Effectiveness of a hybrid deep learning model integrated with a hybrid parameterisation model in decision-making analysis
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Effectiveness of a hybrid deep learning model integrated with a hybrid parameterisation model in decision-making analysis
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Frontiers in Artificial Intelligence and Applications
ISBN
978-1-64368-114-6
ISSN
0922-6389
e-ISSN
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Number of pages
12
Pages from-to
43-54
Publisher name
IOS Press BV
Place of publication
Amsterdam
Event location
Japonsko
Event date
Oct 22, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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