Application of Artifical Neural Networks for Processing Some Biomedical Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23220%2F24%3A43972308" target="_blank" >RIV/49777513:23220/24:43972308 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10577768" target="_blank" >https://ieeexplore.ieee.org/document/10577768</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/MECO62516.2024.10577768" target="_blank" >10.1109/MECO62516.2024.10577768</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Application of Artifical Neural Networks for Processing Some Biomedical Data

Popis výsledku v původním jazyce

Artificial neural networks (ANN) are used for modeling, adaptive control, curve fitting and other applications where they can be trained by a measured data. ANNs are used as an auxiliary tool of artificial intelligence. ANN can learn from training results and can evaluate conclusions from a complex and evidently unrelated set of information. Increasingly, ANNs are used in wide range of applications and also in biomedical engineering. In this contribution, simple applications from the field of medical engineering, especially in exercise testing data evaluation are presented. It should be noted that all data was obtained by real measurement. All examples were processed using Matlab&apos;s Neural Toolbox.

Název v anglickém jazyce

Application of Artifical Neural Networks for Processing Some Biomedical Data

Popis výsledku anglicky

Artificial neural networks (ANN) are used for modeling, adaptive control, curve fitting and other applications where they can be trained by a measured data. ANNs are used as an auxiliary tool of artificial intelligence. ANN can learn from training results and can evaluate conclusions from a complex and evidently unrelated set of information. Increasingly, ANNs are used in wide range of applications and also in biomedical engineering. In this contribution, simple applications from the field of medical engineering, especially in exercise testing data evaluation are presented. It should be noted that all data was obtained by real measurement. All examples were processed using Matlab&apos;s Neural Toolbox.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20601 - Medical engineering

Projekt

<a href="/cs/project/EF18_069%2F0009855" target="_blank" >EF18_069/0009855: Elektrotechnické technologie s vysokým podílem vestavěné inteligence</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2024 13th Mediterranean Conference on Embedded Computing (MECO)

ISBN

979-8-3503-8756-8

ISSN

2377-5475

e-ISSN

2637-9511

Počet stran výsledku

4

Strana od-do

412-415

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Budva, Montenegro

Datum konání akce

11. 6. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001268606200001