## 3 Results
### Performance Evaluation
Learning Rate Adjustments: If the learning rate is adaptive or scheduled to change during training, sudden changes in the learning rate can cause temporary spikes in loss as the model adjusts to the new rate. Model Exploration: At certain points, the model might be exploring different regions of the parameter space, especially early in training. This exploration can lead to temporary instability in the loss and accuracy. Overfitting on Specific Batches: In some cases, the model might overfit to specific mini-batches during training, leading to sudden decreases in validation accuracy or increases in validation loss when evaluated on the entire validation set. These spikes, while they can appear alarming, are typically part of the normal training dynamics. As long as the overall trend in the loss is downward and the accuracy is upward, the training process is generally progressing well. The model depicted in the provided plots appears to be performing well, with high and converging accuracies and low and decreasing losses. This indicates effective learning without significant overfitting, aligning with the performance characteristics discussed in the referenced research paper. The image in Figure 4 was classified as a UML diagram with a 92% accuracy score and as a class diagram with a 93% accuracy score. The model's capabilities have been further enhanced to include the classification of state machine diagrams, which are used to depict the states of an object and the transitions between these states in response to events. By employing refined feature extraction techniques, the model identifies critical components such as states, transitions, events, and actions within state machine diagrams. This enables the model to accurately distinguish state machine diagrams from other types of UML diagrams. The image in Figure 5 has been classified as a UML diagram with a 91% accuracy score and as a state machine diagram with a 93% accuracy score. The enhanced model extends its classification capabilities to sequence diagrams, which represent the interactions among objects in a specific sequence of time. Utilizing advanced feature extraction techniques, the model captures the unique temporal and structural characteristics inherent in sequence diagrams. This includes identifying key elements such as lifelines, messages, and activation bars. By leveraging these features, the model effectively differentiates between sequence diagrams and other UML diagrams. In comparative testing, the model demonstrated higher accuracy in sequence diagram classification than for previously utilized models. This improvement is attributed to the integration of a more sophisticated neural network architecture and enhanced training methodologies, including comprehensive data augmentation and robust feature selection. Figure 4: Example of class diagram used for testing. Figure 5: Example of state machine diagram used for testing. The current model's accuracy surpasses that of earlier versions, confirming its capability in handling complex diagram classifications. The image in Figure 6 has been classified with a 92% accuracy score as a UML diagram and with a 95% accuracy score as a sequence diagram. Manual testing of the model further validated its accuracy and robustness, as it consistently achieved correct classifications in most cases. Human testers meticulously reviewed and assessed the model's predictions against a diverse set of data, including standard and edge cases. This comprehensive evaluation ensured that the model's performance was reliable and consistent. The manual testing process provided an additional layer of assurance, highlighting the model's capability to generalize well across various inputs and to maintain high classification accuracy, further reinforcing its superiority over previous models and automated tools. The current model achieves higher classification accuracy than previous models in the same research by leveraging several advanced techniques.