data ming, anomaly detection, machine learning, deep learning. His papers are published as follows,
Research Achievements [1] Jian Zheng*, Zhang jian, Kui Yu, Shiyan WANG.2025. Detection to anomalous data using hypersphere method with mapping transformation. Expert System with Applications [2] [3] Jian Zheng*, Shumiao Ren, Jingyue Zhang, et al. 2025. Binary classification for imbalanced data using data conformity mechanism. Multimedia Systems. [4] Jian Zheng*, Xin Hu. 2024. An irrelevant attribute resistance approach to binary classification. Information Sciences. [5] Jian Zheng*, Lin Li, Shiyan Wang, et al. 2024. Binary classification for imbalanced datasets using twin hyperspheres based on conformal method. Cluster Computing. [6] Jian Zheng*, Nanshan Ruan, Pingping Wei, et al. 2024. A fuzzy detection approach to high-dimensional anomalies. Multimedia Systems. [7] Jian Zheng*. 2023. Anti-noise twin-hyperspheres with density fuzzy for binary classification to imbalanced data with noise. Complex& Intelligent Systems. [8] Jian Zheng, Hongchun Qu*, Zhaoni Li, et al. 2023. Conformal transformation twin-hyperspheres for highly imbalanced data binary classification. The 9th IEEE International Conference on Data Science and Advanced Analytics. [9] Jian Zheng, Hongchun Qu*, Zhaoni Li, et al. 2022. A deep hypersphere approach to high-dimensional anomaly detection. Applied Soft Computing. [10] Jian Zheng, Hongchun Qu*, Zhaoni Li, et al. 2022. An irrelevant attribute resistant approach to anomaly detection in high-dimensional space using a deep hyper sphere structure. Applied Soft Computing. [11] Jian Zheng*, Jingyi Li, Cong Liu, et al. 2022. Anomaly detection for high-dimensional space using deep hypersphere fused with probability approach. Complex& Intelligent Systems. [12] Honghu Qu, Jian Zheng*（Corresponding author, Xiaoming Tao. 2022. Effects of loss function and data sparsity on smooth manifold extraction with deep model. Expert Systems with Applications. [13] Jian Zheng*, Qingling Wang, Cong Liu, et al. 2022. Relation patterns extraction from high-dimensional climate data with complicated multi-variables using deep neural networks. Applied Intelligence. [14] Jian Zheng*. 2022. Smooth manifold extraction in high‑dimensional data using a deep model. Journal of Ambient Intelligence and Humanized Computing. [15] Jian Zheng, Hongchun Qu*, Zhaoni Li, et al. 2022. A novel autoencoder approach to feature extraction with linear separability for high-dimensional data. PeerJ Computer Science. [16] Jian Zheng*. 2021. Deep neural networks for detection abnormal trend in electricity data. Proceedings of the Romanian Academy. [17] Jian Zheng*, Jianfeng Wang, Yanping Chen, et al. 2021. Effective approximation of high‑dimensional space using neural networks. Journal of Supercomputing. [18] Jian Zheng*, Jianfeng Wang, Yanping Chen, et al. 2021. Neural networks trained with high-dimensional functions approximation data in high-dimensional space. Journal of Intelligent & Fuzzy Systems. [19] Jian Zheng*, Jianfeng Wang, Shuping Chen, et al. 2021. Deep neural networks for climate relation extraction. Global NEST Journal. [20] Jian Zheng*, Jianfeng Wang, Jiang Li, et al. 2021. Relational patterns discovery in climate with deep learning mode. Comptes rendus del Acade mie bulgare des Sciences. [21] Jian Zheng*. 2020. Folding approach of muti-dimensional attributes facing to quality of service in cloud service. Proceedings of the Romanian Academy. |
