Modelling drying time of Candesartan Cilexetil powder using computational intelligence technique
The aim of this work was to use two computational intelligence techniques, namely, artificial neural network (ANN) and support vector regression (SVR), to model the drying time of a pharmaceutical powder Candesartan Cilexetil, which is used for arterial hypertension treatment and heart failure. The...
| Permalink: | http://skupnikatalog.nsk.hr/Record/nsk.NSK01001144968/Details |
|---|---|
| Matična publikacija: |
Kemija u industriji (Online) 70 (2021), 3/4 ; str. 137-144 |
| Glavni autori: | Keskes, Sonia (Author), Hentabli, Mohamed, Laidi, Maamar, Hanini, Salah |
| Vrsta građe: | e-članak |
| Jezik: | eng |
| Predmet: | |
| Online pristup: |
https://doi.org/10.15255/KUI.2020.048 Kemija u industriji (Online) Hrčak |
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| 024 | 7 | |2 doi |a 10.15255/KUI.2020.048 | |
| 035 | |a (HR-ZaNSK)001144968 | ||
| 040 | |a HR-ZaNSK |b hrv |c HR-ZaNSK |e ppiak | ||
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| 042 | |a croatica | ||
| 044 | |a ci |c hr | ||
| 080 | 1 | |a 66 |2 2011 | |
| 080 | 1 | |a 615 |2 2011 | |
| 080 | 1 | |a 004 |2 2011 | |
| 100 | 1 | |a Keskes, Sonia |4 aut |9 HR-ZaNSK | |
| 245 | 1 | 0 | |a Modelling drying time of Candesartan Cilexetil powder using computational intelligence technique |h [Elektronička građa] / |c Sonia Keskes, Mohamed Hentabli, Maamar Laidi, Salah Hanini. |
| 300 | |b Graf. prikazi. | ||
| 504 | |a Bibliografija: 30 jed. | ||
| 504 | |a Summary ; Sažetak. | ||
| 520 | |a The aim of this work was to use two computational intelligence techniques, namely, artificial neural network (ANN) and support vector regression (SVR), to model the drying time of a pharmaceutical powder Candesartan Cilexetil, which is used for arterial hypertension treatment and heart failure. The experimental data set used in this work has been collected from previously published paper of the drying kinetics of Candesartan Cilexetil using vacuum dryer and under different operating conditions. The comparison between the two models has been conducted using different statistical parameters namely root mean squared error (RMSE) and determination coefficient (R2). Results show that SVR model shows high accuracy in comparison with ANN model to predict the non-linear behaviour of the drying time using pertinent variables with {R2 = 0.9991, RMSE = 0.262} against {R2 = 0.998, RMSE = 0.339} for SVR and ANN, respectively. | ||
| 520 | |a Cilj ovog rada bio je primjena dvije tehnike računalne inteligencije (umjetne neuronske mreže (ANN) i regresije potpornih vektora (SVR)) za modeliranje vremena sušenja farmaceutskog praha Candesartan Cilexetil, koji se primjenjuje za liječenje arterijske hipertenzije i zatajenje srca. Eksperimentalni skup podataka korišten u ovom radu prikupljen je iz prethodno objavljenog rada o kinetici sušenja Candesartan Cilexetila pomoću vakuumskog sušionika i pod različitim radnim uvjetima. Usporedba između dva modela provedena je pomoću različitih statističkih parametara, odnosno korijenom srednje kvadratne pogreške (RMSE) i koeficijenta određivanja (R2). Rezultati su pokazali da u usporedbi s modelom ANN model SVR pokazuje visoku točnost za predviđanje nelinearnog ponašanja vremena sušenja koristeći odgovarajuće varijable {R2 = 0,9991, RMSE = 0,262} u odnosu na {R2 = 0,998, RMSE = 0,339} za SVR i ANN. | ||
| 653 | 0 | |a Umjetne neuronske mreže |a Regresija potpornih vektora |a Vakuumsko sušenje |a Farmaceutski prah |a Bolesti srca |a Candesartan Cilexetil | |
| 700 | 1 | |a Hentabli, Mohamed |4 aut |9 HR-ZaNSK | |
| 700 | 1 | |a Laidi, Maamar |4 aut |9 HR-ZaNSK | |
| 700 | 1 | |a Hanini, Salah |4 aut |9 HR-ZaNSK | |
| 773 | 0 | |t Kemija u industriji (Online) |x 1334-9090 |g 70 (2021), 3/4 ; str. 137-144 |w nsk.(HR-ZaNSK)000530475 | |
| 981 | |b Be2021 |b B03/21 | ||
| 998 | |b tino2212 | ||
| 856 | 4 | 0 | |u https://doi.org/10.15255/KUI.2020.048 |
| 856 | 4 | 0 | |u http://silverstripe.fkit.hr/kui/issue-archive/article/786 |y Kemija u industriji (Online) |
| 856 | 4 | 1 | |y Digitalna.nsk.hr |
| 856 | 4 | 0 | |u https://hrcak.srce.hr/254682 |y Hrčak |