Fun Ye

21 total papers · 511 total citations
15 papers, 366 citations indexed

About

Fun Ye is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Fun Ye has authored 15 papers receiving a total of 366 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Computer Vision and Pattern Recognition, 5 papers in Artificial Intelligence and 4 papers in Computer Networks and Communications. Recurrent topics in Fun Ye's work include Metaheuristic Optimization Algorithms Research (5 papers), Advanced Clustering Algorithms Research (4 papers) and Face and Expression Recognition (4 papers). Fun Ye is often cited by papers focused on Metaheuristic Optimization Algorithms Research (5 papers), Advanced Clustering Algorithms Research (4 papers) and Face and Expression Recognition (4 papers). Fun Ye collaborates with scholars based in Taiwan. Fun Ye's co-authors include Ching‐Yi Chen, Ching‐Yi Chen, Hsuan-Ming Feng, Jenhui Chen, Shiann‐Tsong Sheu, Jen‐Shiun Chiang, Ying-Tung Hsiao, Chun-Wen Chen and Chih‐Hsien Hsia and has published in prestigious journals such as Expert Systems with Applications, Cybernetics & Systems and Journal of marine science and technology.

In The Last Decade

Fun Ye

15 papers receiving 329 citations

Author Peers

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

Author						Papers	Cites
Fun Ye	217	104	54	43	40	15	366
Emilio Corchado	173 0.8×	59 0.6×	29 0.5×	39 0.9×	21 0.5×	24	352
Shail Kumar Dinkar	163 0.8×	62 0.6×	48 0.9×	33 0.8×	47 1.2×	19	357
T. Kathirvalavakumar	271 1.2×	92 0.9×	20 0.4×	34 0.8×	20 0.5×	27	393
Chao Yao	178 0.8×	194 1.9×	63 1.2×	34 0.8×	26 0.7×	31	395
Matti Tommiska	286 1.3×	172 1.7×	45 0.8×	29 0.7×	16 0.4×	12	369
Daniel Iercan	155 0.7×	150 1.4×	47 0.9×	53 1.2×	30 0.8×	20	366
Mingxing Duan	156 0.7×	79 0.8×	67 1.2×	20 0.5×	18 0.5×	17	347
Zhongqin Bi	99 0.5×	115 1.1×	32 0.6×	46 1.1×	25 0.6×	34	346
Hongsheng Yu	152 0.7×	126 1.2×	33 0.6×	74 1.7×	16 0.4×	14	391
Nevena Lazic	255 1.2×	84 0.8×	35 0.6×	45 1.0×	12 0.3×	15	398

Countries citing papers authored by Fun Ye

Since Specialization

Citations

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

Fields of papers citing papers by Fun Ye

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fun Ye

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