John F. Gilmore
- Artificial Intelligence top 10%
- Information Systems top 10%
- Computer Vision and Pattern Recognition top 10%
- Control and Systems Engineering
- Sociology and Political Science
- Co-authors
- Mike LoukidesWhitfield DiffieRoss AndersonPeter G. NeumannRonald L. RivestSteven M. BellovinMatt BlazeBruce Schneier
- Topics
- AI-based Problem Solving and Planning (7 papers)Traffic Prediction and Management Techniques (7 papers)Neural Networks and Applications (6 papers)
- Partner nations
- United StatesJapanUnited Kingdom
In The Last Decade
John F. Gilmore
41 papers receiving 319 citations
Peers
Comparison fields: 5 of 70
- Artificial Intelligence 171
- Information Systems 93
- Computer Vision and Pattern Recognition 71
- Control and Systems Engineering 53
- Sociology and Political Science 50
Countries citing papers authored by John F. Gilmore
This map shows the geographic impact of John F. Gilmore'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 John F. Gilmore with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John F. Gilmore more than expected).
Fields of papers citing papers by John F. Gilmore
This network shows the impact of papers produced by John F. Gilmore. 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 John F. Gilmore. The network helps show where John F. Gilmore may publish in the future.
Co-authorship network of co-authors of John F. Gilmore
This figure shows the co-authorship network connecting the top 25 collaborators of John F. Gilmore. A scholar is included among the top collaborators of John F. Gilmore 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 John F. Gilmore. John F. Gilmore is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | HOW RADICAL MEASURES CAN END A CENTURY OF SLAUGHTER ON THE ROADS | 1 |
| 3 | Cracking DES: Secrets of Encryption Research, Wiretap Politics and Chip Design | 47 |
| 4 | 3 | |
| 5 | ATMS UNIVERSAL TRAFFIC OPERATION SIMULATION | 4 |
| 6 | 2 | |
| 7 | 1 | |
| 8 | 1 | |
| 9 | 1 | |
| 10 | 3 | |
| 11 | 12 | |
| 12 | 2 | |
| 13 | Expert system tool evaluation | 1 |
| 14 | A Survey of Expert System Tools. | 12 |
| 15 | Terrain navigation through knowledge-based route planning | 5 |
| 16 | 1 | |
| 17 | 1 | |
| 18 | 1 | |
| 19 | 2 | |
| 20 | 3 |
About John F. Gilmore
John F. Gilmore is a scholar working on Computer Vision and Pattern Recognition, Hardware and Architecture and Building and Construction, having authored 47 papers that have together received 366 indexed citations. Recurring topics across this work include AI-based Problem Solving and Planning (7 papers), Traffic Prediction and Management Techniques (7 papers) and Neural Networks and Applications (6 papers). The work is most often cited by research in Artificial Intelligence (171 citations), Transportation (31 citations) and Information Systems (93 citations). John F. Gilmore has collaborated with scholars based in United States, Japan and United Kingdom. Frequent co-authors include Mike Loukides, Whitfield Diffie, Ross Anderson, Peter G. Neumann, Ronald L. Rivest, Steven M. Bellovin, Matt Blaze, Bruce Schneier, Jeffrey I. Schiller and Mohan M. Trivedi. Their work appears in journals such as Communications of the ACM, Future Generation Computer Systems and Optical Engineering.
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.