## 4 Results
### Testing the Custom Waste Sorting Model  
The CWSM is a hybrid model incorporating a first component for object description and a second component including a classification model based on the previous description. While the first component is an automated process performed by GVAS, an external service, the second component is a local service performed by the Microsoft MLNET tool. The second component is locally trained and evaluated using five algorithms. Figure 13 presents the accuracy for all evaluated algorithms through multiple iterations. Thus, the superiority of LbfgsMaximumEntropyMulti is visible in Figure 13. This algorithm calculated an accuracy of 92.79% for the classification, indicating that the model predicts most of the analyzed categories. The LbfgsMaximumEntropy algorithm represents a variant of multiclass logistic regression based on Maximum Entropy. This method is optimized using the Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm. It is suitable for text classification because it maximizes the probability of an object belonging to one of the defined classes: plastic, glass, metal, and paper. LbfgsMaximumEntropy provided the best results for text classification because this algorithm excels in solving multiclass problems. Unlike standard algorithms, such as the SVM, which is more suitable for binary classification and requires a one-versus-one approach, LbfgsMaximumEntropy uses reduced complexity, facilitating integration into real-time decision making. Random forest, one of the most analyzed algorithms in the specialized literature, is not ideal for textual data because it works better with discrete features than with continuous representations, such as text vectors. LbfgsMaximumEntropy maximizes the probability of belonging to a class, converges quickly due to L-BFGS, and automatically adjusts through L1/L2 regularizations, preventing overfitting. Therefore, LbfgsMaximumEntropy does not have the architecture of a classical neural network with layers. However, it uses specific hyperparameters for training, whose values are set as follows:  
* The L1 regression parameter (Lasso) is set to 5.20%. This controls the sparsity of the model, forcing some coefficients to be 0 to reduce model complexity;
* The L2 regression parameter (Ridge) is set to 18.75%. This parameter adds an extra penalty for large coefficients, preventing overfitting. Figure 13: The accuracy evolution for the analyzed classification algorithms through multiple training iterations. Since the LbfgsMaximumEntropy algorithm uses L-BFGS optimization, which automatically adjusts learning steps for fast convergence, it does not require the specific parameters of a neural network. The CWSM is examined using the same unseen object for the training dataset. The script execution using the ML.NET tool, C# programming language, and Visual Studio development environment is presented in Figure 14. The results are identified as follows (Figure 14):  
* Cardboard recognition achieves accuracy at 98.75%. This result reflects that the CWSM can recognize cardboard objects. This performance suggests that the defining characteristics of the cardboard class are well represented in the training data;
* The glass category has an accuracy of 94.75%. The CWSM performs reasonably well, but there is room for improvement in handling more diverse or challenging glass-related features. This score reflects the confusion with the plastic category;
* Metal is identified with a 98.25% accuracy rate, showcasing strong performance in identifying metallic objects;
* Plastic has approximately the same accuracy as the glass category. The accuracy for plastic stands at 93.25%, indicating poor performance. Table 3 presents the performance parameters for each waste category of the CWSM before applying the _WCCS_. The CWSM's total accuracy is approximately 96.25%, which indicates its high performance. The confusion matrix presented in Figure 15 shows that cardboard has ninety-five true positives, and zero are misclassified.