Kodai Ueyoshi

23 total papers · 627 total citations
22 papers, 449 citations indexed

About

Kodai Ueyoshi is a scholar working on Electrical and Electronic Engineering, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Kodai Ueyoshi has authored 22 papers receiving a total of 449 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Electrical and Electronic Engineering, 15 papers in Computer Vision and Pattern Recognition and 13 papers in Artificial Intelligence. Recurrent topics in Kodai Ueyoshi's work include Advanced Neural Network Applications (14 papers), Advanced Memory and Neural Computing (13 papers) and Machine Learning and ELM (7 papers). Kodai Ueyoshi is often cited by papers focused on Advanced Neural Network Applications (14 papers), Advanced Memory and Neural Computing (13 papers) and Machine Learning and ELM (7 papers). Kodai Ueyoshi collaborates with scholars based in Japan, Switzerland and Belgium. Kodai Ueyoshi's co-authors include Masato Motomura, Shinya Takamaeda-Yamazaki, Kota Ando, Tadahiro Kuroda, Tetsuya Asai, Masayuki Ikebe, Mototsugu Hamada, Shimpei Sato, Haruyoshi Yonekawa and Hiroki Nakahara and has published in prestigious journals such as IEEE Journal of Solid-State Circuits, IEEE Transactions on Circuits and Systems I Regular Papers and IEEE Transactions on Circuits & Systems II Express Briefs.

In The Last Decade

Kodai Ueyoshi

20 papers receiving 443 citations

Author Peers

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

Author Last Decade Papers Cites
Kodai Ueyoshi 371 185 115 62 36 22 449
Haruyoshi Yonekawa 290 0.8× 238 1.3× 113 1.0× 41 0.7× 20 0.6× 11 407
Daisuke Miyashita 398 1.1× 112 0.6× 132 1.1× 32 0.5× 46 1.3× 35 498
Jinwook Oh 324 0.9× 239 1.3× 95 0.8× 69 1.1× 66 1.8× 34 510
Daniel Bankman 359 1.0× 102 0.6× 120 1.0× 33 0.5× 22 0.6× 13 436
Mustafa Ali 400 1.1× 58 0.3× 88 0.8× 101 1.6× 44 1.2× 17 461
Alfio Di Mauro 330 0.9× 70 0.4× 77 0.7× 110 1.8× 67 1.9× 33 440
Mohamed M. Sabry Aly 339 0.9× 70 0.4× 73 0.6× 74 1.2× 52 1.4× 30 473
Guohao Dai 194 0.5× 154 0.8× 152 1.3× 110 1.8× 80 2.2× 43 413
Ayşegül Dündar 226 0.6× 383 2.1× 138 1.2× 57 0.9× 23 0.6× 24 518
Shixuan Zheng 273 0.7× 166 0.9× 143 1.2× 80 1.3× 30 0.8× 8 409

Countries citing papers authored by Kodai Ueyoshi

Since Specialization
Citations

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

Fields of papers citing papers by Kodai Ueyoshi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kodai Ueyoshi

This figure shows the co-authorship network connecting the top 25 collaborators of Kodai Ueyoshi. A scholar is included among the top collaborators of Kodai Ueyoshi 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 Kodai Ueyoshi. Kodai Ueyoshi 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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2026