Therefore, this study proposes a robust model using deep neural networks to classify trash automatically which can be applied in smart waste sorter machines. To build this system, trash classification from trash images is an important issue in computer vision to be addressed for integrating into sensors. Nowadays, society is growing and crowded, the construction of automatic smart waste sorter machine utilizing the intelligent sensors is important and necessary. Although this study has a practical purpose, it is also necessary to promote new policies focused on incentives for local initiatives to support and complement them due to the new decentralized and anthropocentric approach to smart sustainable cities.
The results are the essential elements founded and synthesized in a single visual scheme. Through a literature review, six widely cited and commonly used groups of indicators are selected, and the most frequent themes, indicators, and keywords are identified. This study strives to identify and synthesize essential information, helping managers to define and develop projects and initiatives within the context of smart cities.
Faced with a wide range of information, the coexistence of multiple definitions, and differences between the theoretical concept and what is being carried out in the real world, it is recognized that entrepreneurs and public managers require more clarity regarding the essential attributes that need to be considered in the initiatives of a city that aims to be classified as smart. This study starts by questioning what smart cities are and how they are being planned for the future of the population.
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