Mall, P. K., Narayan, V., Pramanik, S., Srivastava, S., Faiz, M., Sriramulu, S., & Kumar, M. N. (2023). FuzzyNet-Based Modelling Smart Traffic System in Smart Cities Using Deep Learning Models. In Handbook of Research on Data-Driven Mathematical Modeling in Smart Cities (pp. 76-95). IGI Global.
Hameed, A., Violos, J., & Leivadeas, A. (2022). A deep learning approach for IoT traffic multi-classification in a smart-city scenario. IEEE Access, 10, 21193-21210.
Ramana, K., Srivastava, G., Kumar, M. R., Gadekallu, T. R., Lin, J. C. W., Alazab, M., & Iwendi, C. (2023). A Vision Transformer Approach for Traffic Congestion Prediction in Urban Areas. IEEE Transactions on Intelligent Transportation Systems.
Khan, N. U., Shah, M. A., Maple, C., Ahmed, E., & Asghar, N. (2022). Traffic flow prediction: an intelligent scheme for forecasting traffic flow using air pollution data in smart cities with bagging ensemble. Sustainability, 14(7), 4164.
Zhou, S., Wei, C., Song, C., Pan, X., Chang, W., & Yang, L. (2022). Short-term traffic flow prediction of the smart city using 5G internet of vehicles based on edge computing. IEEE Transactions on Intelligent Transportation Systems.
Razali, N. A. M., Shamsaimon, N., Ishak, K. K., Ramli, S., Amran, M. F. M., & Sukardi, S. (2021). Gap, techniques and evaluation: traffic flow prediction using machine learning and deep learning. Journal of Big Data, 8(1), 1-25.
Navarro-Espinoza, A., López-Bonilla, O. R., García-Guerrero, E. E., Tlelo-Cuautle, E., López-Mancilla, D., Hernández-Mejía, C., & Inzunza-González, E. (2022). Traffic flow prediction for smart traffic lights using machine learning algorithms. Technologies, 10(1), 5.
Djenouri, Y., Belhadi, A., Srivastava, G., & Lin, J. C. W. (2023). Hybrid graph convolution neural network and branch-and-bound optimization for traffic flow forecasting. Future Generation Computer Systems, 139, 100-108.
Saleem, M., Abbas, S., Ghazal, T. M., Khan, M. A., Sahawneh, N., & Ahmad, M. (2022). Smart cities: Fusion-based intelligent traffic congestion control system for vehicular networks using machine learning techniques. Egyptian Informatics Journal, 23(3), 417-426.
Hassan, M., Kanwal, A., Jarrah, M., Pradhan, M., Hussain, A., & Mago, B. (2022, February). Smart City Intelligent Traffic Control for Connected Road Junction Congestion Awareness with Deep Extreme Learning Machine. In 2022 International Conference on Business Analytics for Technology and Security (ICBATS) (pp. 1-4). IEEE.
Vijayalakshmi, B., Ramar, K., Jhanjhi, N. Z., Verma, S., Kaliappan, M., Vijayalakshmi, K., ... & Ghosh, U. (2021). An attention-based deep learning model for traffic flow prediction using spatiotemporal features towards sustainable smart city. International Journal of Communication Systems, 34(3), e4609.
Joseph, L. L., Goel, P., Jain, A., Rajyalakshmi, K., Gulati, K., & Singh, P. (2021, October). A novel hybrid deep learning algorithm for smart city traffic congestion predictions. In 2021 6th International Conference on Signal Processing, Computing and Control (ISPCC) (pp. 561-565). IEEE
Awan, F. M., Minerva, R., & Crespi, N. (2021). Using noise pollution data for traffic prediction in smart cities: experiments based on LSTM recurrent neural networks. IEEE Sensors Journal, 21(18), 20722-20729.
Qaisar, S. M., Khan, S. I., Srinivasan, K., & Krichen, M. (2023). Arrhythmia classification using multirate processing metaheuristic optimization and variational mode decomposition. Journal of King Saud University-Computer and Information Sciences, 35(1), 26-37.
Henry, A., Gautam, S., Khanna, S., Rabie, K., Shongwe, T., Bhattacharya, P., Sharma, B., & Chowdhury, S. (2023). Composition of Hybrid Deep Learning Model and Feature Optimization for Intrusion Detection System. Sensors, 23(2), 890.