Neural networks in natural sciences

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13440%2F23%3A43898260" target="_blank" >RIV/44555601:13440/23:43898260 - isvavai.cz</a>

Výsledek na webu

<a href="https://ans2023.ucm.sk/" target="_blank" >https://ans2023.ucm.sk/</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Neural networks in natural sciences

Popis výsledku v původním jazyce

Artificial neural networks (ANNs) have emerged as powerful tools for addressing complex problems invarious fields, including the natural sciences. This contribution involves an exploration of the applications of artificialneural networks, specifically focusing on their use in both regression and classification tasks. We place particularemphasis on utilizing the autoencoder model within the context of the anomaly detection in natural sciences.The autoencoder, a type of unsupervised learning model, has gained significant attention due to its ability tocatch meaningful representations of input data. Its architecture comprises an encoder, which compresses the input datainto a lower-dimensional latent space, and a decoder, which reconstructs the original input from the encodedrepresentation. This unique characteristic of the autoencoder makes it well-suited for extracting valuable features fromhigh-dimensional scientific datasets. We will present some applications of the above-mentioned models in the naturalsciences and highlight the advantages, such as their ability to handle noisy and incomplete data.

Název v anglickém jazyce

Neural networks in natural sciences

Popis výsledku anglicky

Artificial neural networks (ANNs) have emerged as powerful tools for addressing complex problems invarious fields, including the natural sciences. This contribution involves an exploration of the applications of artificialneural networks, specifically focusing on their use in both regression and classification tasks. We place particularemphasis on utilizing the autoencoder model within the context of the anomaly detection in natural sciences.The autoencoder, a type of unsupervised learning model, has gained significant attention due to its ability tocatch meaningful representations of input data. Its architecture comprises an encoder, which compresses the input datainto a lower-dimensional latent space, and a decoder, which reconstructs the original input from the encodedrepresentation. This unique characteristic of the autoencoder makes it well-suited for extracting valuable features fromhigh-dimensional scientific datasets. We will present some applications of the above-mentioned models in the naturalsciences and highlight the advantages, such as their ability to handle noisy and incomplete data.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10102 - Applied mathematics

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

Applied Natural Sciences 2023 Proceedings

ISBN

978-80-572-0357-5

ISSN

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e-ISSN

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Počet stran výsledku

5

Strana od-do

"nestrankovano"

Název nakladatele

University of SS. Cyril and Methodius in Trnava

Místo vydání

Trnava

Místo konání akce

Donovaly

Datum konání akce

18. 9. 2023

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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