Paper proposes method that trains neural networks to learn spatial-temporal properties . Each neuron of neural networks corresponds to asubformula in a flexible wGSTL formula structure . We use a COVID-19dataset and a rain prediction dataset to evaluate the performance of the proposed framework and algorithms . We compare the performance with three baseline classification methods including K-nearestneighbors, decision trees, and artificial neural networks . The classificationaccuracy obtained by the proposedframework is comparable with the baselineclassification methods, according to the paper . Back to the page you came from, contact us at http://www.mailonline.co.uk/newslink.com/news/article-article-news/storyline-change-changing-changechange-changes-to-change.

Author(s) : Nasim Baharisangari, Kazuma Hirota, Ruixuan Yan, Agung Julius, Zhe Xu

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Code :
Coursera

Keywords : networks - neural - methods - dataset - news -

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