Several techniques have been proposed to address the vanishing and exploding gradient problem in RNNs. For instance, gradient clipping can prevent the gradient from exploding, while parameter regularization can help with the vanishing gradient issue. One of the most notable solutions is the introduction of the long short-term memory (LSTM) model by Hochreiter and Schmidhuber . The LSTM model allows the RNN to learn whether to remember or forget relevant information using gated memory cells, making it particularly well-suited for processing long sequences of data. Another variant of the LSTM model is the gated recurrent unit (GRU), which has a simpler structure but performs comparably well to LSTM .