Mustapha Lebbah
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 10%
- Computer Networks and Communications top 10%
- Signal Processing top 10%
- Information Systems top 10%
- Co-authors
- Hanene AzzagYounès BennaniTarn DuongChristine ProvostZahid HalimShanshan TuMartín SaracenoMuhammad Sulaiman
- Topics
- Advanced Clustering Algorithms Research (21 papers)Neural Networks and Applications (17 papers)Image Retrieval and Classification Techniques (11 papers)
In The Last Decade
Mustapha Lebbah
51 papers receiving 447 citations
Peers
Comparison fields: 5 of 86
- Artificial Intelligence 276
- Computer Vision and Pattern Recognition 135
- Computer Networks and Communications 85
- Signal Processing 79
- Information Systems 75
Countries citing papers authored by Mustapha Lebbah
This map shows the geographic impact of Mustapha Lebbah'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 Mustapha Lebbah with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mustapha Lebbah more than expected).
Fields of papers citing papers by Mustapha Lebbah
This network shows the impact of papers produced by Mustapha Lebbah. 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 Mustapha Lebbah. The network helps show where Mustapha Lebbah may publish in the future.
Co-authorship network of co-authors of Mustapha Lebbah
This figure shows the co-authorship network connecting the top 25 collaborators of Mustapha Lebbah. A scholar is included among the top collaborators of Mustapha Lebbah 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 Mustapha Lebbah. Mustapha Lebbah 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 | 1 | |
| 3 | 1 | |
| 4 | 0 | |
| 5 | 1 | |
| 6 | 1 | |
| 7 | Multi-Coclustering de séries temporelles multivariées. Appkication à la validation de systèmes d'aide à la conduite. | 0 |
| 8 | 4 | |
| 9 | 9 | |
| 10 | 13 | |
| 11 | 36 | |
| 12 | A new Model for Scalable θ-subsumption. | 0 |
| 13 | 4 | |
| 14 | 3 | |
| 15 | 2 | |
| 16 | 2 | |
| 17 | 4 | |
| 18 | 8 | |
| 19 | BeSOM :Bernoullion Self-OrganizingMap | 1 |
| 20 | 28 |
About Mustapha Lebbah
Mustapha Lebbah is a scholar working on Computational Mathematics, Signal Processing and Artificial Intelligence, having authored 60 papers that have together received 468 indexed citations. Recurring topics across this work include Advanced Clustering Algorithms Research (21 papers), Neural Networks and Applications (17 papers) and Image Retrieval and Classification Techniques (11 papers). The work is most often cited by research in Artificial Intelligence (276 citations), Signal Processing (79 citations) and Computer Vision and Pattern Recognition (135 citations). Mustapha Lebbah has collaborated with scholars based in France, Tunisia and Algeria. Frequent co-authors include Hanene Azzag, Younès Bennani, Tarn Duong, Christine Provost, Zahid Halim, Shanshan Tu, Martín Saraceno, Muhammad Sulaiman, Muhammad Waqas and Zaineb Chelly Dagdia. Their work appears in journals such as Neural Networks, Machine Learning and Pattern Recognition Letters.
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.