Real-Time Traffic Management Using Feature Selection and Deep Learning in Vehicular Fog Computing
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-67447-1_26
Reference14 articles.
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3. Usmonov, S., Pradeep, A., Fakhriddinov, Z., Sanjar, T., Abdurakhim, A., Khusniddinova, M.: Intelligent traffic management system: AI-enabled IoT traffic lights to mitigate accidents and minimize environmental pollution. In: 2023 3rd International Conference on Intelligent Technologies (CONIT), pp. 1–6. IEEE (2023)
4. Tiwari, R., Rumaney, A. H., Saravanan, M.: (2023, May). Real-time traffic surveillance and detection using deep learning and computer vision techniques. In: 2023 2nd International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies (ViTECoN), pp. 1–6. IEEE (May 2023)
5. Traspadini, A., Giordani, M., Giambene, G., Zorzi, M.: Real-Time HAP-assisted vehicular edge computing for rural areas. IEEE Wireless Commun. Lett. 12(4), 674–678 (2023)
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