Effectiveness of a hybrid deep learning model integrated with a hybrid parameterisation model in decision-making analysis
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F20%3A50017210" target="_blank" >RIV/62690094:18450/20:50017210 - isvavai.cz</a>
Výsledek na webu
<a href="http://dx.doi.org/10.3233/FAIA200551" target="_blank" >http://dx.doi.org/10.3233/FAIA200551</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3233/FAIA200551" target="_blank" >10.3233/FAIA200551</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Effectiveness of a hybrid deep learning model integrated with a hybrid parameterisation model in decision-making analysis
Popis výsledku v původním jazyce
Deep learning has recently gained the attention of many researchers in various fields. A new and emerging machine learning technique, it is derived from a neural network algorithm capable of analysing unstructured datasets without supervision. This study compared the effectiveness of the deep learning (DL) model vs. a hybrid deep learning (HDL) model integrated with a hybrid parameterisation model in handling complex and missing medical datasets as well as their performance in increasing classification. The results showed that 1) the DL model performed better on its own, 2) DL was able to analyse complex medical datasets even with missing data values, and 3) HDL performed well as well and had faster processing times since it was integrated with a hybrid parameterisation model. © 2020 The authors and IOS Press. All rights reserved.
Název v anglickém jazyce
Effectiveness of a hybrid deep learning model integrated with a hybrid parameterisation model in decision-making analysis
Popis výsledku anglicky
Deep learning has recently gained the attention of many researchers in various fields. A new and emerging machine learning technique, it is derived from a neural network algorithm capable of analysing unstructured datasets without supervision. This study compared the effectiveness of the deep learning (DL) model vs. a hybrid deep learning (HDL) model integrated with a hybrid parameterisation model in handling complex and missing medical datasets as well as their performance in increasing classification. The results showed that 1) the DL model performed better on its own, 2) DL was able to analyse complex medical datasets even with missing data values, and 3) HDL performed well as well and had faster processing times since it was integrated with a hybrid parameterisation model. © 2020 The authors and IOS Press. All rights reserved.
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
—
OECD FORD obor
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Návaznosti výsledku
Projekt
—
Návaznosti
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Ostatní
Rok uplatnění
2020
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
Frontiers in Artificial Intelligence and Applications
ISBN
978-1-64368-114-6
ISSN
0922-6389
e-ISSN
—
Počet stran výsledku
12
Strana od-do
43-54
Název nakladatele
IOS Press BV
Místo vydání
Amsterdam
Místo konání akce
Japonsko
Datum konání akce
22. 10. 2020
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
—