KCoreMotif: An Efficient Graph Clustering Algorithm for Large Networks by Exploiting k-core Decomposition and Motifs. - Gang Mei; Jingzhi Tu; Lei Xiao; Francesco Piccialli (CoRR, 2020)

Julia language in machine learning: Algorithms, applications, and open issues. - Kaifeng Gao; Gang Mei; Francesco Piccialli; Salvatore Cuomo; Jingzhi Tu; Zenan Huo (Comput. Sci. Rev., 2020)

Comparative investigation of parallel spatial interpolation algorithms for building large-scale digital elevation models. - Jingzhi Tu; Guoxiang Yang; Pian Qi; Zengyu Ding; Gang Mei (PeerJ Comput. Sci., 2020)

An efficient graph clustering algorithm by exploiting k-core decomposition and motifs. - Gang Mei; Jingzhi Tu; Lei Xiao; Francesco Piccialli (Comput. Electr. Eng., 2021)

An Efficient Deep Learning Approach Using Improved Generative Adversarial Networks for Incomplete Information Completion of Self-driving. - Jingzhi Tu; Gang Mei; Francesco Piccialli (CoRR, 2021)

An Efficient Deep Learning Approach Using Improved Generative Adversarial Networks for Incomplete Information Completion of Self-driving Vehicles. - Jingzhi Tu; Gang Mei; Francesco Piccialli (J. Grid Comput., 2022)

An improved Nyström spectral graph clustering using k-core decomposition as a sampling strategy for large networks. - Jingzhi Tu; Gang Mei; Francesco Piccialli (J. King Saud Univ. Comput. Inf. Sci., 2022)

SWCGAN: Generative Adversarial Network Combining Swin Transformer and CNN for Remote Sensing Image Super-Resolution. - Jingzhi Tu; Gang Mei; Zhengjing Ma; Francesco Piccialli (IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2022)

Physics-Informed Neural Network Integrating PointNet-Based Adaptive Refinement for Investigating Crack Propagation in Industrial Applications. - Jingzhi Tu; Chun Liu 004; Pian Qi (IEEE Trans. Ind. Informatics, 2023)

A Deep Learning Approach for Long-Term Traffic Flow Prediction With Multifactor Fusion Using Spatiotemporal Graph Convolutional Network. - Xiaoyu Qi; Gang Mei; Jingzhi Tu; Ning Xi; Francesco Piccialli (IEEE Trans. Intell. Transp. Syst., 2023)