Qing-Hua Ling

78 total papers · 1.5k total citations
48 papers, 1.1k citations indexed

About

Qing-Hua Ling is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Computer Vision and Pattern Recognition. According to data from OpenAlex, Qing-Hua Ling has authored 48 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 43 papers in Artificial Intelligence, 19 papers in Computational Theory and Mathematics and 9 papers in Computer Vision and Pattern Recognition. Recurrent topics in Qing-Hua Ling's work include Metaheuristic Optimization Algorithms Research (30 papers), Advanced Multi-Objective Optimization Algorithms (19 papers) and Machine Learning and ELM (14 papers). Qing-Hua Ling is often cited by papers focused on Metaheuristic Optimization Algorithms Research (30 papers), Advanced Multi-Objective Optimization Algorithms (19 papers) and Machine Learning and ELM (14 papers). Qing-Hua Ling collaborates with scholars based in China, United States and Ghana. Qing-Hua Ling's co-authors include Fei Han, Henry Han, De-Shuang Huang, Jing Jiang, Fei Han, Benyue Su, Arfan Ali Nagra, Jie Wang, Yuqing Song and Jianming Zhang and has published in prestigious journals such as PLoS ONE, Expert Systems with Applications and IEEE Access.

In The Last Decade

Qing-Hua Ling

42 papers receiving 1.0k citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Qing-Hua Ling	676	243	226	140	111	48	1.1k
Fei Han	872 1.3×	304 1.3×	272 1.2×	185 1.3×	169 1.5×	66	1.4k
Jie Zhang	740 1.1×	246 1.0×	369 1.6×	109 0.8×	153 1.4×	65	1.4k
Harun Uğuz	818 1.2×	260 1.1×	226 1.0×	154 1.1×	131 1.2×	37	1.5k
Rafael Stubs Parpinelli	807 1.2×	191 0.8×	262 1.2×	86 0.6×	98 0.9×	62	1.4k
Prospero C. Naval	355 0.5×	170 0.7×	252 1.1×	86 0.6×	89 0.8×	45	1.1k
Yi Chen	496 0.7×	191 0.8×	159 0.7×	114 0.8×	115 1.0×	56	1.1k
Daniel Hernández-Lobato	639 0.9×	224 0.9×	193 0.9×	81 0.6×	60 0.5×	43	1.1k
Priyanka Anand	746 1.1×	192 0.8×	248 1.1×	256 1.8×	158 1.4×	37	1.2k
Yi Liu	680 1.0×	402 1.7×	108 0.5×	92 0.7×	136 1.2×	62	1.3k
Hassan Shaban	561 0.8×	191 0.8×	219 1.0×	173 1.2×	96 0.9×	19	1.0k

Countries citing papers authored by Qing-Hua Ling

Since Specialization

Citations

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

Fields of papers citing papers by Qing-Hua Ling

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Qing-Hua Ling. 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 Qing-Hua Ling. The network helps show where Qing-Hua Ling may publish in the future.

Co-authorship network of co-authors of Qing-Hua Ling

This figure shows the co-authorship network connecting the top 25 collaborators of Qing-Hua Ling. A scholar is included among the top collaborators of Qing-Hua Ling 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 Qing-Hua Ling. Qing-Hua Ling is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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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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