Yoichiro Maeda

70 papers receiving 275 citations

Peers

Yoichiro Maeda
Comparison fields: 5 of 53
  • Human-Computer Interaction 42
  • Computer Vision and Pattern Recognition 113
  • Control and Systems Engineering 100
  • Artificial Intelligence 98
  • Ocean Engineering 42
Replace Dominik Joho with:
Dominik Joho Germany
Young-Jo Cho South Korea
Samuel Blisard United States
Joo-Chan Sohn South Korea
Dominik Jain Germany
Phillip Walker United States
Marcus Kleinehagenbrock Germany
Benjamín Hernández Mexico
Philipp Althaus Sweden
L. Mayor Switzerland
Yoichiro Maeda relative to Dominik Joho Germany Dominik Joho's profile →
Citations per field
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Citations per year

Countries citing papers authored by Yoichiro Maeda

Since Specialization
Citations

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

Fields of papers citing papers by Yoichiro Maeda

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Yoichiro Maeda, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Yoichiro Maeda Line = papers co-authored together Yoichiro Maeda links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 78 papers — load more, or switch the sort, to bring in the rest.

#Work
1 199142
2 198828
3 200515
4
Parallel genetic algorithm with adaptive genetic parameters tuned by fuzzy reasoning
200513
5 201310
6 20169
7 19998
8 19938
9 20107
10 20117
11 20066
12 20146
13 20066
14 19996
15 19916
16 20136
17 20106
18 20205
19 20095
20 20185

About Yoichiro Maeda

Yoichiro Maeda is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Control and Systems Engineering, Human-Computer Interaction and Social Psychology, having authored 78 papers that have together received 320 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (16 papers), Robotic Path Planning Algorithms (14 papers), Gaze Tracking and Assistive Technology (11 papers), Robotics and Automated Systems (11 papers), Evolutionary Algorithms and Applications (9 papers), Social Robot Interaction and HRI (9 papers), Robot Manipulation and Learning (7 papers) and Visual Attention and Saliency Detection (7 papers). The work is most often cited by research in Human-Computer Interaction (42 citations), Computer Vision and Pattern Recognition (113 citations), Control and Systems Engineering (100 citations), Artificial Intelligence (98 citations) and Ocean Engineering (42 citations). Yoichiro Maeda has collaborated with scholars based in Japan, United States and China. Frequent co-authors include Yasutake Takahashi, Morikazu Takegaki, Takayuki Nakamura, Tomohiro Takagi, Kyohei Yoshida, Kazuyuki Murase, Pintu Chandra Shill, Masayuki Yamamoto, Hiroshi Koyama and Satoru Fukami. Their work appears in journals such as International journal of innovative computing, information & control, International Journal of Approximate Reasoning, Information Sciences, Advanced Robotics and Journal of Intelligent & Fuzzy Systems.

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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