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Zhao, Zhongliang; Emami, Negar; Santos, Hugo; Pacheco, Lucas; Karimzadeh, Mostafa; Braun, Torsten; Braud, Arnaud; Radier, Benoit; Tamagnan, Philippe (2022). Reinforced-LSTM Trajectory Prediction-driven Dynamic Service Migration: A Case Study. Transactions on Network Science and Engineering, 9(4), pp. 2786-2802. IEEE 10.1109/TNSE.2022.3169786
Karimzadeh, Mostafa; Esposito, Alessandro; Zhao, Zhongliang; Braun, Torsten; Sargento, Susana (28 June 2021). RL-CNN: Reinforcement Learning-designed Convolutional Neural Network for Urban Traffic Flow Estimation. In: 17th International Wireless Communications & Mobile Computing Conference - IWCMC 2021 (pp. 29-34). IEEE 10.1109/IWCMC51323.2021.9498948
Karimzadeh, Mostafa; Schwegler, Samuel Martin; Zhao, Zhongliang; Braun, Torsten; Sargento, Susana (28 June 2021). MTL-LSTM: Multi-Task Learning-based LSTM for Urban Traffic Flow Forecasting. In: 17th International Wireless Communications & Mobile Computing Conference - IWCMC 2021 (pp. 564-569). IEEE 10.1109/IWCMC51323.2021.9498905
Karimzadeh, Mostafa; Aebi, Ryan; M. de Souza, Allan; Zhao, Zhongliang; Braun, Torsten; Sargento, Susana; Villas, Leandro (5 May 2021). Reinforcement Learning-designed LSTM for Trajectory and Traffic Flow Prediction. In: IEEE Wireless Communications and Networking Conference (pp. 1-6). IEEE 10.1109/WCNC49053.2021.9417511
Zhao, Zhongliang; Karimzadeh, Mostafa; Pacheco, Lucas; Santos, Hugo; Rosário, Denis; Braun, Torsten; Cerqueira, Eduardo (29 October 2020). Mobility Management with Transferable Reinforcement Learning Trajectory Prediction. IEEE Transactions on Network and Service Management, 17(4), pp. 2102-2116. IEEE 10.1109/TNSM.2020.3034482
Karimzadeh, Mostafa; Gerber, Florian; Zhao, Zhongliang; Braun, Torsten (18 March 2019). Pedestrians Trajectory Prediction in Urban Environments. In: International Conference on Networked Systems (NetSys). München. 18. - 21.03.2019. 10.1109/NetSys.2019.8854506
Karimzadeh, Mostafa (2020). Prediction Models to Enhance Location Based Services in Urban Areas. (Dissertation, Institute for computer science, Faculty of Science)
