## 5 Discussion
The research demonstrates the superiority of using a model that is based on identifying common features of objects belonging to the same class but distinguishes them from other categories. The paper also demonstrates that the processing techniques implemented by pre-implemented AI models are not sufficient for use in real-time applications, as they cannot extrapolate for objects that have never been used before. Furthermore, the custom algorithms implemented must also contain mechanisms to prevent situations involving overlapping common features between classes, such as the case of glass-plastic. This research materialized these mechanisms through the _WCCS_ indicator, demonstrating the ability to identify these exceptional situations. The _WCCS_ indicator proposed in this study assesses the confidence in classifying an object. Unlike other model performance evaluation methods, the _WCCS_ penalizes model uncertainty. Compared to traditional performance evaluation methods, the _WCCS_ has the following advantages. Unlike precision and recall, the _WCCS_ incorporates probabilities for each category and normalizes the results. This approach provides a detailed view of performance per category, different from precision and recall, which do not offer insight into the model's uncertainty regarding specific categories. * The F\({}_{1}\)-Score is another traditional metric for evaluating model performance. Even if the model has a good F1 score, its classifications may still be uncertain. Unlike the F\({}_{1}\)-Score, if the model is indecisive between two categories, the _WCCS_ establishes an additional degree of confidence;
* The Brier score is another metric that measures how well a model's probabilities are calibrated. It does not differentiate between a slightly uncertain model and a completely wrong one. Unlike the Brier score, the _WCCS_ heavily penalizes uncertainty and over-confidence in incorrect predictions. Entropy is another tool for measuring a model's probability distribution. It does not normalize the results based on the dominant category. In contrast, the _WCCS_ introduces normalization based on category importance and probability variation. The _WCCS_, together with standard model evaluation metrics, provides an advanced means of assessment compared to traditional metrics. The paper analyses current reference technologies in the AI field through the Custom Vision services implemented by Azure Custom Vision and Google. The authors believe these two services offer features that cannot be replaced by custom development with a few programmers. Still, they have limited capabilities if not integrated into a customized solution, like the one proposed in this paper. The comparative analysis reveals the evident limitations of ACVS, which requires a large volume of training data. GVAS demonstrates superiority in the context of being integrated as part of the CWSM in the classification problem. The research limitations are given by the number of images used in training and testing. Even if we employed 1000 images for training and 400 images for testing from all four waste categories, it is impossible to test all types of waste (because they have particular shapes, colors, sizes, etc., even in the same waste category) that are produced worldwide and to acquire images from different angles, light types, brightness levels, and shadows. RBin's internet connection influences the processing speed of the CWSM since the model uses GVAS for object labeling at the image level. In the case of a slow connection, the classification process may experience delays. Still, these do not affect the practical use of the system, as user interaction with the waste bin does not require immediate real-time processing. System latency is not an issue because users do not dispose of waste at speeds exceeding a computing system's processing capacity. Even in high volumes of waste scenarios, the classification process is fast enough to meet user requirements.