## 3 Materials and Methodology
### Deep Learning Models  
A subfield of machine learning called deep learning is focused on understanding how brain neurons function. Artificial neural networks (ANNs) are used to implement these algorithms. Several methods, such as supervised and unsupervised learning, can be used to train an algorithm for machine learning or deep learning. Supervised learning includes the task of classifying data examples that have been labeled during the training phase [42]. An LSTM network known as BiLSTM, or bidirectional LSTM, allows for better learning ateach data time step by feeding data from the beginning to the end of the model. As a result, features are learned more effectively at each time step. Bi-LSTM layers contain two units, with identical input and output in the hidden layers, to enable learning from both forward and backward time-series data. Time-series data are handled by one unit in the forward direction and the other in the backward direction. This arrangement is thought to provide future data to the layers for improving training time with stronger feature learning, leading to improved precision for multi-dimensional time-series data. In Figure 2, an example of a bidirectional LSTM cell structure is given with input data \(x_{t-1}\), \(x_{t+1}\), \(x_{t}\), \(x_{t-1}\), alternately processed through a forward hidden layer \(h_{t-1}\), \(h_{t}\), \(h_{t+1}\) and backward hidden layer \(h_{t-1}\), \(h_{t}\), \(h_{t+1}\) to create \(y_{t-1}\), \(y_{t}\), \(y_{t+1}\). A better prediction outcome can be obtained by optimizing the Bi-LSTM's feature information extraction through the forward and backward layers. The kernel size of the model doubles with each iteration according to the architecture of the BiLSTM architecture. An LSTM network with the addition of forward and backward layers forms a Bi-LSTM network. The CNN BiLSTM model comprises multiple layers of Bi-LSTM separated by reshape and batch normalization layers, starting with a one-dimensional convolutional neural network layer. The one-dimensional convolutional neural network layer and the max pooling layer are intended to be used for local perception, spatial layout, and parameter sharing. Smaller sets of parameters and free variables are made possible via parameter sharing, which speeds up feature extraction while consuming less computing power. The grouping of features by spatial arrangement makes it possible to assess a sparse matrix, which improves the ability to identify correlations between features. Lastly, the training time is reduced greatly by using fewer parameters, which is possible with local perception. Consequently, a one-dimensional convolutional neural network allows for quick spatial learning using the provided time-series data. The max pooling layer, which comes after the one-dimensional convolutional neural network layer, enables sample-based discretization of parameters to identify important features, reducing the training time and preventing overfitting. The batch normalization layer, which comes after max pooling, enables parameter normalization between intermediate layers to avoid shortened training periods. Reshape layers, which come after batch normalization layers, reshape the output of the layer before it for the following two BiLSTM layers. Convolutional neural networks (CNNs) are most commonly employed for image processing difficulties, but it has recently been revealed that CNNs can also be effective for natural language processing challenges. CNNs have made significant contributions to deep  
Figure 2: An example of a bidirectional LSTM cell structure. learning and artificial intelligence. With a layered architecture, CNNs exhibit rapid data analysis capabilities facilitated by advanced feature extraction and reduction techniques. Operating as a spatial data filtering feed-forward network, CNNs perform two operations: convolution and pooling. Convolution involves transforming input data into output data through the application of kernels, highlighting key features, and generating a feature map.