Kaoru Nakano

3.0k total citations
24 papers, 553 citations indexed

About

Kaoru Nakano is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering and Management Science and Operations Research. According to data from OpenAlex, Kaoru Nakano has authored 24 papers receiving a total of 553 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 6 papers in Electrical and Electronic Engineering and 3 papers in Management Science and Operations Research. Recurrent topics in Kaoru Nakano's work include Energy Load and Power Forecasting (5 papers), Grey System Theory Applications (3 papers) and Neural Networks and Applications (3 papers). Kaoru Nakano is often cited by papers focused on Energy Load and Power Forecasting (5 papers), Grey System Theory Applications (3 papers) and Neural Networks and Applications (3 papers). Kaoru Nakano collaborates with scholars based in Japan and Vietnam. Kaoru Nakano's co-authors include Akinori Takami, Hirotoshi Yamada, Seiichiro Koda, Hidewaki Nakagawa, Akihiro Fujimoto, Tatsuhiko Tsunoda, Hiroyuki Mori, Takashi Ikeya, Masao Nagasaki and Yusuke Nakamura and has published in prestigious journals such as Nature Genetics, SHILAP Revista de lepidopterología and Journal of Membrane Science.

In The Last Decade

Kaoru Nakano

24 papers receiving 530 citations

Peers

Kaoru Nakano
Comparison fields: 5 of 92
  • Artificial Intelligence 232
  • Electrical and Electronic Engineering 101
  • Cognitive Neuroscience 91
  • Biomedical Engineering 78
  • Molecular Biology 71
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Hanyu Li China View profile →
Citations per field, relative to Kaoru Nakano
Kaoru Nakano · 1×
Citations per year, relative to Kaoru Nakano
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Countries citing papers authored by Kaoru Nakano

Since Specialization
Citations

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

Fields of papers citing papers by Kaoru Nakano

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kaoru Nakano

This figure shows the co-authorship network connecting the top 25 collaborators of Kaoru Nakano. A scholar is included among the top collaborators of Kaoru Nakano 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 Kaoru Nakano. Kaoru Nakano 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
# Work Indexed citations
1 7
2 22
3 3
4 9
5 28
6 3
7 2
8 1
9 4
10 1
11 80
12
Extraction of eicosapentaenoic acid ethyl ester using metallic copper and a copper(2) salt in water
1
13 16
14 2
15 10
16 53
17 1
18 2
19 256
20
Information processing using a model of associative memory
4

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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