Davy Preuveneers

3.3k citations
134 papers · 1.7k · h-index 20

Impact in

Papers in

Davy Preuveneers

132 papers receiving 1.6k citations

Peers

Davy Preuveneers
Comparison fields: 5 of 113
  • Computer Networks and Communications 734
  • Information Systems 616
  • Computer Vision and Pattern Recognition 410
  • Signal Processing 206
  • Industrial and Manufacturing Engineering 173
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Hongming Cai China
JaeSeung Song South Korea
Neil Yorke‐Smith Netherlands
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Grammati Pantziou Greece
Shanshan Zhao United States
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Citations per year

Countries citing papers authored by Davy Preuveneers

Since Specialization
Citations

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

Fields of papers citing papers by Davy Preuveneers

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Davy Preuveneers. 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 Davy Preuveneers. The network helps show where Davy Preuveneers may publish in the future.

Co-authors

The 25 scholars most cited alongside Davy Preuveneers, 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 Davy Preuveneers Line = papers co-authored together Davy Preuveneers links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 134 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2018215
2 2017136
3 2019108
4 200798
5 200884
6 202143
7 201938
8 202036
9 201733
10 201833
11 202028
12 201428
13 201526
14 201925
15 201925
16 200823
17 201223
18
Proceedings of the 9th international conference on intelligentenvironments (IE '13)
201322
19
Quality extensions and uncertainty handling for context ontologies
200620
20 202119

About Davy Preuveneers

Davy Preuveneers is a scholar working on Computer Networks and Communications, Information Systems, Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing, having authored 134 papers that have together received 1.7k indexed citations. Recurring topics across this work include Context-Aware Activity Recognition Systems (45 papers), Service-Oriented Architecture and Web Services (18 papers), IoT and Edge/Fog Computing (16 papers), Advanced Malware Detection Techniques (16 papers), User Authentication and Security Systems (14 papers), Adversarial Robustness in Machine Learning (12 papers), Software System Performance and Reliability (11 papers) and Opportunistic and Delay-Tolerant Networks (11 papers). The work is most often cited by research in Computer Networks and Communications (734 citations), Information Systems (616 citations), Computer Vision and Pattern Recognition (410 citations), Signal Processing (206 citations) and Industrial and Manufacturing Engineering (173 citations). Davy Preuveneers has collaborated with scholars based in Belgium, Hungary and United Kingdom. Frequent co-authors include Wouter Joosen, Yolande Berbers, Elisabeth Ilie‐Zudor, Jan Spooren, Ilias Tsingenopoulos, Vera Rimmer, Stefano Chessa, Carles Gómez, Anthony Fleury and George Roussos. Their work appears in journals such as Applied Sciences, Future Internet, Journal of Systems and Software, IEEE Transactions on Information Forensics and Security and Future Generation Computer Systems.

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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