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Smart city article details

Title	Smart City Urban Planning Using An Evolutionary Deep Learning Model
ID_Doc	50577
Authors	Alghamdi M.
Year	2024
Published	Soft Computing, 28, 1
DOI	http://dx.doi.org/10.1007/s00500-023-08219-4
Abstract	Following the evolution of big data collection, storage, and manipulation techniques, deep learning has drawn the attention of numerous recent studies proposing solutions for smart cities. These solutions were focusing especially on energy consumption, pollution levels, public services, and traffic management issues. Predicting urban evolution and planning is another recent concern for smart cities. In this context, this paper introduces a hybrid model that incorporates evolutionary optimization algorithms, such as Teaching–learning-based optimization (TLBO), into the functioning process of neural deep learning models, such as recurrent neural network (RNN) networks. According to the achieved simulations, deep learning enhanced by evolutionary optimizers can be an effective and promising method for predicting urban evolution of future smart cities. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Author Keywords	Deep learning; Multi-objective optimization; RNN; Smart cities; TLBO; Urban planification

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