# An Efficient Optimized DenseNet Model for Aspect-Based Multi-Label Classification
## 4 Simulation Results and Discussion
### Compared Models  
The suggested EDAO was put through its paces in a full comparison with the most commonly used MLL techniques to demonstrate its efficacy. In the ensemble multi-label learning (EnML) domain, two distinct scenarios, ML-NCL and ML-HASC, stand out. These scenarios optimize a single target to validate the provided objective function. By focusing on individual targets, they serve as valuable testbeds for refining and validating the optimization process within the EnML framework. All of these techniques are summarized in the following sections. * Proposed Ensembler: DenseNet is used as a DNN algorithm ensemble with AO and as an ensembler. The DenseNet makes use of its bunch of layers. * NB: The ML algorithm was utilized in the multi-labeling method. * CNN: The multi-label learning (MLL) algorithm is fundamentally rooted in the domain of NNs. * ML-RBF [22]: Additionally, the RBF NN algorithm-based MLL algorithm is the primary trainer utilized by the ENL system and is developed based on RBF NN. * RAKEL [26]: Another approach to multivalued learning. In this approach, a basic learner with a single label makes judgments based on a randomly selected subset of the encountered labels. This limited subset serves as the basis for decision-making, reflecting the nature of most basic learners in this context. * ECC [24] provides a detailed description of an ensemble approach for MLL based on using classifier chains. To transform EnML into a subproblem, we must first modify the unique goal into the one we used to create EnML. \[ \begin{split}& M\operatorname{L}-\operatorname{HSIC}^{\prime}=1/ \log(M\operatorname{L}-\operatorname{HSIC})\\ & M\operatorname{L}-\operatorname{NCL}^{\prime}=1-M\operatorname {L}-\operatorname{NCL}/(m\times L)\end{split} \] (2)  
Enhanced DenseNet with Aquila Optimizer (EDAO) is a revolutionary advancement in sentiment analysis that has unique characteristics that distinguish it from other approaches. The Aquila Optimizer, a customized optimization method created especially for adjusting DenseNet's parameters, is one of its main contributions. This new optimizer, which prioritizes effectiveness and efficiency, is essential to improving the convergence and general performance of the model. Moreover, EDAO overcomes the drawbacks of other sentiment analysis techniques, excelling especially in multi-label sentiment analysis. Through its optimization process, EDAO combines accuracy and diversity, in contrast to benchmark techniques that could just favor accuracy. This integrated approach is beneficial,allowing EDAO to perform better than approaches with a more limited accuracy-focused scope and to catch sentiment fluctuations with ease. More specifically, to identify minute emotion differences across various texts, EDAO carefully use DenseNet-AO, a DenseNet variation. By leveraging DenseNet-AO, this approach greatly improves the model's capacity to traverse the intricacies of multi-label datasets, hence addressing a prevalent sentiment analysis difficulty. DenseNet-AO integration is also essential for increasing generalization efficiency, which enables the model to easily adjust to new or unknown input. A sensitivity analysis is a crucial assessment indicator for the decision-making process in terms of robustness and stability, according to EDAO. This novel approach solves the issues with the robustness of current techniques and offers a thorough evaluation of the model's dependability in a range of scenarios. Furthermore, EDAO simplifies label space translation, reducing the computational burden involved in this procedure. Using the LP approach for label space translation improves the model's efficiency and scalability, enabling it to handle large datasets with ease. Essentially, Enhanced DenseNet with Aquila Optimizer is a ground-breaking sentiment analysis system that combines cutting-edge optimization techniques, DenseNet-AO's strength, and an all-encompassing strategy to overcome the drawbacks of conventional approaches.