Faïcel Chamroukhi

1.6k total citations · 1 hit paper
28 papers, 1.0k citations indexed

About

Faïcel Chamroukhi is a scholar working on Artificial Intelligence, Statistics and Probability and Molecular Biology. According to data from OpenAlex, Faïcel Chamroukhi has authored 28 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Artificial Intelligence, 10 papers in Statistics and Probability and 3 papers in Molecular Biology. Recurrent topics in Faïcel Chamroukhi's work include Bayesian Methods and Mixture Models (18 papers), Statistical Methods and Inference (7 papers) and Statistical Methods and Bayesian Inference (6 papers). Faïcel Chamroukhi is often cited by papers focused on Bayesian Methods and Mixture Models (18 papers), Statistical Methods and Inference (7 papers) and Statistical Methods and Bayesian Inference (6 papers). Faïcel Chamroukhi collaborates with scholars based in France, Australia and Canada. Faïcel Chamroukhi's co-authors include Latifa Oukhellou, Samer Mohammed, Ferhat Attal, Yacine Amirat, Hien D. Nguyen, Yacine Amirat, Allou Samé, Patrice Aknin, Gérard Govaert and Geoffrey J. McLachlan and has published in prestigious journals such as SHILAP Revista de lepidopterología, Sensors and Neurocomputing.

In The Last Decade

Faïcel Chamroukhi

26 papers receiving 990 citations

Hit Papers

Physical Human Activity Recognition Using Wearable Sensors 2015 2026 2018 2022 2015 100 200 300 400 500
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Physical Human Activity Recognition Using Wearable Sensors
    2015 578 citations Samer Mohammed, Faïcel Chamroukhi et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Faïcel Chamroukhi France 11 533 324 312 158 117 28 1.0k
Nuno Pombo Portugal 20 390 0.7× 174 0.5× 279 0.9× 239 1.5× 125 1.1× 89 1.3k
Miguel Damas Spain 18 859 1.6× 425 1.3× 514 1.6× 324 2.1× 264 2.3× 55 1.8k
Uzoma Rita Alo Nigeria 8 478 0.9× 271 0.8× 252 0.8× 209 1.3× 83 0.7× 13 955
Jinwon Lee South Korea 13 297 0.6× 108 0.3× 89 0.3× 145 0.9× 117 1.0× 47 705
Sozo Inoue Japan 17 697 1.3× 296 0.9× 255 0.8× 284 1.8× 171 1.5× 117 1.1k
Xavier Parra Spain 9 1.4k 2.6× 748 2.3× 478 1.5× 481 3.0× 232 2.0× 18 1.9k
Changseok Bae South Korea 14 335 0.6× 167 0.5× 226 0.7× 164 1.0× 134 1.1× 80 876
Elena Gaura United Kingdom 14 301 0.6× 139 0.4× 242 0.8× 366 2.3× 243 2.1× 90 944
Minh Nhut Nguyen Singapore 11 461 0.9× 382 1.2× 220 0.7× 131 0.8× 85 0.7× 27 1000

Countries citing papers authored by Faïcel Chamroukhi

Since Specialization
Citations

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

Fields of papers citing papers by Faïcel Chamroukhi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Faïcel Chamroukhi

This figure shows the co-authorship network connecting the top 25 collaborators of Faïcel Chamroukhi. A scholar is included among the top collaborators of Faïcel Chamroukhi 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 Faïcel Chamroukhi. Faïcel Chamroukhi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Furno, Angelo, et al.. (2026). Federated dynamic modeling and learning for spatiotemporal data forecasting. Neurocomputing. 672. 132712–132712.
2.
Chamroukhi, Faïcel, et al.. (2024). Functional mixtures-of-experts. Statistics and Computing. 34(3). 1 indexed citations
3.
Braunschweig, Bertrand, et al.. (2024). AITA: AI trustworthiness assessment. AI and Ethics. 4(1). 1–3. 4 indexed citations
4.
Chamroukhi, Faïcel, et al.. (2022). Approximation of probability density functions via location-scale finite mixtures in Lebesgue spaces. Communication in Statistics- Theory and Methods. 52(14). 5048–5059. 6 indexed citations
5.
6.
Nguyen, Hien D., et al.. (2022). A non-asymptotic approach for model selection via penalization in high-dimensional mixture of experts models. Electronic Journal of Statistics. 16(2). 3 indexed citations
7.
Nguyen, Hien D., et al.. (2021). Approximations of conditional probability density functions in Lebesgue spaces via mixture of experts models. SHILAP Revista de lepidopterología. 8(1).
8.
Nguyen, Hien D., et al.. (2020). Approximation by finite mixtures of continuous density functions that vanish at infinity. SHILAP Revista de lepidopterología. 7(1). 1750861–1750861. 42 indexed citations
9.
Nguyen, Hien D., Faïcel Chamroukhi, & Florence Forbes. (2019). Approximation results regarding the multiple-output Gaussian gated mixture of linear experts model. Neurocomputing. 366. 208–214. 10 indexed citations
10.
Nguyen, Hien D. & Faïcel Chamroukhi. (2018). Practical and theoretical aspects of mixture‐of‐experts modeling: An overview. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery. 8(4). 16 indexed citations
11.
Chamroukhi, Faïcel, et al.. (2018). Regularized Maximum-Likelihood Estimation of Mixture-of-Experts for Regression and Clustering. 1–8. 5 indexed citations
12.
Chamroukhi, Faïcel. (2016). Robust mixture of experts modeling using thetdistribution. Neural Networks. 79. 20–36. 22 indexed citations
13.
Chamroukhi, Faïcel. (2016). Piecewise Regression Mixture for Simultaneous Functional Data Clustering and Optimal Segmentation. Journal of Classification. 33(3). 374–411. 8 indexed citations
14.
Chamroukhi, Faïcel, Hervé Glotin, & Allou Samé. (2013). Model-based functional mixture discriminant analysis with hidden process regression for curve classification. Neurocomputing. 112. 153–163. 12 indexed citations
15.
Mohammed, Samer, et al.. (2013). An Unsupervised Approach for Automatic Activity Recognition Based on Hidden Markov Model Regression. IEEE Transactions on Automation Science and Engineering. 10(3). 829–835. 126 indexed citations
16.
Chamroukhi, Faïcel, et al.. (2013). Joint segmentation of multivariate time series with hidden process regression for human activity recognition. Neurocomputing. 120. 633–644. 64 indexed citations
17.
Chamroukhi, Faïcel, et al.. (2012). Functional Mixture Discriminant Analysis with hidden process regression for curve classification. The European Symposium on Artificial Neural Networks. 2 indexed citations
18.
Chamroukhi, Faïcel & Hervé Glotin. (2012). Mixture model-based functional discriminant analysis for curve classification. arXiv (Cornell University). 63. 1–8. 2 indexed citations
19.
Chamroukhi, Faïcel, Allou Samé, Gérard Govaert, & Patrice Aknin. (2010). A hidden process regression model for functional data description. Application to curve discrimination. Neurocomputing. 73(7-9). 1210–1221. 31 indexed citations
20.
Chamroukhi, Faïcel, Allou Samé, Gérard Govaert, & Patrice Aknin. (2009). Time series modeling by a regression approach based on a latent process. Neural Networks. 22(5-6). 593–602. 30 indexed citations

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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2026