Daisuke Ikeda
- Artificial Intelligence top 10%
- Periodontics top 5%
- Public Health, Environmental and Occupational Health
- Molecular Biology
- Epidemiology
- Co-authors
- Hiroya TakamuraManabu OkumuraNao SuzukiYoshio NakanoToshihiko KogaHiroshi SakamotoYasuo YoshidaNaomi Yamaguchi
- Topics
- Topic Modeling (5 papers)Spam and Phishing Detection (3 papers)Sentiment Analysis and Opinion Mining (3 papers)
- Partner nations
- JapanUnited StatesSouth Korea
In The Last Decade
Daisuke Ikeda
17 papers receiving 217 citations
Peers
Comparison fields: 5 of 65
- Artificial Intelligence 103
- Periodontics 74
- Public Health, Environmental and Occupational Health 35
- Molecular Biology 33
- Epidemiology 25
Countries citing papers authored by Daisuke Ikeda
This map shows the geographic impact of Daisuke Ikeda'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 Daisuke Ikeda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daisuke Ikeda more than expected).
Fields of papers citing papers by Daisuke Ikeda
This network shows the impact of papers produced by Daisuke Ikeda. 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 Daisuke Ikeda. The network helps show where Daisuke Ikeda may publish in the future.
Co-authorship network of co-authors of Daisuke Ikeda
This figure shows the co-authorship network connecting the top 25 collaborators of Daisuke Ikeda. A scholar is included among the top collaborators of Daisuke Ikeda 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 Daisuke Ikeda. Daisuke Ikeda is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 2 | |
| 3 | 0 | |
| 4 | 0 | |
| 5 | 8 | |
| 6 | 1 | |
| 7 | 2 | |
| 8 | 1 | |
| 9 | 2 | |
| 10 | 3 | |
| 11 | 1 | |
| 12 | Onset time determination of precursory events in time series data by an extension of Singular Spectrum Transformation | 3 |
| 13 | Detecting precursory events in time series data by an extension of singular spectrum transformation | 3 |
| 14 | Learning to Shift the Polarity of Words for Sentiment Classification | 47 |
| 15 | Semi-supervised learning for blog classification | 17 |
| 16 | 1 | |
| 17 | 68 | |
| 18 | 27 | |
| 19 | Intractability of Decision Problems for Finite-Memory Automata. | 0 |
| 20 | 26 |
About Daisuke Ikeda
Daisuke Ikeda is a scholar working on Artificial Intelligence, Human-Computer Interaction and Microbiology, having authored 23 papers that have together received 227 indexed citations. Recurring topics across this work include Topic Modeling (5 papers), Spam and Phishing Detection (3 papers) and Sentiment Analysis and Opinion Mining (3 papers). The work is most often cited by research in Periodontics (74 citations), Microbiology (3 citations) and Artificial Intelligence (103 citations). Daisuke Ikeda has collaborated with scholars based in Japan, United States and South Korea. Frequent co-authors include Hiroya Takamura, Manabu Okumura, Nao Suzuki, Yoshio Nakano, Toshihiko Koga, Hiroshi Sakamoto, Yasuo Yoshida, Naomi Yamaguchi, Yoshihisa Yamashita and Shingo Kato. Their work appears in journals such as Journal of Clinical Microbiology, Infection and Immunity and Theoretical Computer Science.
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.