## 4 Simulation
### Simulation of the FEAM
In the experimental setup of this study, the gap between training accuracy and validation accuracy began to widen from epoch 3. Around the point where an accuracy of approximately 0.45 was achieved, the difference between training and validation accuracy started to increase. After 25 epochs, while training accuracy reached up to 0.9, validation accuracy plateaued around 0.5 and did not improve further. \[F_{1}=2*\frac{1}{\frac{1}{1+\frac{1}{p}}}=\frac{p*\tau}{p+\tau}\]  
\[\text{p}=\text{precision, }\text{r}=\text{recall}\]  
Accuracy can be misleading statistics depending on the data. For instance, in a medical AI system designed to diagnose cancer, a model that always predicts a negative result would achieve a high accuracy because there are significantly more healthy individuals than those with cancer. Such a model, despite having high accuracy, is essentially meaningless. In this context, recall (also known as sensitivity) becomes a useful metric. Recall measures the proportion of actual positive cases that are correctly identified as positive. For example, if there are 10 positive cases and the model predicts all as negative, the recall will be 0 (0/10), indicating a problem with the model. Precision, on the other hand, contrasts with recall. If a model always predicts positive, the recall will be 1, as all actual positive cases are identified. However, this model would be as ineffective as one predicting all negatives if it produces many false positives. Precision addresses this issue by measuring the proportion of true positives among all instances the model predicted as positive. For example, if there are 10 positive cases and the model predicts 100 instances as positive, with only 10 being true positives, the precision would be 0.1 (10/100), indicating a problem with the model. In this paper, we use the F1 score, a combined statistic of precision and recall. The F1 score is the harmonic mean of precision and recall. The reason for using the harmonic mean is that it ensures that the F1 score is low when either precision or recall is close to zero. Figure 8 shows the F1 scores for each model. Model A has an F1 score of 0.4189, Model B has 0.3774, Model C has 0.4562, and Model D has 0.4752. It can be observed that the F1 score tends to increase from Model A to Model D, with Model D demonstrating the best performance. In artificial intelligence, the time required for model training, and the time it takes for the trained model to make predictions are critical metrics. When comparing the training times, DFLM takes approximately 1 s per epoch, while the CNN model requires about 100 s.  
Figure 7: Accuracy of existing CNN models confirmed with test data. Furthermore, for testing, the CNN model needs 8.128 s per step, whereas DFLM completes the evaluation of test data in just 0.004 s per step. This indicates that the proposed model operates at a very fast speed when put into practical use. The number of samples used for training was insufficient, which resulted in a low overall accuracy and a large variance in accuracy during training. This needs to be improved. We expect that future research will increase the training data and explore the use of various deep learning models, rather than just fully connected neural networks, to improve accuracy.