## 2 Materials and Methods
### Methods  
In this study, we aimed to enhance the classification accuracy compared with the previous method. Therefore, we proposed the addition of Gaussian noise to make the model more robust, more generalizable, and more accurate, especially when the available training data are limited and the models are prone to overfitting. Gaussian noise addition helps to avoid overfitting and produces a more generalizable model. This study proposes a combination of deep learning with One-Hot Encoding for classifying the enhancers' quality and strength. The strategy suggested in this study is composed of two steps. The first step is the feature representation phase, in which One-Hot Encoding is used to encode each encoding sequence to feed our CNN model for classification. The secondary step involves the prediction of enhancers through the use of a CNN-based model by supplying the already identified enhancers from the first step. A detailed illustration of the proposed model is shown in Figure 1. Each enhancer is 200 nt long and is formed by four nucleic acids, including adenine (A), guanine (G), cytosine (C), and thymine (T). The first two of the aforementioned nucleic acids (adenine and guanine) are purines, while the latter two (cytosine and thymine) are pyrimidines. A new matrix from a set of four binary numbers representing the four types of nucleic acids was assembled for One-Hot Encoding, where A was encoded by [1 0 0 0], C was encoded by [0 1 0 0], G was encoded by [0 0 1 0], and T was encoded as [0 0 0 1]. The input for the network was a 200 \(\times\) 4 encoding matrix sequence, which had a length of 200. We deployed the use of Gaussian noise in our input layer. Additional input signals can be considered in the model design, such as H3K27 ac signals; however, for simplicity, the design relied only on One-Hot Encoding. We used Gaussian noise with a standard deviation of 0.2. The network consisted of three 1-D CNN blocks comprising Conv1D, a regularizer layer, group normalization, max pooling, and a dropout layer. The regularization layer L2, also known as weight decay, forced the weights to be very close to zero, but not entirely zero. This helped to polish the model implementation for new data. The regularization layer was also useful for the slight tailoring of the algorithm for better functioning. Group normalization functioned as a layer that normalized the features within each group. A max pooling layer was used because of its ability to reduce overfitting by putting forward an epitomized form of the representation and trimming the computational cost by depleting some parameters. A flattened layer was used between the convolutional layer and the fully connected layer. A flattened layer was used to modify the feature matrix into a vector that could later be used in the fully connected neural network classifier via a rectified linear unit (ReLU), which was used for all except the last layer that would employ the sigmoid activation function. A similar architecture was used to classify strong and weak enhancers. The models were trained within 200 epochs, the use of early stopping with a patience level of 10, binary cross entropy, and an Adagrad optimizer with a learning rate of 0.0001. The work in [26] required the training of a language model for encoding the input sequence; however, the encoding of the proposed model was simpler because it did not require the use of a language model as it relied solely on One-Hot Encoding. Table 1 lists the detailed configurations of the proposed model for both stages.