| Title |
Smart city urban planning using an evolutionary deep learning model |
| ID_Doc |
40558 |
| Authors |
Alghamdi, M |
| Title |
Smart city urban planning using an evolutionary deep learning model |
| Year |
2024 |
| Published |
Soft Computing, 28, 1 |
| DOI |
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. |
| Author Keywords |
Deep learning; Multi-objective optimization; RNN; Smart cities; TLBO; Urban planification |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000971522600001 |
| WoS Category |
Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications |
| Research Area |
Computer Science |
| PDF |
|