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Research Article | Open Access |

Machine Learning for Smart Cities: A Comprehensive Review of Applications and Opportunities

Author 1: Xiaoning Dou Author 2: Weijing Chen Author 3: Lei Zhu Author 4: Yingmei Bai Author 5: Yan Li Author 6: Xiaoxiao Wu
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 14, No. 9 · Published 2023 · Cited by 12

DOI: https://doi.org/10.14569/IJACSA.2023.01409104

Abstract

The smart city concept originated a few years ago as a combination of ideas about how information and communication technologies can improve urban life. With the advent of the digital revolution, many cities globally are investing heavily in designing and implementing smart city solutions and projects. Machine Learning (ML) has evolved into a powerful tool within the smart city sector, enabling efficient resource management, improved infrastructure, and enhanced urban services. This paper discusses the diverse ML algorithms and their potential applications in smart cities, including Artificial Intelligence (AI) and Intelligent Transportation Systems (ITS). The key challenges, opportunities, and directions for adopting ML to make cities smarter and more sustainable are outlined.

Keywords

How to Cite this Article

Dou, X., Chen, W., Zhu, L., Bai, Y., Li, Y., & Wu, X. (2023). Machine Learning for Smart Cities: A Comprehensive Review of Applications and Opportunities. International Journal of Advanced Computer Science and Applications, 14(9). https://doi.org/10.14569/IJACSA.2023.01409104

Dou, Xiaoning, et al.. "Machine Learning for Smart Cities: A Comprehensive Review of Applications and Opportunities." International Journal of Advanced Computer Science and Applications, vol. 14, no. 9, 2023, https://doi.org/10.14569/IJACSA.2023.01409104.

@article{Dou2023,
  title     = {Machine Learning for Smart Cities: A Comprehensive Review of Applications and Opportunities},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {9},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Xiaoning Dou and Weijing Chen and Lei Zhu and Yingmei Bai and Yan Li and Xiaoxiao Wu},
  doi       = {10.14569/IJACSA.2023.01409104},
  url       = {https://doi.org/10.14569/IJACSA.2023.01409104}
}

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