Kenichi Tamura

866 citations
104 papers · 624 indexed · h-index 13
Topics
Metaheuristic Optimization Algorithms Research (49 papers)Advanced Multi-Objective Optimization Algorithms (41 papers)Evolutionary Algorithms and Applications (17 papers)

In The Last Decade

Kenichi Tamura

89 papers receiving 601 citations

Peers

Kenichi Tamura
Comparison fields: 5 of 101
  • Artificial Intelligence 274
  • Computational Theory and Mathematics 148
  • Control and Systems Engineering 136
  • Electrical and Electronic Engineering 98
  • Biomedical Engineering 78
Replace G. Narayana Naik with:
G. Narayana Naik India
Dong Hwa Kim South Korea
Shuqiang Huang China
Sew Sun Tiang Malaysia
Guan-Chun Luh Taiwan
Mehmet Umut Erdaş Türkiye
Ганеш Какандикар India
Jiangtao Wang China
Kenichi Tamura relative to G. Narayana Naik India G. Narayana Naik's profile →
Citations per field
00.5×1.7×
G. Narayana Naik · 1×
Citations per year

Countries citing papers authored by Kenichi Tamura

Since Specialization
Citations

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

Fields of papers citing papers by Kenichi Tamura

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kenichi Tamura

This figure shows the co-authorship network connecting the top 25 collaborators of Kenichi Tamura. A scholar is included among the top collaborators of Kenichi Tamura 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 Kenichi Tamura. Kenichi Tamura is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 1
2 1
3 1
4 2
5 0
6 3
7 3
8 8
9 37
10 1
11 89
12
Adaptive state feedback control for descriptor systems
3
13 3
14 3
15 14
16 6
17 1
18 1
19 1
20 1

About Kenichi Tamura

Kenichi Tamura is a scholar working on Computational Theory and Mathematics, Numerical Analysis and Artificial Intelligence, having authored 104 papers that have together received 624 indexed citations. Recurring topics across this work include Metaheuristic Optimization Algorithms Research (49 papers), Advanced Multi-Objective Optimization Algorithms (41 papers) and Evolutionary Algorithms and Applications (17 papers). The work is most often cited by research in Computational Theory and Mathematics (148 citations), Artificial Intelligence (274 citations) and Industrial and Manufacturing Engineering (64 citations). Kenichi Tamura has collaborated with scholars based in Japan, United States and Australia. Frequent co-authors include Keiichiro Yasuda, Hiromitsu Ohmori, Kiyotaka Shimizu, Atsushi Ishigame, Hidetoshi Onodera, Yumiko Ishikawa, Yuji Haraguchi, N. Takeda, Tatsuya Shimizu and Yusuke Hara. Their work appears in journals such as Scientific Reports, RSC Advances and Information Sciences.

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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