R.M. Goodman
Impact in
- Insect Science top 1%
- Insect Pheromone Research and Control
- Artificial Intelligence top 1%
- Neural Networks and Applications
- Fuzzy Logic and Control Systems
Papers in
-
- Neural Networks and Applications 23
- Coding theory and cryptography 9
- Algorithms and Data Compression 9
- Machine Learning and Algorithms 7
-
- CCD and CMOS Imaging Sensors 7
- Co-authors
- Padhraic Smyth (15 shared papers)Alcherio Martinoli (4 shared papers)A.T. Hayes (3 shared papers)Changhoon Lee (2 shared papers)John Kim (2 shared papers)Tzi‐Dar Chiueh (6 shared papers)Charles M. Higgins (5 shared papers)J.M. Allman (1 shared paper)
- Journals
- IEEE Transactions on Information Theory (6 papers)Neural Computation (2 papers)Electronics Letters (2 papers)IEEE Transactions on Computers (2 papers)BioScience (1 paper)
- Partner nations
- United StatesUnited KingdomTaiwan
In The Last Decade
R.M. Goodman
77 papers receiving 2.2k citations
Peers
Comparison fields: 5 of 121
- Insect Science 439
- Artificial Intelligence 844
- Computer Vision and Pattern Recognition 376
- Computer Networks and Communications 380
- Computational Theory and Mathematics 237
Countries citing papers authored by R.M. Goodman
This map shows the geographic impact of R.M. Goodman'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 R.M. Goodman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites R.M. Goodman more than expected).
Fields of papers citing papers by R.M. Goodman
This network shows the impact of papers produced by R.M. Goodman. 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 R.M. Goodman. The network helps show where R.M. Goodman may publish in the future.
Co-authors
The 25 scholars most cited alongside R.M. Goodman, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 90 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2002 | 347 | |
| 2 | 1997 | 234 | |
| 3 | 1992 | 203 | |
| 4 | Rule Induction Using Information Theory. | 1991 | 121 |
| 5 | 1991 | 111 | |
| 6 | 1993 | 92 | |
| 7 | 2003 | 90 | |
| 8 | 2002 | 81 | |
| 9 | 1991 | 77 | |
| 10 | 2000 | 63 | |
| 11 | 2002 | 62 | |
| 12 | 1988 | 53 | |
| 13 | 1992 | 52 | |
| 14 | 1993 | 46 | |
| 15 | 1988 | 45 | |
| 16 | 1994 | 43 | |
| 17 | 2002 | 41 | |
| 18 | 1994 | 41 | |
| 19 | 1994 | 35 | |
| 20 | 2002 | 33 |
About R.M. Goodman
R.M. Goodman is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering, Computer Networks and Communications, Computer Vision and Pattern Recognition and Computational Mechanics, having authored 90 papers that have together received 2.4k indexed citations. Recurring topics across this work include Neural Networks and Applications (23 papers), Error Correcting Code Techniques (12 papers), Coding theory and cryptography (9 papers), Algorithms and Data Compression (9 papers), Machine Learning and Algorithms (7 papers), CCD and CMOS Imaging Sensors (7 papers), Fluid Dynamics and Turbulent Flows (6 papers) and Image Retrieval and Classification Techniques (6 papers). The work is most often cited by research in Insect Science (439 citations), Artificial Intelligence (844 citations), Computer Vision and Pattern Recognition (376 citations), Computer Networks and Communications (380 citations) and Computational Theory and Mathematics (237 citations). R.M. Goodman has collaborated with scholars based in United States, United Kingdom and Taiwan. Frequent co-authors include Padhraic Smyth, Alcherio Martinoli, A.T. Hayes, Changhoon Lee, John Kim, Tzi‐Dar Chiueh, Charles M. Higgins, J.M. Allman, Andrew Moore and John W. V. Miller. Their work appears in journals such as IEEE Transactions on Information Theory, Neural Computation, Electronics Letters, IEEE Transactions on Computers and BioScience.
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.