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- W4285503423 abstract "Recurrent Neural Networks (RNN) coupled with Long Short-Term Memory (LSTM) is known for being a powerful connection model that is associated with time series and having retaining states. This paper performed a thorough investigation on RNN-LSTM model performance when applied on five (5) different datasets. Results of the varied experiment showed that implementing RNN-LSTM requires reasonable consideration on the number of dense layers in the model as it improves the overall performance accuracy. Among all the experiments performed on different datasets, working on MNIST datasets significantly reached peaked over a 99% accuracy rate, having (4) dense layers, (2) LSTM, and (3) dropout layers. It has also been found in this paper that the number of batch size and epochs have a minimal effect on the RNN-LSTM model performance as it greatly depends on the actual structure of the model layers." @default.
- W4285503423 created "2022-07-15" @default.
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- W4285503423 date "2022-05-27" @default.
- W4285503423 modified "2023-09-25" @default.
- W4285503423 title "A Performance Review of Recurrent Neural Networks Long Short-Term Memory (LSTM)" @default.
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- W4285503423 doi "https://doi.org/10.1109/incet54531.2022.9824567" @default.
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