## 2 Materials and Methods  
This paper proposes a fatigue detection system based on medical signals. The proposed method is divided into four stages: feature extraction, preprocessing, scaling, and classification, as shown in Figure 2. The feature extraction task is carried out using DWT, FFT, Discrete Cosine Transform (DCT), and Discrete Sine Transform (DST). The DWT is employed for noise reduction. The extracted signals are transformed using the trigonometric transformations, FFT, DCT, and DST. PCA is used through the preprocessing stage to improve the classification process and decorrelate the extracted features. The extracted features are then classified using machine and deep learning models. Classifiers such as DT, KNN, SVM, RF, MLP with backpropagation, QDA, and LR are used in the machine learning approach. In addition, the hyperparameters of the proposed algorithms are mechanically chosen using the grid search method [34; 35]. Table 1 illustrates samples of the titrations of hyperparameter selection, while Table 2 presents the results of our research regarding the optimal values for the classifier's hyper-parameters. The deep learning approach includes concatenated CNNs and 1D CNNs. The proposed CNN model consists of an input layer, three convolutional, max pooling, dropout, and batchnormalization layers, a flatten layer, and a dense layer. At the input layer, the data is reduced to 500 features to be enrolled in the proposed models. The maximum pooling and convolutional layers are used to extract features from input data. The first, second, and third convolutional layers have 128, 128, and 256 filters, respectively. Each set also includes a dropout and a batch normalization layer. These layers are used to prevent overfitting from occurring. Furthermore, the flatten layer, which prepares the feature vectors for enrollment in classification, completes the connection between the feature extractors and the classification layer. A dense layer with a softmax activation function is used to deploy the classification task. Figure 2: Proposed system for detecting fatigue. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Hyper Parameter**} & \multirow{2}{*}{**Accuracy**} \\ \hline
**No. of Filters** & **Activation Function** & **Dropout** & **Epochs** & **Learning Rate** & **Optimizer** & \\ \hline
128 & relu & 0.4 & 150 & 0.01 & adam & 1 \\
256 & relu & 0.2 & 200 & 0.001 & rmsprop & 1 \\
256 & relu & 0.3 & 150 & 0.01 & adam & 1 \\
256 & relu & 0.2 & 150 & 0.001 & rmsprop & 0.99900794 \\
64 & relu & 0.3 & 150 & 0.01 & rmsprop & 0.99900794 \\
256 & relu & 0.2 & 200 & 0.01 & rmsprop & 0.99900794 \\
128 & relu & 0.3 & 200 & 0.01 & adam & 0.99900794 \\
256 & relu & 0.2 & 150 & 0.001 & adam & 0.99900794 \\
64 & relu & 0.4 & 100 & 0.001 & rmsprop & 0.991071403 \\
64 & relu & 0.4 & 150 & 0.001 & adam & 0.990079343 \\
64 & relu & 0.3 & 100 & 0.01 & adam & 0.990079343 \\
64 & relu & 0.4 & 100 & 0.01 & adam & 0.990079343 \\
256 & relu & 0.4 & 100 & 0.001 & adam & 0.98087284 \\
256 & relu & 0.2 & 100 & 0.001 & adam & 0.980895224 \\
256 & relu & 0.3 & 200 & 0.01 & adam & 0.98107130164 \\
256 & 56 & 75 & 96 & 52 & 56 & 72 \\
59 & 68 & 88 & 91 & 57 & 62 & 65 \\
50 & 54 & 18 & 77 & 71 & 59 & 34 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Sample of iterations for Hyperparameter Optimization. In this paper, we propose another sequential model based on C-CNNs. Concatenation describes how multiple forks or sub-models process the input data. In a concatenation layer, the results of these individual models are combined. The proposed concatenated model's architecture, as shown in Figure 3, consists of an input layer, and two branches of a sequence of convolutional, max pooling, and dropout layers. The branch outputs are then concatenated using a concatenation layer. The concatenated feature vector is then enrolled in three convolutional, max pooling, and dropout layers. A flatten layer is used for fully connected tasks, and a dense layer is used for classification tasks.