Qinghua Liu

1.0k total citations
22 papers, 214 citations indexed

About

Qinghua Liu is a scholar working on Artificial Intelligence, Signal Processing and Computer Networks and Communications. According to data from OpenAlex, Qinghua Liu has authored 22 papers receiving a total of 214 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 6 papers in Signal Processing and 5 papers in Computer Networks and Communications. Recurrent topics in Qinghua Liu's work include Time Series Analysis and Forecasting (5 papers), Anomaly Detection Techniques and Applications (5 papers) and Network Security and Intrusion Detection (3 papers). Qinghua Liu is often cited by papers focused on Time Series Analysis and Forecasting (5 papers), Anomaly Detection Techniques and Applications (5 papers) and Network Security and Intrusion Detection (3 papers). Qinghua Liu collaborates with scholars based in China, United States and France. Qinghua Liu's co-authors include Hao Liang, Gauri Joshi, Jianyu Wang, H. Vincent Poor, Qing-Hua Ling, Fei Han, Ying Xiong, John Paparrizos, Xianming Xie and Themis Palpanas and has published in prestigious journals such as IEEE Transactions on Signal Processing, Optics Express and BMC Bioinformatics.

In The Last Decade

Qinghua Liu

21 papers receiving 211 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Qinghua Liu China	9	101	30	30	28	23	22	214
Shakil Ahmed Bangladesh	8	137 1.4×	10 0.3×	21 0.7×	31 1.1×	36 1.6×	14	238
Atul Rawal United States	5	117 1.2×	19 0.6×	40 1.3×	31 1.1×	24 1.0×	18	231
Chika Yinka-Banjo Nigeria	6	58 0.6×	29 1.0×	42 1.4×	44 1.6×	17 0.7×	35	182
Dweepna Garg India	9	60 0.6×	24 0.8×	21 0.7×	59 2.1×	29 1.3×	32	238
Fei Zheng China	7	162 1.6×	14 0.5×	15 0.5×	61 2.2×	16 0.7×	20	248
Khaled Badran Egypt	10	175 1.7×	32 1.1×	31 1.0×	22 0.8×	8 0.3×	29	301
Ghaith Manita Tunisia	10	153 1.5×	8 0.3×	24 0.8×	47 1.7×	28 1.2×	31	283
J. de Curtò Spain	9	85 0.8×	9 0.3×	30 1.0×	62 2.2×	27 1.2×	31	271
Harshita Patel India	7	138 1.4×	17 0.6×	44 1.5×	31 1.1×	56 2.4×	24	250
M. A. El-Dosuky Egypt	8	58 0.6×	7 0.2×	18 0.6×	41 1.5×	48 2.1×	30	219

Countries citing papers authored by Qinghua Liu

Since Specialization

Citations

This map shows the geographic impact of Qinghua Liu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Qinghua Liu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Qinghua Liu more than expected).

Fields of papers citing papers by Qinghua Liu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Qinghua Liu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Qinghua Liu. The network helps show where Qinghua Liu may publish in the future.

Co-authorship network of co-authors of Qinghua Liu

This figure shows the co-authorship network connecting the top 25 collaborators of Qinghua Liu. A scholar is included among the top collaborators of Qinghua Liu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Qinghua Liu. Qinghua Liu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Boniol, Paul, A. Krishna, Qinghua Liu, et al.. (2025). VUS: effective and efficient accuracy measures for time-series anomaly detection. The VLDB Journal. 34(3). 10 indexed citations

Liu, Qinghua, Seunghak Lee, & John Paparrizos. (2025). TSB-AutoAD: Towards Automated Solutions for Time-Series Anomaly Detection. Proceedings of the VLDB Endowment. 18(11). 4364–4379. 3 indexed citations

Jia, Zixi, Qinghua Liu, Hong Li, Yuyan Chen, & Jiqiang Liu. (2025). Evaluating the Long-Term Memory of Large Language Models. 19759–19777.

Liu, Qinghua, Paul Boniol, Themis Palpanas, & John Paparrizos. (2024). Time-Series Anomaly Detection: Overview and New Trends. Proceedings of the VLDB Endowment. 17(12). 4229–4232. 13 indexed citations

Boniol, Paul, Qinghua Liu, Mong‐Han Huang, Themis Palpanas, & John Paparrizos. (2024). Dive into Time-Series Anomaly Detection: A Decade Review. arXiv (Cornell University). 1 indexed citations

Liu, Qinghua, et al.. (2023). Rice grains and grain impurity segmentation method based on a deep learning algorithm-NAM-EfficientNetv2. Computers and Electronics in Agriculture. 209. 107824–107824. 16 indexed citations

Liu, Qinghua, et al.. (2022). Device Action Prediction Based on K-means and Apriori for Smart Home. 1015–1021. 2 indexed citations

Liu, Qinghua, et al.. (2022). Depthwise Separable Convolutions based relation network for few-shot learning. 1156–1159. 1 indexed citations

Chen, Guoming & Qinghua Liu. (2022). Overview of ship image recognition methods based on computer vision. Journal of Physics Conference Series. 2387(1). 12001–12001. 1 indexed citations

10.

Liu, Qinghua & Qingping Li. (2021). A Joint Allocation Algorithm of Computing and Communication Resources Based on Reinforcement Learning in MEC System. Journal of Information Processing Systems. 17(4). 721–736. 1 indexed citations

11.

Misra, Dipendra, Qinghua Liu, Chi Jin, & John Langford. (2021). Provable Rich Observation Reinforcement Learning with Combinatorial Latent States. International Conference on Learning Representations. 2 indexed citations

12.

Wang, Jianyu, Qinghua Liu, Hao Liang, Gauri Joshi, & H. Vincent Poor. (2021). A Novel Framework for the Analysis and Design of Heterogeneous Federated Learning. IEEE Transactions on Signal Processing. 69. 5234–5249. 71 indexed citations

13.

Liu, Qinghua, et al.. (2021). A Sharp Analysis of Model-based Reinforcement Learning with Self-Play. International Conference on Machine Learning. 7001–7010. 1 indexed citations

14.

Ye, Feng, Zihao Liu, Qinghua Liu, & Zhijian Wang. (2020). Hydrologic Time Series Anomaly Detection Based on Flink. Mathematical Problems in Engineering. 2020. 1–12. 6 indexed citations

15.

Sørensen, Øystein, et al.. (2020). BayesMallows: An R Package for the Bayesian Mallows Model. The R Journal. 12(1). 324–324. 8 indexed citations

16.

Xiong, Ying, Qing-Hua Ling, Fei Han, & Qinghua Liu. (2019). An efficient gene selection method for microarray data based on LASSO and BPSO. BMC Bioinformatics. 20(S22). 715–715. 27 indexed citations

17.

Xie, Xianming, et al.. (2019). Efficient phase unwrapping algorithm based on cubature information particle filter applied to unwrap noisy continuous phase maps. Optics Express. 27(7). 9906–9906. 17 indexed citations

18.

Liu, Qinghua, Andrew H. Reiner, Arnoldo Frigessi, & Ida Scheel. (2019). Diverse personalized recommendations with uncertainty from implicit preference data with the Bayesian Mallows model. Knowledge-Based Systems. 186. 104960–104960. 9 indexed citations

19.

Liu, Qinghua, et al.. (2019). Road roughness acquisition and classification using improved restricted Boltzmann machine deep learning algorithm. Sensor Review. 39(6). 733–742. 8 indexed citations

20.

Liu, Qinghua, et al.. (2014). Fusing moving average model and stationary wavelet decomposition for automatic incident detection: case study of Tokyo Expressway. Journal of Traffic and Transportation Engineering (English Edition). 1(6). 404–414. 7 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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