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- W4319065764 abstract "A novel supervised model is presented in this paper with One-Dimensional Convolutional Neural Networks (CNNs) followed by double Bidirectional Gated Recurrent Units (2-BiGRUs) for text categorization. The overall model contains six components, two of which are required. Starting with the input layer which converts the text into an embedding matrix and then passed to the embedding layer via GloVe and Word2Vec embeddings. The convolution block then extracts the bidirectional temporal characteristics from text data and flatting them by the Flatten layer, using Softmax activation and dropouts; the results are then normalized using the Batch Normalization layer and sent to the Dense or output Layer which generates the categorization results by using the Softmax activation. Multiple experiments with loss and precision scores are the primary criterion for evaluating the proposed model on WebKb, Reuters R8 and R52, AG-News (AG), and 20 NewsGroups (20NG) datasets. Our model achieves better precision results on the WebKB, AG, and 20 NG data sets than the best results of other approaches. The proposed model demonstrates that performance scores are superior." @default.
- W4319065764 created "2023-02-04" @default.
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- W4319065764 date "2023-01-01" @default.
- W4319065764 modified "2023-09-27" @default.
- W4319065764 title "Text Categorization Using Convolutional and Bidirectional Fast Gated Recurrent Unit" @default.
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- W4319065764 doi "https://doi.org/10.1007/978-3-031-25344-7_46" @default.
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