# An Efficient Optimized DenseNet Model for Aspect-Based Multi-Label Classification
## 4 Simulation Results and Discussion
### Performance of Different Methods
By visualizing these variations, we gain a deeper understanding of how our proposed method responds to uncertain component changes, further strengthening our approach's reliability and effectiveness. The SA in Figure 15 serves as a vital component of our study, allowing us to assess the impact of uncertainties on our proposed method's performance and providing valuable insights for optimizing our approach. Furthermore, Figure 16 depicts the SA of the most recent benchmark methods. Through SA comparisons, it is evident that the EDAO variables exhibit the least variation among the baseline-related variables, surpassing benchmark algorithms. Figures 17-20 show the confusion matrix of DenseNet-AO and other existing models, which demonstrates the best performance among the models. Table 9 determines the limitations the EDAO approach addresses, highlighting its significant advancements in multi-label sentiment analysis. EDAO offers distinct advantages over existing methods through its integration of accuracy and diversity, the utilization of DenseNet-AO, improved generalization efficiency, robust decision-making, and efficient label space transformation. These features contribute to EDAO's effectiveness in capturing sentiment variations, enhancing performance, and handling complex multi-label datasets. Table 10 compares execution times in seconds for various techniques used in different domains. These values represent approximate execution times based on real-world scenarios. The DenseNet-AO technique stands out with notably lower execution times across all domains, ranging from 20 to 35. It indicates its efficient computational complexity, making it a promising choice for tasks in the news, emotion, medical, bird, hotel, automobile, movie, and protein domains. Figure 19: Confusion matrix of BERT. Figure 17: Confusion matrix of DenseNet-AO. Figure 20: Confusion matrix of CNN. On the other hand, the RAKEL technique demonstrates varying execution times, with some domains taking longer than others. For example, it requires around 120 s for emotions and automobiles. The ECC technique also exhibits variability in execution times, with the medical domain having the longest execution time of 180. The ML-RBF and CNN techniques generally show execution times in the 70 s to 120 s range, while the NB technique requires approximately 180s for the medical domain. BERT, LSTM, Transformer, GAT, ResNet, SVM, Logistic Regression, and random forest techniques also display varying execution times across domains from the 70 s to 200 s range. Table 11 presents the Pearson correlation coefficients between various techniques and domains. The coefficients serve as gauges that measure the intricate connection between two variables, spanning a scale from \(-1\) to 1. A coefficient that gravitates towards 1 signifies a robust positive correlation, while a coefficient approaching \(-1\) indicates a compelling negative correlation. Delving into the tabulated data, we discover that DenseNet-AO consistently showcases remarkable positive correlations across various domains. Its values gracefully range from 0.86 to 0.92, portraying unwavering strength and cohesion. It indicates a strong linear relationship between DenseNet-AO and the respective domains, implying that DenseNet-AO performs consistently well across different data types.