## 3 Results
### Comparison of the Predictive Performance of AlexNet and AlexNetMini Models  
As illustrated in Table 3, in Scenario 1, the mean F1-score for AlexNetMini exceeded that of AlexNet by 0.0035. Conversely, in Scenario 2, AlexNet demonstrated a mean F1-score that was 0.001 greater than that of AlexNetMini. In Scenario 3, the average F1-score for AlexNetMini surpassed that of AlexNet by 0.0056. Consequently, Scenario 3, which involved dataset division based on a cluster analysis of the gravity centers of catches, emerged as the most effective segmentation scheme for training data.  
Furthermore, Figure 7 indicated that as the training set ratio (TSP) for AlexNet increased from 0.5 to 0.8, the numbers of the experimental units with higher F1-scores (represented by blue dots) were identified as 5, 8, 2, and 15, respectively. In contrast, the numbers of relatively stable experimental units (indicated by orange dots) were 25, 32, 27, and 30, respectively. For AlexNetMini, the numbers of experimental units with relatively higher F1-scores were recorded as 0, 0, 1, and 29, while the numbers of relatively stable experimental units were 37, 30, 26, and 33. Considering these metrics, we proposed that the optimal TSP value be established at 0.8.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**AlexNet Model**} & \multicolumn{2}{c}{**AlexNetMini Model**} & \multirow{2}{*}{**Average of the Two Models**} \\ \cline{3-3} \cline{5-6}  & & **F1-Score** & & & **Average** & **F1-Score** & **Average** & **Models** \\ \hline Scenario 1 & 1 & K: 0.6957 & 0.6957 & L: 0.6992 & 0.6992 & 0.6975 \\ \hline \multirow{2}{*}{Scenario 2} & 1 & A: 0.7297 & \multirow{2}{*}{0.7505} & B: 0.7441 & \multirow{2}{*}{0.7495} & 0.7369 \\  & 2 & G: 0.7728 & & H: 0.7640 & & 0.7684 \\ \hline \multirow{3}{*}{Scenario 3} & 1 & C: 0.7501 & \multirow{3}{*}{0.7430} & D: 0.7419 & \multirow{3}{*}{0.7486} & 0.7640 \\ \cline{1-1} \cline{3-3} \cline{5-6}  & 2 & E: 0.7495 & & F: 0.7690 & & 0.7593 \\ \cline{1-1} \cline{3-3} \cline{5-6}  & 3 & I: 0.7339 & & J: 0.7386 & & 0.7363 \\ \hline \hline \end{tabular}
\end{table}
Table 3: A comparative analysis of the F1-score of AlexNet and AlexNetMini under different scenarios.  
Figure 6: Comparison of validation set accuracy results for AM3 (**A**) and AM6 (**B**).  
52 for the respective learning rates. In the case of the AlexNetMini model, the numbers of experimental units with higher F1-scores remained consistent at 14 and 16 for the different learning rates, while the numbers of relatively stable experimental units were noted as 61 and 65, respectively. These findings indicated that the learning rate exerted minimal influence on both the F1-score and the stability of the models.  
Furthermore, the analysis revealed that there were 128 relatively stable experimental units in the AlexNetMini model compared to 112 in the AlexNet model, suggesting that AlexNetMini demonstrated greater stability compared to its counterpart.  
According to Figure 7, both AlexNetMini and AlexNet exhibited five experimental units (indicated by yellow dots), achieving the highest F1-score when the dropout values were set at 0 and 0.2. However, as the dropout values increased to 0.4, 0.6, and 0.8, the numbers of the experimental units attaining the highest F1-score declined to 2, 0, and 0, respectively. Consequently, superior performance can be achieved at dropout values of 0 and 0.2.