## 4 Results
### Testing the Custom Waste Sorting Model
Glass has eighty-nine true positives; one is misclassified as metal and nine are misclassified as plastic. Metal has ninety-six true positives; two are misclassified as glass and one is misclassified as plastic. Plastic shows ninety true positives; five are misclassified as cardboard, nine are misclassified as glass, and three are misclassified as metal. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Category** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F\({}_{\mathbf{1}}\)-Score (\%)** \\ \hline Cardboard & 98.75 & 95.00 & 100 & 97.44 \\ Glass & 94.75 & 89.00 & 89.90 & 89.45 \\ Metal & 98.25 & 96.00 & 96.97 & 96.48 \\ Plastic & 93.25 & 90.00 & 84.11 & 86.96 \\ \hline Total & 96.25 & 92.50 & 92.75 & 92.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance parameters for each category of the CWSM. Figure 14: CWSM screenshot of the execution. For the CWSM, we have imposed a 90% threshold of the _WCCS_ (the red horizontal line in Figure 16). Suppose that when the _WCCS_ level is higher than 90%, additional inspection is needed, such as in the cases in Figure 16. Thus, the thirty misclassified elements are represented, and it is seen that after applying the _WCCS_, only two elements will remain misclassified. Therefore, Figure 17 presents the confusion matrix obtained after applying the _WCCS_. Table 4 presents the performance parameters after applying the _WCCS_. The results in Table 4 show a significant improvement in the performance metrics for all four categories. Thus, the overall accuracy obtained for the CWSM after applying the WCSS for all four categories is 99.75%, with a precision of 99.50%, a recall of 99.50%, and an F\({}_{1}\)-Score of 99.50%. This is because when the CWSM does not have a sufficiently high confidence level in classifying an object, it relies on the user's final decision. In what follows, we detail the computation of the _WCCS_ for two scenarios. The first scenario presents when the CWSM correctly identifies the object waste category. We analyze cardboard objects, identified as cardboard, with the probabilities provided by GVAS (Figure 18). Because there are no multiple identifications for the same class, the mean probability \(\mu\) is 0.25. The variance is 0.043 as follows:  
\[Variance=\frac{(0.60-0.25)^{2}+(0.19-0.25)^{2}+(0.15-0.25)^{2}+(0.06-0.25)^{ 2}}{4}=0.043\]  
Using this variance value, the _WCCS_ is as follows:  
\[WCCS=\frac{0.60+0.19+0.15+0.06}{1+0.043}\times 100=0.959\times 100=95.9\%\]  
Figure 17: Confusion matrix for cardboard, glass, metal, and plastic waste using the CWSM after applying the _WCCS_. Figure 18: CWSM probability that the object belongs to each category in the first scenario. This value indicates the necessity of checking manually whether the CWSM made a valid identification. The second scenario corresponds to an incorrect evaluation. We evaluated a glass object that was identified as plastic (Figure 19). The mean probability is 0.25, and the variance is calculated at 0.03. The WCCS of 96.3% reflects high confidence in the dominant category (plastic, 55%), with some probability spread indicating uncertainty. Exceeding the value of 0.90 indicates particular attention to identification. It does not mean an incorrect classification. It rather highlights a degree of uncertainty regarding the way the class was identified. Mathematically, this threshold value indicates a boundary where the CWSM's confidence becomes less evenly distributed across categories, resulting in a higher concentration of probability in one or more specific classes. The threshold of 0.90 is derived to balance confidence with variability (measured as variance). A WCCS above this threshold suggests that the classifier has a dominant waste category. At the same time, it shows inconsistency in distributing probabilities across other potential waste categories. Exceeding the 90% value points out ambiguities in the feature space and data patterns that challenge the CWSM's decision-making process.