Jingzhi Tu

11 papers Journal 11
YearRankTypeTitle / Venue / Authors
2023 J jnl
A Deep Learning Approach for Long-Term Traffic Flow Prediction With Multifactor Fusion Using Spatiotemporal Graph Convolutional Network.
IEEE Trans. Intell. Transp. Syst.
Xiaoyu Qi, Gang Mei, Jingzhi Tu, Ning Xi, Francesco Piccialli
2023 J jnl
Physics-Informed Neural Network Integrating PointNet-Based Adaptive Refinement for Investigating Crack Propagation in Industrial Applications.
IEEE Trans. Ind. Informatics
Jingzhi Tu, Chun Liu, Pian Qi
2022 J jnl
An Efficient Deep Learning Approach Using Improved Generative Adversarial Networks for Incomplete Information Completion of Self-driving Vehicles.
J. Grid Comput.
Jingzhi Tu, Gang Mei, Francesco Piccialli
2022 J jnl
An improved Nyström spectral graph clustering using
J. King Saud Univ. Comput. Inf. Sci.
Jingzhi Tu, Gang Mei, Francesco Piccialli
2022 J jnl
SWCGAN: Generative Adversarial Network Combining Swin Transformer and CNN for Remote Sensing Image Super-Resolution.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens.
Jingzhi Tu, Gang Mei, Zhengjing Ma, Francesco Piccialli
2021 J jnl
An Efficient Deep Learning Approach Using Improved Generative Adversarial Networks for Incomplete Information Completion of Self-driving.
CoRR
Jingzhi Tu, Gang Mei, Francesco Piccialli
2021 J jnl
An efficient graph clustering algorithm by exploiting
Comput. Electr. Eng.
Gang Mei, Jingzhi Tu, Lei Xiao, Francesco Piccialli
2020 J jnl
Comparative investigation of parallel spatial interpolation algorithms for building large-scale digital elevation models.
PeerJ Comput. Sci.
Jingzhi Tu, Guoxiang Yang, Pian Qi, Zengyu Ding, Gang Mei
2020 J jnl
Julia Language in Machine Learning: Algorithms, Applications, and Open Issues.
CoRR
Kaifeng Gao, Jingzhi Tu, Zenan Huo, Gang Mei, Francesco Piccialli, Salvatore Cuomo
2020 J jnl
Julia language in machine learning: Algorithms, applications, and open issues.
Comput. Sci. Rev.
Kaifeng Gao, Gang Mei, Francesco Piccialli, Salvatore Cuomo, Jingzhi Tu, Zenan Huo
2020 J jnl
KCoreMotif: An Efficient Graph Clustering Algorithm for Large Networks by Exploiting k-core Decomposition and Motifs.
CoRR
Gang Mei, Jingzhi Tu, Lei Xiao, Francesco Piccialli