## 4 Experiments
### Deep Learning Models  
Multi-Layer Perceptron (MLP): One kind of feedforward Artificial Neural Network (ANN) that forms the basis of deep learning or the Deep Neural Network (DNN) is the MLP. An input layer, one or more hidden layers, and an output layer are the three primary components of MLP. Every neuron in a layer is connected to every other neuron in the layers that surround it, indicating that the MLP network is fully connected. The input layer receives and normalizes the features from the input data. The hidden layers, whose number might vary, use representations they have learned to process the input data. Finally, the output layer generates predictions or decisions based on the extracted data [48; 75].  
Convolutional Neural Networks (CNNs): One of the most effective models of deep learning is CNN. Leveraging a convolutional architecture, CNNs act as feedforward neural networks that automatically extract features from incoming data [76]. A classifier and a feature extractor are combined in CNN's two-stage architecture to allow automatic feature extraction and end-to-end training with minimal pre-processing. In contrast to conventional techniques, CNNs do not require manual feature engineering since they learn and recognize features straight from the data [48; 77].  
Long Short-Term Memory (LSTM): LSTM [78] is an advanced variant of Recurrent Neural Networks (RNNs) particularly designed to address the common issue of long-term dependencies. LSTM networks are highly effective in retaining information over long sequences and solving the vanishing gradient problem. An LSTM processes the current input and the output from the previous phase at each time step, resulting in an output that is transferred to the subsequent time step. The last hidden layer at the last time step is often used for classification. Three gates make up an LSTM: input, forget, and output gates. These gates are essential for efficiently handling reading and writing operations inside the LSTM architecture by controlling the flow of data into and out of the memory unit [79].  
Bidirectional LSTM (Bi-LSTM): Bi-LSTM is an enhancement of the LSTM that captures both past and future context in sequence modeling tasks, addressing the limitations of LSTM designs. Unlike conventional LSTM, which processes input data in a single forward orientation, Bi-LSTM processes data in both forward and reverse orientations, allowing it to leverage additional contextual information from the entire sequence [79].  
Gated Recurrent Unit (GRU): Another recurrent neural network (RNN) version that tackles the short-term memory problem in sequence modeling is the GRU [80], which has a simpler architecture than LSTM. With only two gates instead of the three LSTM gates and no cell state, it has a simpler design that allows for quicker learning because of its lower computational complexity. The GRU can efficiently handle data from previous time steps and record long-term situations in arrangements because of its design, which consists of a reset gate, an update gate, and the current memory value [81].  
Bidirectional GRU (Bi-GRU): The Bi-GRU is an extension of the GRU design that incorporates both past as well as future instances in sequential modeling problems, hence overcoming some of the normal GRU's constraints. Unlike the GRU, which only examines input sequences in a forward orientation, the Bi-GRU can operate in both forward and reverse [81].  
Transformer: The transformer architecture [82], widely recognized for its effectiveness in modeling sequential data, consists of multiple identical layers. A position-wise completely linked feedforward network and a multi-head self-attention mechanism are integrated into each layer. Layer normalization and residual connections are added to these elements to stabilize training and promote gradient flow.