Browsing by Subject "genetic programming"

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Access status: Open Access ,
Computational intelligence for predicting biological effects of drug absorption in lungs
(Wydawnictwa AGH, 2019) Pacławski, Adam; Szlęk, Jakub; Mendyk, Aleksander
Recently, the lungs have been extensively examined as a route for delivering drugs (active pharmaceutical ingredients, APIs) into the bloodstream, this is mainly due to the possibility of the noninvasive administration of macromolecules such as proteins and peptides. The absorption mechanisms of chemical compounds in the lungs are still not fully understood, which makes pulmonary formulation composition development challenging. This manuscript presents the development of an empirical model capable of predicting the excipients’ influence on the absorption of drugs in the lungs. Due to the complexity of the problem and the not-fully-understood mechanisms of absorption, computational intelligence tools were applied. As a result, a mathematical formula was established and analyzed. The normalized root-mean-squared error (NRMSE) and $R^2$ of the model were 4.57%, and 0.83, respectively. The presented approach is beneficial both practically by developing an in silico predictive model and theoretically by gaining knowledge of the influence of APIs and excipient structure on absorption in the lungs.
Access status: Open Access ,
Evolutionary data driven modelling and many objective optimization of non linear noisy data in the blast furnace iron making process
(Wydawnictwa AGH, 2021) Mahanta, Bashista Kumar; Chakraborti, Nirupam
The optimization of process parameters in modern blast furnace operation, where both control and accessing large data set with multiple variables and objectives is a challenging task. To handle such non-linear and noisy data set deep learning techniques have been used in recent time. In this study an evolutionary deep neural network algorithm (EvoDN2) has been applied to derive a data driven model for blast furnace. The optimal front generated from deep neural network is compared against the optimal models developed from bi-objective genetic programming algorithm (BioGP) and evolutionary neural network (EvoNN). The optimization process is applied to all the training models by using constraint based reference vector evolutionary algorithm (cRVEA).