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• W4297337265 abstract "Modern research in the field of classification, detection and semantic segmentation focuses on the use of recurrent neural networks as the basis for their approaches. Therefore, a deep understanding of the mechanisms of functioning of such deep models is essential for discovering new architectures of neural networks. This paper proposes a smart city control system architecture based on deep convolutional neural networks. The control system has a multilayer architecture that combines loosely coupled intelligent components. As the main layer, a solution based on deep learning technology is applied, which allows solving several tasks simultaneously: segmentation, detection and classification of images received from surveillance cameras of the smart city system. The data obtained at the output of this layer is used for further analysis and decision-making in the smart city system. The proposed architecture has a high degree of modularity and allows the replacement of individual elements in a loosely coupled architecture. In this paper, deep learning and computer vision technologies are also considered, on the basis of which the image processing layer from video cameras is implemented. A masked recurrent neural network is used for this task." @default.
• W4297337265 created "2022-09-28" @default.
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• W4297337265 date "2022-09-04" @default.
• W4297337265 modified "2023-10-11" @default.
• W4297337265 title "Smart City Management System Based on Multi-purpose Deep Neural Network" @default.
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• W4297337265 doi "https://doi.org/10.1109/rusautocon54946.2022.9896282" @default.
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