Makoto Imamura
- Artificial Intelligence top 10%
- Signal Processing top 5%
- Computer Networks and Communications top 10%
- Economics and Econometrics
- Biomedical Engineering
- Co-authors
- Eamonn KeoghDaniel NikovskiYan ZhuMichael JonesAkira MizunoTsuyoshi IizukaHarumi WatanabeMasafumi MIWA
- Topics
- Time Series Analysis and Forecasting (15 papers)Complex Systems and Time Series Analysis (8 papers)Analog and Mixed-Signal Circuit Design (7 papers)
- Journals
- IEEE AccessIEEE Transactions on Industry ApplicationsIEEE Transactions on Instrumentation and Measurement
- Partner nations
- JapanUnited StatesThailand
In The Last Decade
Makoto Imamura
32 papers receiving 259 citations
Peers
Comparison fields: 5 of 53
- Artificial Intelligence 152
- Signal Processing 143
- Computer Networks and Communications 66
- Economics and Econometrics 42
- Biomedical Engineering 31
Countries citing papers authored by Makoto Imamura
This map shows the geographic impact of Makoto Imamura'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 Makoto Imamura with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Makoto Imamura more than expected).
Fields of papers citing papers by Makoto Imamura
This network shows the impact of papers produced by Makoto Imamura. 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 Makoto Imamura. The network helps show where Makoto Imamura may publish in the future.
Co-authorship network of co-authors of Makoto Imamura
This figure shows the co-authorship network connecting the top 25 collaborators of Makoto Imamura. A scholar is included among the top collaborators of Makoto Imamura 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 Makoto Imamura. Makoto Imamura is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 5 | |
| 3 | 9 | |
| 4 | 3 | |
| 5 | 1 | |
| 6 | 1 | |
| 7 | 17 | |
| 8 | 59 | |
| 9 | 7 | |
| 10 | 2 | |
| 11 | 1 | |
| 12 | 12 | |
| 13 | 1 | |
| 14 | 30 | |
| 15 | 4 | |
| 16 | 0 | |
| 17 | 2 | |
| 18 | 0 | |
| 19 | 18 | |
| 20 | 5 |
About Makoto Imamura
Makoto Imamura is a scholar working on Signal Processing, Transportation and Artificial Intelligence, having authored 38 papers that have together received 265 indexed citations. Recurring topics across this work include Time Series Analysis and Forecasting (15 papers), Complex Systems and Time Series Analysis (8 papers) and Analog and Mixed-Signal Circuit Design (7 papers). The work is most often cited by research in Signal Processing (143 citations), Artificial Intelligence (152 citations) and Computer Networks and Communications (66 citations). Makoto Imamura has collaborated with scholars based in Japan, United States and Thailand. Frequent co-authors include Eamonn Keogh, Daniel Nikovski, Yan Zhu, Michael Jones, Akira Mizuno, Tsuyoshi Iizuka, Harumi Watanabe, Masafumi MIWA, Takuo Suganuma and Mikiko Sato. Their work appears in journals such as IEEE Access, IEEE Transactions on Industry Applications and IEEE Transactions on Instrumentation and Measurement.
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.