Tom Auld
Impact in
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- Network Security and Intrusion Detection
- Network Traffic and Congestion Control
- Artificial Intelligence top 5%
- Internet Traffic Analysis and Secure E-voting
- Anomaly Detection Techniques and Applications
Papers in
- Finance 2
- Financial Markets and Investment Strategies 2
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- Sports Analytics and Performance 2
- Insurance and Financial Risk Management 1
- Co-authors
- Andrew W. Moore (1 shared paper)Stephen F. Gull (1 shared paper)M. Bridges (1 shared paper)M. P. Hobson (1 shared paper)Oliver Linton (1 shared paper)Oliver B. Linton (1 shared paper)
- Journals
- Monthly Notices of the Royal Astronomical Society (1 paper)International Journal of Forecasting (1 paper)SSRN Electronic Journal (1 paper)IEEE Transactions on Neural Networks (1 paper)
- Partner nations
- United KingdomSouth Sudan
In The Last Decade
Tom Auld
4 papers receiving 384 citations
Tom Auld's Hit Papers
Peers
Comparison fields: 5 of 38
- Computer Networks and Communications 332
- Artificial Intelligence 356
- Signal Processing 116
- Hardware and Architecture 50
- Information Systems 53
Countries citing papers authored by Tom Auld
This map shows the geographic impact of Tom Auld'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 Tom Auld with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tom Auld more than expected).
Fields of papers citing papers by Tom Auld
This network shows the impact of papers produced by Tom Auld. 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 Tom Auld. The network helps show where Tom Auld may publish in the future.
Co-authors
The 6 scholars most cited alongside Tom Auld, 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 | Bayesian Neural Networks for Internet Traffic Classification Hit paper breakdown → | 2007 | 369 |
| 2 | 2008 | 37 | |
| 3 | 2018 | 8 | |
| 4 | 2017 | 1 |
About Tom Auld
Tom Auld is a scholar working on Finance, Economics and Econometrics, Computer Networks and Communications, Astronomy and Astrophysics and Clinical Psychology, having authored 4 papers that have together received 415 indexed citations. Recurring topics across this work include Financial Markets and Investment Strategies (2 papers), Sports Analytics and Performance (2 papers), Insurance and Financial Risk Management (1 paper), Galaxies: Formation, Evolution, Phenomena (1 paper), Cosmology and Gravitation Theories (1 paper), Gambling Behavior and Treatments (1 paper), Network Packet Processing and Optimization (1 paper) and Internet Traffic Analysis and Secure E-voting (1 paper). The work is most often cited by research in Computer Networks and Communications (332 citations), Artificial Intelligence (356 citations), Signal Processing (116 citations), Hardware and Architecture (50 citations) and Information Systems (53 citations). Tom Auld has collaborated with scholars based in United Kingdom and South Sudan. Frequent co-authors include Andrew W. Moore, Stephen F. Gull, M. Bridges, M. P. Hobson, Oliver Linton and Oliver B. Linton. Their work appears in journals such as Monthly Notices of the Royal Astronomical Society, International Journal of Forecasting, SSRN Electronic Journal and IEEE Transactions on Neural Networks.
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.