Cedric De Boom
- Artificial Intelligence top 10%
- Information Systems
- Computer Vision and Pattern Recognition
- Cognitive Neuroscience
- Signal Processing
- Co-authors
- Bart DhoedtSteven Van CanneytThomas DemeesterTim VerbelenSam LerouxSamantha HansenChing-Wei ChenFilip De Turck
- Topics
- Advanced Neural Network Applications (3 papers)Image and Video Quality Assessment (3 papers)Cutaneous Melanoma Detection and Management (3 papers)
- Partner nations
- BelgiumNetherlandsUnited Kingdom
In The Last Decade
Cedric De Boom
19 papers receiving 213 citations
Peers
Comparison fields: 5 of 68
- Artificial Intelligence 137
- Information Systems 38
- Computer Vision and Pattern Recognition 38
- Cognitive Neuroscience 27
- Signal Processing 25
Countries citing papers authored by Cedric De Boom
This map shows the geographic impact of Cedric De Boom'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 Cedric De Boom with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Cedric De Boom more than expected).
Fields of papers citing papers by Cedric De Boom
This network shows the impact of papers produced by Cedric De Boom. 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 Cedric De Boom. The network helps show where Cedric De Boom may publish in the future.
Co-authorship network of co-authors of Cedric De Boom
This figure shows the co-authorship network connecting the top 25 collaborators of Cedric De Boom. A scholar is included among the top collaborators of Cedric De Boom 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 Cedric De Boom. Cedric De Boom 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 | 2 | |
| 3 | 14 | |
| 4 | 2 | |
| 5 | 4 | |
| 6 | 5 | |
| 7 | Adapted NMFD update procedure for removing double hits in drum mixture decompositions | 1 |
| 8 | 20 | |
| 9 | 5 | |
| 10 | 10 | |
| 11 | 3 | |
| 12 | 1 | |
| 13 | 4 | |
| 14 | 12 | |
| 15 | 2 | |
| 16 | Learning representations for tweets through word embeddings | 2 |
| 17 | 115 | |
| 18 | 9 | |
| 19 | Semantics-driven Event Clustering in Twitter Feeds | 13 |
About Cedric De Boom
Cedric De Boom is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Artificial Intelligence, having authored 19 papers that have together received 227 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (3 papers), Image and Video Quality Assessment (3 papers) and Cutaneous Melanoma Detection and Management (3 papers). The work is most often cited by research in Artificial Intelligence (137 citations), Signal Processing (25 citations) and Computer Vision and Pattern Recognition (38 citations). Cedric De Boom has collaborated with scholars based in Belgium, Netherlands and United Kingdom. Frequent co-authors include Bart Dhoedt, Steven Van Canneyt, Thomas Demeester, Tim Verbelen, Sam Leroux, Samantha Hansen, Ching-Wei Chen, Filip De Turck, Tijl De Bie and Christopher L. Buckley. Their work appears in journals such as IEEE Access, Sensors and Journal of the American Academy of Dermatology.
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.