What is it about?

We have proposed an architectural multilayer to describe the distributed deep learning for smart cities in big data systems. The components of our system are Smart city layer, big data layer, and deep learning layer. The Smart city layer responsible for the question of Smart city components, its Internet of things, sensors and effectors, and its integration in the system, big data layer concerns data characteristics 10, and its distribution over the system. The deep learning layer is the model of our system. It is responsible for data analysis.

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Why is it important?

We apply our proposed architecture in a Smart environment and Smart energy. 10; In a Smart environment, we study the Toluene forecasting in Madrid Smart city. For Smart energy, we study wind energy foresting in Australia. Our proposed architecture can reduce the time of execution and improve the deep learning model, such as Long Term Short Memory10;.

Perspectives

Based on the results of this study, as part of future work, an alternative deep learning algorithm, such as the convolution neural network and the auto-encoder, will be applied.

Dr Mouloud AYAD

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This page is a summary of: Using a distributed deep learning algorithm for analyzing big data in smart cities, Smart and Sustainable Built Environment, April 2020, Emerald,
DOI: 10.1108/sasbe-04-2019-0040.
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