## 6 Conclusions
The general conclusions of the article focus on the inability of AI models to extract distinctive features when a large number of characteristics are involved based on images so that they can make extrapolations that allow them to identify objects they have never seen before. Customized models are necessary to perform such tasks, as demonstrated by the research presented in this article. Equally, choosing an optimal number of data that does not lead to underfitting or overfitting is an experimental matter that falls within the responsibility of the AI engineer. Additionally, using a customized indicator specific to the problem ensures the identification of cases not recognized by the service. The article demonstrates, through the prototype created at low costs and the uniqueness of the proposed CWSM, the superiority of customized models over pre-implemented servicesdeveloped by companies such as Google LLC, Mountain View, CA, USA or Microsoft Corporation, Redmond, WA, USA. Future research should integrate spectrometry sensors into RBin, which, in combination with the algorithm proposed in this paper, aim to achieve 100% accuracy in waste classification. New categories, such as food waste collection for compost production, are also being considered. The authors propose to investigate the possibility of integrating infrared spectroscopy to distinguish between different plastic, cardboard, and metal types, even when the objects are deformed or damaged. Additionally, X-ray fluorescence is another idea the authors wish to explore as a research direction for the rapid detection of metals. Integrating these sensors into RBin would represent a classification process that would eliminate confusion between visually similar materials. Moreover, developing a multimodal classification system is an important research direction the authors propose for improving RBin. This direction would enable a smart system capable of detecting the physical properties of waste (density, weight, recyclability) through IoT sensors. Furthermore, using ML technologies on edge devices (e.g., Raspberry Pi + AI accelerators) to classify waste without requiring a cloud connection would represent an evolutionary step for RBin, while transmitting data to a centralized waste management platform would allow for generating statistics about the types of collected waste and providing recommendations for improving recycling. This multimodal approach would enable better management in the recycling process and represents another research direction for the authors. Conceptualization, C.-M.R. ; methodology, C.-M.R. and A.S.; software, C.-M.R. ; validation, C.-M.R. and A.S.; formal analysis, C.-M.R. and A.S.; investigation, A.S. and M.R.T. ; resources, A.S. and M.R.T. ; data curation, C.-M.R. ; writing--original draft preparation, A.S. and M.R.T. ; writing--review and editing, A.S. and M.R.T. ; visualization, C.-M.R., A.S. and M.R.T. ; supervision, C.-M.R. ; funding acquisition, A.S. All authors have read and agreed to the published version of the manuscript. This research was funded by the Petroleum-Gas University of Ploiesti, Romania. Not applicable. The raw data supporting the conclusions of this article will be made available by the authors upon request. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} ACVS & Microsoft Azure Custom Vision Service \\ AI & Artificial intelligence \\ ANN & Artificial neural network \\ ANOVA & Analysis of variance \\ AP & Average precision \\ CAM & Class Activation Mapping \\ CNN & Convolutional neural network \\ CV & Computer vision \\ CWSM & Custom Waste Sorting Model \\ DBI & Davies-Bouldin Index \\ _dfB_ & Between-Groups Degrees of Freedom \\ _dfW_ & Within-Groups Degrees of Freedom \\ DL & Deep learning \\ \end{tabular}