لخّصلي

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تلخيص قليل Long Short-Term Memory (LSTM) represents an advanced form of
recurrent neural network (RNN) that specifically deals with the
challenge of long-term dependencies. In contrast to traditional RNNs,
LSTM incorporates specialized modules to address this issue,
enhancing its ability to capture extended patterns accurately. Initially
introduced in 1997, LSTM surpasses the limitations of RNNs by
integrating a gate mechanism with three essential gates: the input gate,
forget gate, and output gate. These gates prevent the issues of
vanishing or exploding errors, allowing for adjustments to the cell state
vector to capture lengthy dependencies effectively. This structured
information flow within the cell empowers the network to retain
multiple time dependencies with various characteristics. Additionally,
recurrent neural networks (RNNs) complement LSTM by recollecting
past knowledge and applying it to the current LSTM input. The LSTM
architecture has demonstrated success in various statistical AI tasks
and is widely used in speech recognition, image processing,
manufacturing, autonomous systems, and communications.
An example of its application is the Recognition of Voice of
Emotional Speech (RAVDESS) dataset, which was utilized in
the study.