## 4 Discussion  
These results illustrate that the MIMO models generally outperform the SISO models, particularly in terms of precision and F1 score. Notably, the MIMO 2 configuration's perfect precision and high F1 score indicate a robust balance between precision and recall, confirming the model's efficacy in accurately classifying UML diagrams. While the initial MLP network, focused solely on class diagram classification, achieved an F1 score of 0.92, it is important to note that the new MLP model, despite achieving a slightly lower F1 score of 0.91, is still highly effective. This is particularly commendable given the expanded set of categories now included in the classification task (see Table 6). The addition of state machine and sequence diagrams increased the complexity of the classification problem, yet the model has maintained a high level of performance. This underscores the robustness and adaptability of the new model in handling a more diverse set  
Figure 6: Example of sequence diagram used for testing. of UML diagrams, ensuring continued accuracy and reliability across various classification tasks within software engineering. By providing these detailed metrics, we ensure a comprehensive validation of the model's performance, addressing the gap in the initial report and substantiating the claims of improved accuracy and robustness. The model's ability to handle edge cases was rigorously tested using advanced tools such as ChatGPT 4 Pro version and GitHub Copilot 1.84, which previously struggled with correctly classifying these challenging scenarios, as displayed in Figures 7 and 8. The model excels in managing edge cases due to its well-prepared dataset, which includes advanced feature extraction layers and robust error-handling mechanisms. These features enable the model to capture nuanced patterns and subtle variations in data that are often missed by other models. Additionally, the model employs an extensive training dataset that includes a diverse range of examples, ensuring it is well-equipped to generalize and perform accurately across atypical and complex inputs. Despite these advancements, there are limitations that need to be addressed. The model occasionally fails to classify edge cases accurately, indicating potential gaps in the dataset. These failures suggest that the dataset should be revised to include more specific features or exclude irrelevant ones, ensuring a more comprehensive representation of all possible scenarios. To understand the performance of our feature-based neural network model, we compared it with existing approaches used for UML diagram classification. These approaches include both traditional machine learning models and deep learning techniques, as reported in previous studies. Our model achieved an average accuracy of 90% and an F1 score of 0.87, surpassing traditional models such as Support Vector Machines (SVM) and Random Forests, which typically report accuracies around 70-80% for UML diagram classification. Our model demonstrates superior performance on a wider range of UML diagram types, including class, sequence, and state machine diagrams, compared to [1], who used a CNN-based approach and achieved an accuracy of 88% on class diagrams. Additionally, our model achieves higher precision and recall across multiple diagram types in contrast to [2]. Unlike previous work that primarily focused on class diagrams, the new model effectively handles multiple UML types (class, sequence, state machine), enhancing its applicability in diverse educational and software engineering contexts. By breaking down UML diagrams into their feature components, the model can achieve greater precision in distinguishing between diagram elements. While the model exhibits strong performance, there are limitations that must be acknowledged. The model's performance relies on the quality and accuracy of the feature extraction process. Any errors in extracting features from UML diagrams could affect classification results.