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Constructing An Intelligent Model Based On Support Vector Regression To Simulate The Solubility Of Drugs In Polymeric Media, Sait Senceroglu, Mohamed Arselene Ayari, Tahereh Rezaei, Fardad Faress, Amith Khandakar, Muhammad E. H. Chowdhury, Zanko Hassan Jawhar
Constructing An Intelligent Model Based On Support Vector Regression To Simulate The Solubility Of Drugs In Polymeric Media, Sait Senceroglu, Mohamed Arselene Ayari, Tahereh Rezaei, Fardad Faress, Amith Khandakar, Muhammad E. H. Chowdhury, Zanko Hassan Jawhar
International Business and Entrepreneurship Faculty Publications and Presentations
This study constructs a machine learning method to simultaneously analyze the thermodynamic behavior of many polymer–drug systems. The solubility temperature of Acetaminophen, Celecoxib, Chloramphenicol, D-Mannitol, Felodipine, Ibuprofen, Ibuprofen Sodium, Indomethacin, Itraconazole, Naproxen, Nifedipine, Paracetamol, Sulfadiazine, Sulfadimidine, Sulfamerazine, and Sulfathiazole in 1,3-bis[2-pyrrolidone-1-yl] butane, Polyvinyl Acetate, Polyvinylpyrrolidone (PVP), PVP K12, PVP K15, PVP K17, PVP K25, PVP/VA, PVP/VA 335, PVP/VA 535, PVP/VA 635, PVP/VA 735, Soluplus analyzes from a modeling perspective. The least-squares support vector regression (LS-SVR) designs to approximate the solubility temperature of drugs in polymers from polymer and drug types and drug loading in polymers. The structure of this machine …
Introducing A Linear Empirical Correlation For Predicting The Mass Heat Capacity Of Biomaterials, Reza Iranmanesh, Afham Pourahmad, Fardad Faress, Sevil Tutunchian, Mohammad Amin Ariana, Hamed Sadeqi, Saleh Hosseini, Falah Alobaid, Babak Aghel
Introducing A Linear Empirical Correlation For Predicting The Mass Heat Capacity Of Biomaterials, Reza Iranmanesh, Afham Pourahmad, Fardad Faress, Sevil Tutunchian, Mohammad Amin Ariana, Hamed Sadeqi, Saleh Hosseini, Falah Alobaid, Babak Aghel
International Business and Entrepreneurship Faculty Publications and Presentations
This study correlated biomass heat capacity (Cp) with the chemistry (sulfur and ash content), crystallinity index, and temperature of various samples. A five-parameter linear correlation predicted 576 biomass Cp samples from four different origins with the absolute average relative deviation (AARD%) of ~1.1%. The proportional reduction in error (REE) approved that ash and sulfur contents only enlarge the correlation and have little effect on the accuracy. Furthermore, the REE showed that the temperature effect on biomass heat capacity was stronger than on the crystallinity index. Consequently, a new three-parameter correlation utilizing crystallinity index and temperature was developed. This model was …