## 4 Result of Implementation
### Results of Model Performance Evaluation  
In Figure 7, we ran all epochs 50 times to find the one with the lowest data loss rate.  
As you can see in the graph, we have the smallest loss rate at epoch 30. Therefore, we tested each AI model by using epoch 30 as the data training section.  
As a performance test method for the AI models in this study, the structure of each model was applied to the format designed in Section 3.  
In Table 4, we analyze the test results for CNN+LSTM, CNN+Transformer, CNN, LSTM, and Extreme Learning Machine (ELM). The CNN+LSTM hybrid model has an accuracy of 89.00%, indicating that it is predicting the data fairly accurately. The model appears to predict the benign class relatively accurately, with a precision of 89.41% and a recall of 91.04%. The F1-score is 90.22%, indicating a balanced performance of the model. However, it is important to consider that the prediction error is quite large with an RMSE of 0.28. The CNN+Transformer hybrid model has an accuracy of 96.21%, indicating that it is predicting the data very accurately, with precision and recall of 94.53% and 94.16%, respectively, showing high accuracy and balanced prediction. The F1-score is very high  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Division** & **Total Data** & **Normal Data** & **Abnormal Data** \\ \hline Composition of Time Series Data & 120*1000 & & \\ Data measurement time (seconds) & 600 & & \\ Number of data & 1000 & 600 & 400 \\ \hline Total number data & 120,000 & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Construction of training data set.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Division** & **Normal Range (ppm)** & **Abnormal Range (ppm)** \\ \hline Hydrogen Sulfide & 0.4 \(\pm\) 0.3 & 40 \(\pm\) 20 \\ Ammonia & 9 \(\pm\) 4 & 300 \(\pm\) 100 \\ Benzene & 0.05 \(\pm\) 0.03 & 50 \(\pm\) 20 \\ Toluene & 12.36 \(\pm\) 3 & 500 \(\pm\) 200 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Construction of training data set.  
Figure 7: Find the optimal high-parameter value for the AI model.  
at 94.35%. It also has a low prediction error with an RMSE of 0.27. The accuracy of the CNN model is 87.19%, indicating that it is predicting the data somewhat accurately, and it tends to correctly predict the positive class, with precision and recall of 89.41% and 91.04%, respectively. The F1-score is 90.22%, indicating a balanced performance. It also has a low prediction error with an RMSE of 0.23. The accuracy of the LSTM model is quite high at 95.00%, with precision and recall of 92.55% and 94.17%, respectively, showing high accuracy and balanced prediction. The F1-score is high at 92.33%. It has a very low prediction error with an RMSE of 0.03. The ELM model performs poorly compared to the other models, with a moderate accuracy of 85.50%, and a moderate precision and recall of 85.26% and 85.19%, respectively. However, it has a low F1-score and RMSE of 85.19% and 0.31, respectively. Compared to other models, this model is relatively lacking in predictive results.  
As a result, the CNN+Transformer hybrid model performs the best, predicting the data very accurately. The LSTM model also performs well, and the CNN model performs moderately well. However, the CNN+LSTM and ELM models perform relatively poorly compared to the other models.