Ying Dai

72 total papers · 488 total citations
43 papers, 309 citations indexed

About

Ying Dai is a scholar working on Computer Vision and Pattern Recognition, Complementary and alternative medicine and Biomedical Engineering. According to data from OpenAlex, Ying Dai has authored 43 papers receiving a total of 309 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Computer Vision and Pattern Recognition, 8 papers in Complementary and alternative medicine and 8 papers in Biomedical Engineering. Recurrent topics in Ying Dai's work include Image Retrieval and Classification Techniques (11 papers), Advanced Image and Video Retrieval Techniques (9 papers) and Traditional Chinese Medicine Studies (8 papers). Ying Dai is often cited by papers focused on Image Retrieval and Classification Techniques (11 papers), Advanced Image and Video Retrieval Techniques (9 papers) and Traditional Chinese Medicine Studies (8 papers). Ying Dai collaborates with scholars based in Japan, China and Germany. Ying Dai's co-authors include Yasuaki Nakano, Basabi Chakraborty, Minghui Shi, Ziyi Zhu, Shaozi Li, Koji Hashimoto, Jing Jiang, Yuya Nakano, Toshiaki Ishii and Yoshitaka Shibata and has published in prestigious journals such as Pattern Recognition, Multimedia Tools and Applications and Mathematical Methods in the Applied Sciences.

In The Last Decade

Ying Dai

35 papers receiving 267 citations

Author Peers

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

Author						Papers	Cites
Ying Dai	220	28	26	23	22	43	309
Jin‐Woo Hong	161 0.7×	15 0.5×	18 0.7×	57 2.5×	8 0.4×	43	250
Yueli Cui	232 1.1×	72 2.6×	42 1.6×	27 1.2×	2 0.1×	29	341
Lingfeng Xu	101 0.5×	18 0.6×	18 0.7×	30 1.3×	4 0.2×	48	335
R. Gopikakumari	173 0.8×	59 2.1×	64 2.5×	62 2.7×	2 0.1×	39	284
G. Yamuna	146 0.7×	40 1.4×	42 1.6×	19 0.8×	3 0.1×	54	306
Meie Fang	128 0.6×	48 1.7×	42 1.6×	10 0.4×	3 0.1×	53	353
Ziqi Liu	179 0.8×	28 1.0×	59 2.3×	6 0.3×	2 0.1×	27	309
Takeshi Shakunaga	263 1.2×	23 0.8×	17 0.7×	15 0.7×		44	330
Gyeonghoon Kim	173 0.8×	33 1.2×	39 1.5×	11 0.5×		42	348
Yong-Goo Shin	280 1.3×	37 1.3×	34 1.3×	9 0.4×	1 0.0×	35	350

Countries citing papers authored by Ying Dai

Since Specialization

Citations

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

Fields of papers citing papers by Ying Dai

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ying Dai

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