Chunquan Liang

947 total citations · 1 hit paper
8 papers, 666 citations indexed

About

Chunquan Liang is a scholar working on Artificial Intelligence, Computer Networks and Communications and Signal Processing. According to data from OpenAlex, Chunquan Liang has authored 8 papers receiving a total of 666 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 3 papers in Computer Networks and Communications and 2 papers in Signal Processing. Recurrent topics in Chunquan Liang's work include Data Stream Mining Techniques (4 papers), Machine Learning and Data Classification (4 papers) and Anomaly Detection Techniques and Applications (3 papers). Chunquan Liang is often cited by papers focused on Data Stream Mining Techniques (4 papers), Machine Learning and Data Classification (4 papers) and Anomaly Detection Techniques and Applications (3 papers). Chunquan Liang collaborates with scholars based in China, Australia and United States. Chunquan Liang's co-authors include Bin Liu, Yuehan Chen, Dongjian He, Peng Jiang, Shuicheng Yan, Qunliang Song, Peng Shi, Zhengguo Hu, Zhengguo Hu and Yang Zhang and has published in prestigious journals such as IEEE Access, Information Sciences and Neurocomputing.

In The Last Decade

Chunquan Liang

7 papers receiving 614 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Real-Time Detection of Apple Leaf Diseases Using Deep Learning Approach Based on Improved Convolutional Neural Networks

2019 602 citations Peng Jiang, Yuehan Chen et al. IEEE Access profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Chunquan Liang China	4	562	223	93	66	57	8	666
Shanwen Zhang China	10	615 1.1×	268 1.2×	120 1.3×	39 0.6×	61 1.1×	18	791
G. Geetharamani India	9	559 1.0×	202 0.9×	94 1.0×	37 0.6×	31 0.5×	18	832
Yuxiang Li China	6	510 0.9×	199 0.9×	84 0.9×	31 0.5×	63 1.1×	25	645
J. Arun Pandian India	9	772 1.4×	280 1.3×	115 1.2×	63 1.0×	46 0.8×	32	917
Edna C. Too Kenya	5	756 1.3×	290 1.3×	168 1.8×	60 0.9×	27 0.5×	11	922
Mohammed Brahimi Algeria	6	576 1.0×	258 1.2×	99 1.1×	72 1.1×	37 0.6×	16	765
Aravind Krishnaswamy Rangarajan India	10	616 1.1×	306 1.4×	109 1.2×	58 0.9×	35 0.6×	14	786
Shilpi Harnal India	9	331 0.6×	129 0.6×	65 0.7×	39 0.6×	20 0.4×	24	464
Amit Prakash Singh India	14	429 0.8×	157 0.7×	43 0.5×	103 1.6×	28 0.5×	62	649
Sulieman Bani‐Ahmad Jordan	9	390 0.7×	238 1.1×	35 0.4×	82 1.2×	22 0.4×	30	575

Countries citing papers authored by Chunquan Liang

Since Specialization

Citations

This map shows the geographic impact of Chunquan Liang'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 Chunquan Liang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chunquan Liang more than expected).

Fields of papers citing papers by Chunquan Liang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chunquan Liang. 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 Chunquan Liang. The network helps show where Chunquan Liang may publish in the future.

Co-authorship network of co-authors of Chunquan Liang

This figure shows the co-authorship network connecting the top 25 collaborators of Chunquan Liang. A scholar is included among the top collaborators of Chunquan Liang 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 Chunquan Liang. Chunquan Liang 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:

8 of 8 papers shown

1.

Feng, Xinyuan, et al.. (2025). Representative negative sampling for graph positive-unlabeled learning. Neurocomputing. 656. 131462–131462.

2.

Liang, Chunquan, et al.. (2024). Bootstrap Latent Prototypes for graph positive-unlabeled learning. Information Fusion. 112. 102553–102553. 1 indexed citations

3.

Jiang, Peng, Yuehan Chen, Bin Liu, Dongjian He, & Chunquan Liang. (2019). Real-Time Detection of Apple Leaf Diseases Using Deep Learning Approach Based on Improved Convolutional Neural Networks. IEEE Access. 7. 59069–59080. 602 indexed citations breakdown →

4.

Liang, Chunquan, Mei Li, & Bin Liu. (2019). Online Computing Quantile Summaries Over Uncertain Data Streams. IEEE Access. 7. 10916–10926. 2 indexed citations

5.

Liang, Chunquan, et al.. (2018). Continuously maintaining approximate quantile summaries over large uncertain datasets. Information Sciences. 456. 174–190. 2 indexed citations

6.

Liang, Chunquan, Shuicheng Yan, Peng Shi, & Zhengguo Hu. (2014). Learning accurate very fast decision trees from uncertain data streams. International Journal of Systems Science. 46(16). 3032–3050. 6 indexed citations

7.

Liang, Chunquan, Shuicheng Yan, Peng Shi, & Zhengguo Hu. (2012). Learning very fast decision tree from uncertain data streams with positive and unlabeled samples. Information Sciences. 213. 50–67. 27 indexed citations

8.

Liang, Chunquan, Shuicheng Yan, & Qunliang Song. (2010). Decision Tree for Dynamic and Uncertain Data Streams. Asian Conference on Machine Learning. 209–224. 26 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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