## 4 Experiments
### Evaluation Metrics  
Our proposed architecture generalization performance and efficiency require a practical and feasible experimental estimation method and standard measurement. Four  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Search Space** \\ \hline Number of filter & \{16:512\} \\ Kernel size & \{2, 4, 6:12\} \\ Batch size & \{16:256\} \\ Activation function & \{ReLU, Linear, Sigmoid, Tanh\} \\ Optimization method & \{Adam, EAG, RMSprop, Nadam, Adamax, Adadelta, Adagrad\} \\ Dropout rate & \{0:0.5\} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameters search space.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Layer** & **Type** & **Kernel \(\times\) Filter** & **Other Parameters** \\ \hline
1 & Convo1D & 2 \(\times\) 128 & Activation = ReLU, Strides = 2 \\
2 & MaxPool1D & — & Size = 2, Strides = 2 \\
3 & Convo1D & 2 \(\times\) 128 & Activation = ReLU, Strides = 2 \\
4 & MaxPool1D & — & Size = 2, Strides = 2 \\
5 & Convo1D & 2 \(\times\) 128 & Activation = ReLU, Strides = 2 \\
6 & Convo1D & 2 \(\times\) 128 & Activation = ReLU, Strides = 2 \\
7 & Flatten & — & — \\
8 & Dense & 1 \(\times\) 128 & Activation = ReLU \\
9 & Dropout & — & Rate = 0.25 \\
10 & Dense (Output) & \{1 \(\times\) 10\} & Activation = Softmax \\ \hline \hline \end{tabular}
\end{table}
Table 3: Proposed model detailed parameters.  
measurement indices, namely: sensitivity, specificity, accuracy, precision, recall, F-measure, area under ROC curve, and the area under the precision-recall curve, were used to measure models performance. Each task output was assessed using different metrics depending on the task. Various performance metrics often contribute to distinct decision outcomes, especially when evaluating various classifiers' capabilities.  
\[\textit{Sensitivity}=\frac{TP}{TP+FN} \tag{2}\]  
\[\textit{Specificity}=\frac{TN}{TN+FP} \tag{3}\]  
\[\textit{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN} \tag{4}\]  
\[\textit{Precision}=\frac{TP}{TP+FP} \tag{5}\]  
\[\textit{Recall}=\frac{TP}{TP+FN} \tag{6}\]  
\[\textit{F-measure}=\frac{Precision\times Recall}{Precision+Recall} \tag{7}\]  
The sample is divided into four cases according to the combination of precise classes and algorithm prediction classes: true-positive (TP), false-positive (FP), true-negative (TN), and false-negative (FN). Sensitivity is the probability that groups are correctly categorized. However, specificity is the probability that non-classes are classified correctly. In contrast, precision is the possibility of non-classes being correctly identified. The F-score or F-measure is commonly used to evaluate information in the network. Furthermore, used to combine the precision and recall of the model. Moreover, it measures the networks' accuracy on the selected dataset and evaluates binary classification systems, which classify examples into 'positive' or 'negative'. ROC curves plot y-axis sensitivity and x-axis specificity, corresponding to the decision thresholds. AUROC is indicating the area under the ROC curve, which is the model goodness-of-fit. A perfect model has an AUROC of 1, and random output is indicated by an AUROC of 0.5. The closer AUROC to 1, the better the performance of the model [94].