Itsuki Noda
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 2%
- Control and Systems Engineering top 5%
- Mechanical Engineering top 10%
- Computer Networks and Communications top 5%
- Co-authors
- Hiroaki KitanoMinoru AsadaEiichi OsawaHitoshi MatsubaraYasuo KuniyoshiSatoshı TadokoroKazuo HirakiIan Frank
- Topics
- Evacuation and Crowd Dynamics (13 papers)Reinforcement Learning in Robotics (12 papers)Robotics and Automated Systems (11 papers)
- Cited by
- Computer Vision and Pattern RecognitionArtificial IntelligenceControl and Systems Engineering
- Partner nations
- JapanUnited StatesItaly
In The Last Decade
Itsuki Noda
71 papers receiving 1.4k citations
Peers
Comparison fields: 5 of 102
- Artificial Intelligence 741
- Computer Vision and Pattern Recognition 509
- Control and Systems Engineering 270
- Mechanical Engineering 248
- Computer Networks and Communications 245
Countries citing papers authored by Itsuki Noda
This map shows the geographic impact of Itsuki Noda'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 Itsuki Noda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Itsuki Noda more than expected).
Fields of papers citing papers by Itsuki Noda
This network shows the impact of papers produced by Itsuki Noda. 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 Itsuki Noda. The network helps show where Itsuki Noda may publish in the future.
Co-authorship network of co-authors of Itsuki Noda
This figure shows the co-authorship network connecting the top 25 collaborators of Itsuki Noda. A scholar is included among the top collaborators of Itsuki Noda 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 Itsuki Noda. Itsuki Noda is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 2 | |
| 5 | 1 | |
| 6 | 2 | |
| 7 | 3 | |
| 8 | 0 | |
| 9 | Simulation, Modeling, and Programming for Autonomous Robots: Third International Conference, SIMPAR 2012, Tsukuba, Japan, November 5-8, 2012 | 0 |
| 10 | Verification of evacuation plan by exhaustive testing with evacuation simulator NetMAS | 0 |
| 11 | 1 | |
| 12 | 1 | |
| 13 | 0 | |
| 14 | 3 | |
| 15 | RoboCup 2005: Robot Soccer World Cup IX (Lecture Notes in Computer Science) | 4 |
| 16 | 1 | |
| 17 | 73 | |
| 18 | 22 | |
| 19 | 10 | |
| 20 | 235 |
About Itsuki Noda
Itsuki Noda is a scholar working on Transportation, Ocean Engineering and Artificial Intelligence, having authored 85 papers that have together received 1.6k indexed citations. Recurring topics across this work include Evacuation and Crowd Dynamics (13 papers), Reinforcement Learning in Robotics (12 papers) and Robotics and Automated Systems (11 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (509 citations), Artificial Intelligence (741 citations) and Control and Systems Engineering (270 citations). Itsuki Noda has collaborated with scholars based in Japan, United States and Italy. Frequent co-authors include Hiroaki Kitano, Minoru Asada, Eiichi Osawa, Hitoshi Matsubara, Yasuo Kuniyoshi, Satoshı Tadokoro, Kazuo Hiraki, Ian Frank, Tokiichiro Takahashi and Susumu Shimada. Their work appears in journals such as IEEE Access, Artificial Intelligence and Lecture notes in 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.