# An Efficient Optimized DenseNet Model for Aspect-Based Multi-Label Classification
## 4 Simulation Results and Discussion
### Performance of Different Methods  
For each experimental data collection, tenfold cross-validation is conducted. Using the provided outputs, the selected technique's standard deviation and average productivity are computed for the entire dataset. The experiments were carried out utilizing a device powered by a robust 2.24 GHz Intel Xeon processor and bolstered by a generous 16 GB of RAM. Tables 6-8 show the productivity of six different approaches. Based on all metrics and datasets, the proposed model beats the other methods evaluated, including the ML-RBF process and two combination approaches, RAKEL and ECC. The technique described remains constant across all datasets. It has been demonstrated that, compared to non-ensemble methods, our ensembler EDAO can effectively improve the generalization efficiency of DNN. EDAO's advantages over other multi-component learning techniques, such as ECC and NB, highlight our concept's effectiveness. By harnessing the potential of integrated learners in conjunction with machine learning, MLL undergoes a significant improvement in generalization. Within this proposed setting, DenseNet excels, driven by its unmatched ability to address diversity-related objectives that have long challenged the field of multi-label research. Through the ingenious use of the AO algorithm, DenseNet transcends conventional boundaries, leading to a movement of diversification across the MLL domain. This distinctive attribute is a fundamental pillar behind DenseNet's remarkable ascent. In our proposed approach, we examine the impact of objective functions on outcomes by comparing EDAO with CNN and Naive Bayes, representing two types of objective functions. The technique consistently yields the best results across all datasets, as evidenced in Tables 6-8. Visualizations of the model loss and suggested model accuracy are presented in Figures 6 and 7. In our conceptual model, we observe a compelling trend: As the number of epochs increases, our model's accuracy demonstrates significant improvement while the loss steadily decreases. This pattern reinforces the effectiveness of our approach, highlighting the positive correlation between training epochs and enhanced model performance. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Datasets (\%)**} \\ \cline{2-10}
**Techniques** & **P** & **P** & **P** & **P** & **P** & **P** & **P** & **P** & **P** \\ \hline DenseNet-AO & 92.27 & 94.22 & 90.23 & 92.24 & 94.66 & 94.23 & 93.84 & 90.77 \\ NB & 80.9 & 72.34 & 80.53 & 61.34 & 62.34 & 67.34 & 82.09 & 58.34 \\ ECC & 72.12 & 72.8 & 67.67 & 70.88 & 70.03 & 73.11 & 70.78 & 73.69 \\ RAKEL & 77.12 & 77.8 & 72.67 & 75.88 & 75.03 & 78.11 & 75.78 & 78.69 \\ CNN & 85.27 & 88.02 & 86.23 & 84.56 & 85.74 & 86.11 & 86.84 & 81.57 \\ ML-RBF & 69.12 & 69.8 & 64.67 & 67.88 & 67.03 & 70.11 & 67.78 & 70.69 \\ BERT & 88.67 & 79.56 & 84.78 & 87.45 & 88.21 & 89.56 & 90.12 & 85.32 \\ LSTM & 79.23 & 74.32 & 76.45 & 78.32 & 78.45 & 79.67 & 81.56 & 77.89 \\ Transformer & 87.54 & 81.45 & 79.56 & 76.45 & 77.89 & 79.34 & 82.67 & 75.67 \\ GAT & 81.32 & 78.23 & 75.32 & 72.56 & 73.21 & 75.78 & 80.23 & 72.34 \\ ResNet & 84.21 & 79.87 & 78.21 & 75.32 & 76.45 & 78.56 & 81.34 & 75.89 \\ SVM & 78.95 & 75.34 & 72.45 & 70.34 & 71.56 & 73.89 & 77.56 & 70.67 \\ Logistic Regression & 83.67 & 77.89 & 75.67 & 73.21 & 74.32 & 76.45 & 80.45 & 74.21 \\ Random Forest & 86.32 & 80.56 & 77.32 & 76.56 & 77.45 & 79.23 & 83.21 & 77.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Benchmark scheme vs EDAO (precision).