Fabrice Muhlenbach
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition
- Molecular Biology
- Information Systems
- Signal Processing
- Co-authors
- Stéphane LallichDjamel A. ZighedRicco RakotomalalaAlexis BuettgenJean-Louis DessallesPierre MaretChristo El MorrEnakshi Dua
- Topics
- Topic Modeling (3 papers)Data Mining Algorithms and Applications (3 papers)Machine Learning and Algorithms (2 papers)
- Partner nations
- FranceCanadaUnited States
In The Last Decade
Fabrice Muhlenbach
12 papers receiving 137 citations
Peers
Comparison fields: 5 of 67
- Artificial Intelligence 98
- Computer Vision and Pattern Recognition 38
- Molecular Biology 14
- Information Systems 12
- Signal Processing 6
Countries citing papers authored by Fabrice Muhlenbach
This map shows the geographic impact of Fabrice Muhlenbach'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 Fabrice Muhlenbach with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fabrice Muhlenbach more than expected).
Fields of papers citing papers by Fabrice Muhlenbach
This network shows the impact of papers produced by Fabrice Muhlenbach. 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 Fabrice Muhlenbach. The network helps show where Fabrice Muhlenbach may publish in the future.
Co-authorship network of co-authors of Fabrice Muhlenbach
This figure shows the co-authorship network connecting the top 25 collaborators of Fabrice Muhlenbach. A scholar is included among the top collaborators of Fabrice Muhlenbach 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 Fabrice Muhlenbach. Fabrice Muhlenbach is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 10 | |
| 3 | 4 | |
| 4 | 8 | |
| 5 | 3 | |
| 6 | Computing the Semantic Relatedness of Music Genre using Semantic Web Data. | 0 |
| 7 | 1 | |
| 8 | 1 | |
| 9 | 10 | |
| 10 | 6 | |
| 11 | 88 | |
| 12 | Traitement des exemples atypiques en apprentissage par la régression. | 1 |
| 13 | 7 | |
| 14 | UNIVERSITE PIERRE ET MARIE CURIE - PARIS 6 ECOLE DES HAUTES ETUDES EN SCIENCES SOCIALES UNIVERSITE PAUL SABATIER - TOULOUSE 3 ECOLE POLYTECHNIQUE | 1 |
About Fabrice Muhlenbach
Fabrice Muhlenbach is a scholar working on Signal Processing, Artificial Intelligence and Information Systems, having authored 14 papers that have together received 141 indexed citations. Recurring topics across this work include Topic Modeling (3 papers), Data Mining Algorithms and Applications (3 papers) and Machine Learning and Algorithms (2 papers). The work is most often cited by research in Artificial Intelligence (98 citations), Health Informatics (4 citations) and Computer Vision and Pattern Recognition (38 citations). Fabrice Muhlenbach has collaborated with scholars based in France, Canada and United States. Frequent co-authors include Stéphane Lallich, Djamel A. Zighed, Ricco Rakotomalala, Alexis Buettgen, Jean-Louis Dessalles, Pierre Maret, Christo El Morr, Enakshi Dua, Guillaume Lopez and Dennis Diefenbach. Their work appears in journals such as Pattern Recognition, Neurocomputing and Journal of Intelligent Information 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.