John J. Prevost

749 citations
34 papers · 475 · h-index 12

Impact in

Papers in

John J. Prevost

32 papers receiving 456 citations

Peers

John J. Prevost
Comparison fields: 5 of 78
  • Computer Networks and Communications 196
  • Information Systems 176
  • Computer Vision and Pattern Recognition 99
  • Artificial Intelligence 105
  • Control and Systems Engineering 61
Replace Gul Hassan Sodhro with:
Gul Hassan Sodhro Pakistan
Manal Abdullah Alohali Saudi Arabia
Shibli Nisar Pakistan
Michael Schukat Ireland
Ching-Han Chen Taiwan
Ting Cao China
Peter Langendörfer Germany
Fuhong Song China
Farzad Samie Germany
John J. Prevost relative to Gul Hassan Sodhro Pakistan Gul Hassan Sodhro's profile →
Citations per field
00.5×1.5×2.4×
Gul Hassan Sodhro · 1×
Citations per year

Countries citing papers authored by John J. Prevost

Since Specialization
Citations

This map shows the geographic impact of John J. Prevost'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 J. Prevost with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John J. Prevost more than expected).

Fields of papers citing papers by John J. Prevost

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John J. Prevost. 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 J. Prevost. The network helps show where John J. Prevost may publish in the future.

Co-authors

The 14 scholars most cited alongside John J. Prevost, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with John J. Prevost Line = papers co-authored together John J. Prevost links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 34 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2011123
2 202347
3 201542
4 201833
5 201824
6 201720
7 201820
8 202018
9 201615
10 201813
11 200512
12 202212
13 202211
14 202010
15 201610
16 20189
17 20139
18 20167
19 20146
20 20205

About John J. Prevost

John J. Prevost is a scholar working on Computer Networks and Communications, Information Systems, Computer Vision and Pattern Recognition, Artificial Intelligence and Cognitive Neuroscience, having authored 34 papers that have together received 475 indexed citations. Recurring topics across this work include Cloud Computing and Resource Management (12 papers), IoT and Edge/Fog Computing (11 papers), Video Surveillance and Tracking Methods (5 papers), Distributed and Parallel Computing Systems (4 papers), Anomaly Detection Techniques and Applications (4 papers), Caching and Content Delivery (3 papers), EEG and Brain-Computer Interfaces (3 papers) and Software System Performance and Reliability (3 papers). The work is most often cited by research in Computer Networks and Communications (196 citations), Information Systems (176 citations), Computer Vision and Pattern Recognition (99 citations), Artificial Intelligence (105 citations) and Control and Systems Engineering (61 citations). John J. Prevost has collaborated with scholars based in United States, Australia and Belgium. Frequent co-authors include Mo Jamshidi, Brian Kelley, Paul Rad, Patrick Benavidez, Fatemeh Afghah, Amanda Fernandez, Abhijit Majumdar, Ho‐Hoon Lee, James Benson and Mehdi Roopaei. Their work appears in journals such as IEEE Access, International Journal of Control, IEEE Transactions on Multimedia, PLoS ONE and IEEE Systems Journal.

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.

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