based on the use of feed-forward artificial neural network (FF-ANN) and least square support vector machine (LS-SVM). FF-NN with Levenberg-Marquardt (LM) and Cartesian genetic programming (CGP) algorithms was trained to determine the best number of hidden layers and the number of neurons. This comparison demonstrated that the LM algorithm had the minimum mean square error (MSE) for SOF (1.59 times 10-28) and DAC (4.71 times 10-28). In LS-SVM model, the optimum regularization parameter (?) and width of the function (?) were achieved with root mean square error (RMSE) of 0.9355 and 0.2641 for SOF and DAC, respectively. The coefficient of determination (R2) value of mixtures containing SOF and DAC was 0.996 and 0.997, respectively. The percentage recovery values were in the range of 94.03-104.58 and 94.04-106.41 for SOF and DAC, respectively. Statistical test (ANOVA) was implemented to compare high-performance liquid chromatography (HPLC) and spectrophotometry, which showed no significant difference. These results indicate that the proposed method possesses great potential ability for prediction of concentration of components in pharmaceutical formulations",
- }

Genetic Programming entries for Kiarash Keyvan Mahmoud Reza Sohrabi Fereshteh Motiee