Daniel Graves
Impact in
- Artificial Intelligence top 5%
- Advanced Clustering Algorithms Research
- Fuzzy Logic and Control Systems
- Neural Networks and Applications
- Reinforcement Learning in Robotics
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- Face and Expression Recognition
- Medical Image Segmentation Techniques
- Image Retrieval and Classification Techniques
Papers in
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- Reinforcement Learning in Robotics 6
- Advanced Clustering Algorithms Research 3
- Fuzzy Logic and Control Systems 3
- Neural Networks and Applications 2
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- Time Series Analysis and Forecasting 4
- Music and Audio Processing 2
- Co-authors
- Witold Pedrycz (8 shared papers)Xin Xu (1 shared paper)Zhenhua Huang (1 shared paper)Silviu Pitis (1 shared paper)Richard S. Sutton (1 shared paper)Jun Luo (2 shared papers)Yingxu Wang (2 shared papers)Tony Tsai (1 shared paper)
- Journals
- Neurocomputing (1 paper)Fuzzy Sets and Systems (1 paper)Adaptive Behavior (1 paper)Electronics Letters (1 paper)IEEE Transactions on Cybernetics (1 paper)
- Partner nations
- CanadaUnited KingdomChina
In The Last Decade
Daniel Graves
18 papers receiving 434 citations
Peers
Comparison fields: 5 of 78
- Artificial Intelligence 310
- Computer Vision and Pattern Recognition 173
- Media Technology 51
- Signal Processing 52
- Computational Theory and Mathematics 51
Countries citing papers authored by Daniel Graves
This map shows the geographic impact of Daniel Graves'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 Daniel Graves with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Graves more than expected).
Fields of papers citing papers by Daniel Graves
This network shows the impact of papers produced by Daniel Graves. 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 Daniel Graves. The network helps show where Daniel Graves may publish in the future.
Co-authors
The 25 scholars most cited alongside Daniel Graves, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2009 | 278 | |
| 2 | 2008 | 43 | |
| 3 | 2014 | 27 | |
| 4 | 2007 | 19 | |
| 5 | 2020 | 15 | |
| 6 | 2018 | 9 | |
| 7 | 2012 | 8 | |
| 8 | Pay models for online news in the US and Europe: 2019 update | 2019 | 8 |
| 9 | 2010 | 7 | |
| 10 | 2009 | 7 | |
| 11 | 2022 | 5 | |
| 12 | 2021 | 4 | |
| 13 | 2019 | 4 | |
| 14 | 2021 | 3 | |
| 15 | 2022 | 3 | |
| 16 | Structural segmentation of music with fuzzy clustering | 2008 | 2 |
| 17 | 2023 | 2 | |
| 18 | 2021 | 1 |
About Daniel Graves
Daniel Graves is a scholar working on Artificial Intelligence, Signal Processing, Control and Systems Engineering, Computer Vision and Pattern Recognition and Management Science and Operations Research, having authored 18 papers that have together received 445 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (6 papers), Time Series Analysis and Forecasting (4 papers), Advanced Clustering Algorithms Research (3 papers), Fuzzy Logic and Control Systems (3 papers), Music and Audio Processing (2 papers), Neural Networks and Applications (2 papers), Fiscal Policies and Political Economy (1 paper) and Autonomous Vehicle Technology and Safety (1 paper). The work is most often cited by research in Artificial Intelligence (310 citations), Computer Vision and Pattern Recognition (173 citations), Media Technology (51 citations), Signal Processing (52 citations) and Computational Theory and Mathematics (51 citations). Daniel Graves has collaborated with scholars based in Canada, United Kingdom and China. Frequent co-authors include Witold Pedrycz, Xin Xu, Zhenhua Huang, Silviu Pitis, Richard S. Sutton, Jun Luo, Yingxu Wang, Tony Tsai, Haitham Bou Ammar and Henry Leung. Their work appears in journals such as Neurocomputing, Fuzzy Sets and Systems, Adaptive Behavior, Electronics Letters and IEEE Transactions on Cybernetics.
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.