Min Cao

42 total papers · 728 total citations
28 papers, 542 citations indexed

About

Min Cao is a scholar working on Artificial Intelligence, Mechanical Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Min Cao has authored 28 papers receiving a total of 542 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 7 papers in Mechanical Engineering and 6 papers in Computer Vision and Pattern Recognition. Recurrent topics in Min Cao's work include Radiomics and Machine Learning in Medical Imaging (4 papers), Non-Destructive Testing Techniques (4 papers) and Asphalt Pavement Performance Evaluation (4 papers). Min Cao is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (4 papers), Non-Destructive Testing Techniques (4 papers) and Asphalt Pavement Performance Evaluation (4 papers). Min Cao collaborates with scholars based in China, United States and Taiwan. Min Cao's co-authors include Dejin Zhang, Li He, Bailing Zhang, Qingquan Li, Ying Chen, Jingfu Wang, Xinxin Zhang, Peng Li, Jianzhong Wang and Junyu Liang and has published in prestigious journals such as IEEE Access, Sensors and IEEE Transactions on Intelligent Transportation Systems.

In The Last Decade

Min Cao

27 papers receiving 519 citations

Author Peers

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

Author						Papers	Cites
Min Cao	188	143	112	74	55	28	542
Houpu Yao	174 0.9×	133 0.9×	83 0.7×	37 0.5×	8 0.1×	21	547
H. Pina	199 1.1×	76 0.5×	37 0.3×	25 0.3×	32 0.6×	32	450
Amir Hossein Rezaie	228 1.2×	92 0.6×	68 0.6×	118 1.6×	14 0.3×	52	591
Phong Nguyen	123 0.7×	146 1.0×	93 0.8×	85 1.1×	6 0.1×	34	591
Xiaoyu Zhang	65 0.3×	124 0.9×	77 0.7×	65 0.9×	15 0.3×	45	485
Thanh Sang-To	277 1.5×	73 0.5×	154 1.4×	41 0.6×	6 0.1×	20	608
Jilei Zhou	248 1.3×	83 0.6×	69 0.6×	28 0.4×	12 0.2×	27	493
M. R. Ashory	227 1.2×	117 0.8×	40 0.4×	63 0.9×	11 0.2×	42	474
Wei Chen	255 1.4×	106 0.7×	37 0.3×	149 2.0×	8 0.1×	50	585
Тае-Il Seo	76 0.4×	364 2.5×	33 0.3×	37 0.5×	44 0.8×	34	577

Countries citing papers authored by Min Cao

Since Specialization

Citations

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

Fields of papers citing papers by Min Cao

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Min Cao

This figure shows the co-authorship network connecting the top 25 collaborators of Min Cao. A scholar is included among the top collaborators of Min Cao 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 Min Cao. Min Cao 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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