Selected article for: "artificial intelligence prediction and intelligence prediction"
Author: Laszlo Robert Kolozsvari; Tamas Berczes; Andras Hajdu; Rudolf Gesztelyi; Attila TIba; Imre Varga; Gergo Jozsef Szollosi; Szilvia Harsanyi; Szabolcs Garboczy; Judit Zsuga
Title: Predicting the epidemic curve of the coronavirus (SARS-CoV-2) disease (COVID-19) using artificial intelligence
  • Document date: 2020_4_22
    • ID: 35xpmdbj_14
    Snippet: The copyright holder for this preprint (which was not peer-reviewed) is . https://doi.org/10.1101/2020.04.17.20069666 doi: medRxiv preprint RNN-based model for prediction 186 The state-of-the-art for time series analysis is artificial intelligence-based analytic tools, which 187 have the best prediction performance. Recurrent Neural Networks (RNNs) are specifically 188 designed to cope with sequential input, characteristic of textual or temporal .....
    Document: The copyright holder for this preprint (which was not peer-reviewed) is . https://doi.org/10.1101/2020.04.17.20069666 doi: medRxiv preprint RNN-based model for prediction 186 The state-of-the-art for time series analysis is artificial intelligence-based analytic tools, which 187 have the best prediction performance. Recurrent Neural Networks (RNNs) are specifically 188 designed to cope with sequential input, characteristic of textual or temporal data. 22 This 189 architecture is a neural network-based architecture, that contains hidden layers chained the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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