## 3 Results
### Performance Evaluation  
Using the core 25 samples, we obtained a precision of 0.9, indicating that 90% of the instances it identified as positive were indeed correct. The recall was approximately 0.857, reflecting the model's ability to correctly identify about 85.7% of all actual positive cases. The F1 score, which balances precision and recall, is approximately 0.877, showing that the model performs well in terms of both accuracy and recall. The overall accuracy of the \begin{table}
\begin{tabular}{c c c} \hline \hline  & **MLP Network Previous Experiment** & **MLP Network Current Experiment** \\ \hline
**UML diagram categories** & class & class, sequence, state machine \\
**Feature dataset size** & 2000 & 1500 \\
**Class diagram correct classification** & 88\% & 92\% \\
**Average accuracy score** & 89\% & 90\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of current research against previous experiment. model is 0.8, meaning that 80% of all predictions made by the model, both positive and negative, were correct. However, the specificity is 0.5, indicating that the model correctly identified 50% of the actual negative cases. This suggests that while the model is effective at identifying positive cases, there is room for improvement in distinguishing negative cases. The validation of the classification model is demonstrated through the detailed results for various configurations of Single-Input Single-Output (SISO) and Multiple-Input Multiple-Output (MIMO) models. For the SISO 1 configuration, the model achieved a precision of 0.82, a recall of 0.73, and an F1 score of 0.77. The MIMO 1 configuration showed improved performance with a precision of 0.93, a recall of 0.75, and an F1 score of 0.83. Further enhancements were observed in the SISO 2 configuration, where both precision and recall were 0.89, leading to an F1 score of 0.89. The highest performance was recorded with the MIMO 2 configuration, which achieved a precision of 1.00, a recall of 0.85, and an F1 score of 0.91. The plots illustrate the accuracy and loss of a model during training and validation phases over 100 epochs. The left plot represents the accuracy, with the blue line indicating training accuracy and the red line indicating validation accuracy. The training accuracy starts at approximately 0.45 and steadily increases, reaching around 0.9 by the end of the training period. Similarly, the validation accuracy begins at around 0.45 and rises to about 0.85 by the final epoch. This steady improvement and convergence suggest that the model was learning effectively. The right plot shows the loss, where the blue line represents training loss and the red line represents validation loss. Initially, the training loss is high, around 2.0, but it decreases to below 0.5 by the end of the training period. The validation loss follows a similar pattern, starting high at around 2.0, experiencing a slight increase, and then decreasing to stabilize around 0.6. The decrease in both training and validation losses indicates successful training. The convergence of the training and validation accuracies, coupled with the decrease in losses, suggests that the model is not overfitting. The slight gap between training and validation performance is typical and expected in well-trained models. The model achieves high accuracy and low loss on both training and validation sets, aligning with the desired training outcomes, as highlighted in [30]. This paper discusses the ResNet architecture, which achieves high accuracy on image recognition tasks and includes an analysis of training and validation accuracy as well as loss curves. Throughout both plots, we observe occasional spikes, particularly noticeable in the validation loss. These spikes are common in the training of neural networks and can occur due to several reasons:  
Mini-batch Variability: During training, the model updates its parameters based on mini-batches of the training data. Some mini-batches may contain more challenging or noisy examples, causing temporary increases in loss.