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Adesanya, A, Misra, S, Maskeliunas, R and Damasevicius, R (2021) Prospects of ocean-based renewable energy for West Africa's sustainable energy future. Smart and Sustainable Built Environment, 10(1), 37-50.
Albeiro Alberto Aguilar, O and Saúl Tomás Salas, S (2021) Good practices of labor welfare and environmental protection in potato crops in Colombia: A way to contribute to the sustainable development of Colombian agriculture. Smart and Sustainable Built Environment, 10(1), 51-66.
Dash, A (2021) Determinants of EVs adoption: a study on green behavior of consumers. Smart and Sustainable Built Environment, 10(1), 125-37.
Naoui, M A, Lejdel, B, Ayad, M, Amamra, A and kazar, O (2021) Using a distributed deep learning algorithm for analyzing big data in smart cities. Smart and Sustainable Built Environment, 10(1), 90-105.
- Type: Journal Article
- Keywords: smart city; deep learning; internet of things; clustering; machine learning; decision making; energy consumption; smart cities; mathematical models; architecture; forecasting; Australia
- ISBN/ISSN:
- URL: http://dx.doi.org/10.1108/SASBE-04-2019-0040
- Abstract:
The purpose of this paper is to propose a distributed deep learning architecture for smart cities in big data systems. 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, and its distribution over the system. The deep learning layer is the model of our system. It is responsible for data analysis We apply our proposed architecture in a Smart environment and Smart energy. 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 Memory. This research needs the application of other deep learning models, such as convolution neuronal network and autoencoder Findings of the research will be helpful in Smart city architecture. It can provide a clear view into a Smart city, data storage, and data analysis. The Toluene forecasting in a Smart environment can help the decision-maker to ensure environmental safety. The Smart energy of our proposed model can give a clear prediction of power generation. The findings of this study are expected to contribute valuable information to decision-makers for a better understanding of the key to Smart city architecture. Its relation with data storage, processing, and data analysis.
Palencia, M, Mora, M and Lerma, T A (2021) Environment-friendly stimulus-sensitive polyurethanes based on cationic aminoglycosides for the controlled release of phytohormones. Smart and Sustainable Built Environment, 10(1), 1-17.
Qi, J K, Yang, J Y, Oliver Hoon Leh, L, Edwards, R and Jamalunlaili, A (2021) Thermal comfort prediction of air-conditioned and passively cooled engineering testing centres in a higher educational institution using CFD. Smart and Sustainable Built Environment, 10(1), 18-36.
Taleb, H M and Abumoeilak, L (2021) An assessment of different courtyard configurations in urban communities in the United Arab Emirates. Smart and Sustainable Built Environment, 10(1), 67-89.
Willar, D, Estrellita Varina Yanti, W, Daisy Debora Grace, P and Rudolf Estephanus Golioth, M (2021) Sustainable construction practices in the execution of infrastructure projects: The extent of implementation. Smart and Sustainable Built Environment, 10(1), 106-24.