Pitoyo Hartono
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition top 10%
- Control and Systems Engineering top 10%
- Biomedical Engineering
- Computational Theory and Mathematics top 10%
- Co-authors
- Shuji HashimotoThomas TrappenbergPeter SinčákHideyuki SawadaMattias WahdeKenji SuzukiJun-ichi AbeJán Vaščák
- Topics
- Neural Networks and Applications (29 papers)Face and Expression Recognition (11 papers)Reinforcement Learning in Robotics (9 papers)
- Journals
- SHILAP Revista de lepidopterologíaIEEE AccessFrontiers in Psychology
In The Last Decade
Pitoyo Hartono
66 papers receiving 376 citations
Peers
Comparison fields: 5 of 85
- Artificial Intelligence 183
- Computer Vision and Pattern Recognition 106
- Control and Systems Engineering 63
- Biomedical Engineering 38
- Computational Theory and Mathematics 36
Countries citing papers authored by Pitoyo Hartono
This map shows the geographic impact of Pitoyo Hartono'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 Pitoyo Hartono with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pitoyo Hartono more than expected).
Fields of papers citing papers by Pitoyo Hartono
This network shows the impact of papers produced by Pitoyo Hartono. 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 Pitoyo Hartono. The network helps show where Pitoyo Hartono may publish in the future.
Co-authorship network of co-authors of Pitoyo Hartono
This figure shows the co-authorship network connecting the top 25 collaborators of Pitoyo Hartono. A scholar is included among the top collaborators of Pitoyo Hartono 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 Pitoyo Hartono. Pitoyo Hartono 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 | 2 | |
| 3 | 0 | |
| 4 | 0 | |
| 5 | 2 | |
| 6 | 0 | |
| 7 | 1 | |
| 8 | 54 | |
| 9 | 16 | |
| 10 | 5 | |
| 11 | 5 | |
| 12 | 5 | |
| 13 | 4 | |
| 14 | Nonlinear Classification using Ensemble of Linear Perceptrons | 0 |
| 15 | 39 | |
| 16 | Machine Listening for Autonomous Musical Performance Systems | 2 |
| 17 | 17 | |
| 18 | Temperature Switching in Neural Network Ensemble | 8 |
| 19 | Adaptive Timbre Control System Using Gesture | 1 |
| 20 | Adaptive timbre control using gesture | 2 |
About Pitoyo Hartono
Pitoyo Hartono is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing, having authored 74 papers that have together received 393 indexed citations. Recurring topics across this work include Neural Networks and Applications (29 papers), Face and Expression Recognition (11 papers) and Reinforcement Learning in Robotics (9 papers). The work is most often cited by research in Health Informatics (21 citations), Artificial Intelligence (183 citations) and Computer Vision and Pattern Recognition (106 citations). Pitoyo Hartono has collaborated with scholars based in Japan, Slovakia and Canada. Frequent co-authors include Shuji Hashimoto, Thomas Trappenberg, Peter Sinčák, Hideyuki Sawada, Mattias Wahde, Kenji Suzuki, Jun-ichi Abe, Ján Vaščák, Ryo Saegusa and Kôichi Yokosawa. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Access and Frontiers in Psychology.
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.