Formula-E race strategy development using artificial neural networks and Monte Carlo tree search. - Xuze Liu; Abbas Fotouhi (Neural Comput. Appl., 2020)

Formula-E race strategy development using distributed policy gradient reinforcement learning. - Xuze Liu; Abbas Fotouhi; Daniel J. Auger (Knowl. Based Syst., 2021)

Application of advanced tree search and proximal policy optimization on formula-E race strategy development. - Xuze Liu; Abbas Fotouhi; Daniel J. Auger (Expert Syst. Appl., 2022)

Nash double Q-based multi-agent deep reinforcement learning for interactive merging strategy in mixed traffic. - Lin Li 056; Wanzhong Zhao; Chunyan Wang 013; Abbas Fotouhi; Xuze Liu (Expert Syst. Appl., 2024)

Formula-E Multi-Car Race Strategy Development - A Novel Approach Using Reinforcement Learning. - Xuze Liu; Abbas Fotouhi; Daniel J. Auger (IEEE Trans. Intell. Transp. Syst., 2024)

Optimal Control of Race Car With Aerodynamic Slipstreaming Effect. - Xuze Liu; Abbas Fotouhi; Marco Cecotti; Daniel J. Auger (IEEE Trans. Control. Syst. Technol., 2024)

A ranked filter-based three-way clustering strategy for intrusion detection in highly secure IoT networks. - Fazal Wahab; Shengjun Ma; Xuze Liu; Yuhai Zhao; Anwar Shah; Bahar Ali (Comput. Electr. Eng., 2025)

Dual-granularity multi-instance multi-label learning with variational autoencoder. - Meixia Wang; Yuhai Zhao; Yejiang Wang; Miaomiao Huang; Xuze Liu; Xingwei Wang 001 (Knowl. Based Syst., 2025)