Jonathan R. Wells
- Artificial Intelligence top 5%
- Computer Networks and Communications top 10%
- Signal Processing top 10%
- Control and Systems Engineering
- Computer Vision and Pattern Recognition
- Co-authors
- Kai Ming TingFei Tony LiuTharindu BandaragodaDavid AlbrechtYe ZhuTakashi WashioSunil AryalSwee Chuan Tan
- Topics
- Anomaly Detection Techniques and Applications (11 papers)Time Series Analysis and Forecasting (4 papers)Network Security and Intrusion Detection (4 papers)
In The Last Decade
Jonathan R. Wells
16 papers receiving 251 citations
Peers
Comparison fields: 5 of 52
- Artificial Intelligence 216
- Computer Networks and Communications 107
- Signal Processing 74
- Control and Systems Engineering 28
- Computer Vision and Pattern Recognition 28
Countries citing papers authored by Jonathan R. Wells
This map shows the geographic impact of Jonathan R. Wells'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 Jonathan R. Wells with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jonathan R. Wells more than expected).
Fields of papers citing papers by Jonathan R. Wells
This network shows the impact of papers produced by Jonathan R. Wells. 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 Jonathan R. Wells. The network helps show where Jonathan R. Wells may publish in the future.
Co-authorship network of co-authors of Jonathan R. Wells
This figure shows the co-authorship network connecting the top 25 collaborators of Jonathan R. Wells. A scholar is included among the top collaborators of Jonathan R. Wells 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 Jonathan R. Wells. Jonathan R. Wells is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 8 | |
| 2 | 4 | |
| 3 | 4 | |
| 4 | 2 | |
| 5 | 2 | |
| 6 | 1 | |
| 7 | 5 | |
| 8 | 99 | |
| 9 | 26 | |
| 10 | 45 | |
| 11 | 11 | |
| 12 | 8 | |
| 13 | A non-time series approach to vehicle related time series problems | 2 |
| 14 | 1 | |
| 15 | 6 | |
| 16 | 35 |
About Jonathan R. Wells
Jonathan R. Wells is a scholar working on Signal Processing, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 16 papers that have together received 259 indexed citations. Recurring topics across this work include Anomaly Detection Techniques and Applications (11 papers), Time Series Analysis and Forecasting (4 papers) and Network Security and Intrusion Detection (4 papers). The work is most often cited by research in Signal Processing (74 citations), Artificial Intelligence (216 citations) and Computer Networks and Communications (107 citations). Jonathan R. Wells has collaborated with scholars based in Australia, Japan and China. Frequent co-authors include Kai Ming Ting, Fei Tony Liu, Tharindu Bandaragoda, David Albrecht, Ye Zhu, Takashi Washio, Sunil Aryal, Swee Chuan Tan, Shyh Wei Teng and Geoffrey I. Webb. Their work appears in journals such as Pattern Recognition, IEEE Transactions on Knowledge and Data Engineering and Machine Learning.
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.