1/7/2023 0 Comments Smart trash![]() This issue is not only related to getting disposed of waste in proper places but also reducing waste disposal volume. Waste management issue is a relevant topic that had been focused on many persuasive technologies. The experimental results indicate that DNN-TC yields 94% and 98% in terms of accuracy for Trashnet and VN-trash datasets respectively and thus it outperforms the state-of-the-art methods for trash classification on both experimental datasets. Finally, the experiments are conducted to compare the performances of DNN-TC and the state-of-the-art methods for trash classification on VN-trash dataset as well as Trashnet dataset to show the effectiveness of the proposed model. Next, this study develops a deep neural network model for trash classification named DNN-TC which is an improvement of ResNext model to improve the predictive performance. Firstly, we collect the VN-trash dataset that consists of 5904 images belonging to three different classes including Organic, Inorganic and Medical wastes from Vietnam. 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.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |