Allou Samé

685 total citations
30 papers, 388 citations indexed

About

Allou Samé is a scholar working on Artificial Intelligence, Signal Processing and Control and Systems Engineering. According to data from OpenAlex, Allou Samé has authored 30 papers receiving a total of 388 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Artificial Intelligence, 9 papers in Signal Processing and 5 papers in Control and Systems Engineering. Recurrent topics in Allou Samé's work include Bayesian Methods and Mixture Models (7 papers), Time Series Analysis and Forecasting (6 papers) and Anomaly Detection Techniques and Applications (6 papers). Allou Samé is often cited by papers focused on Bayesian Methods and Mixture Models (7 papers), Time Series Analysis and Forecasting (6 papers) and Anomaly Detection Techniques and Applications (6 papers). Allou Samé collaborates with scholars based in France, Tanzania and South Korea. Allou Samé's co-authors include Latifa Oukhellou, Gérard Govaert, Patrice Aknin, Faïcel Chamroukhi, Simon Meunier, Kyoungchul Kong, Yacine Amirat, Christophe Ambroise, Loïc Quéval and Weiguang Huo and has published in prestigious journals such as SHILAP Revista de lepidopterología, Applied Energy and International Journal of Hydrogen Energy.

In The Last Decade

Allou Samé

29 papers receiving 376 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Allou Samé France 12 154 119 73 50 46 30 388
Xiaodan Xu China 11 124 0.8× 81 0.7× 64 0.9× 67 1.3× 25 0.5× 35 426
Yuepeng Chen China 11 68 0.4× 98 0.8× 21 0.3× 167 3.3× 12 0.3× 80 450
Xifeng Guo China 10 158 1.0× 318 2.7× 53 0.7× 75 1.5× 20 0.4× 41 510
Hoon Kang South Korea 9 121 0.8× 78 0.7× 10 0.1× 116 2.3× 15 0.3× 34 345
E. A. Badr Lebanon 10 81 0.5× 301 2.5× 145 2.0× 20 0.4× 10 0.2× 21 575
Woohyun Kim South Korea 12 62 0.4× 122 1.0× 102 1.4× 158 3.2× 15 0.3× 25 567
Seungwon Jung South Korea 11 167 1.1× 255 2.1× 45 0.6× 36 0.7× 25 0.5× 16 472
Muddu Madakyaru India 13 78 0.5× 42 0.4× 18 0.2× 490 9.8× 20 0.4× 44 597
Iulia Stamatescu Romania 12 81 0.5× 206 1.7× 60 0.8× 64 1.3× 23 0.5× 56 424
Zhenxi Wang China 9 139 0.9× 131 1.1× 24 0.3× 73 1.5× 7 0.2× 16 376

Countries citing papers authored by Allou Samé

Since Specialization
Citations

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

Fields of papers citing papers by Allou Samé

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Allou Samé

This figure shows the co-authorship network connecting the top 25 collaborators of Allou Samé. A scholar is included among the top collaborators of Allou Samé 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 Allou Samé. Allou Samé 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.
Côme, Étienne, et al.. (2024). Deep Probabilistic Forecasting of Multivariate Count Data With “Sums and Shares” Distributions: A Case Study on Pedestrian Counts in a Multimodal Transport Hub. IEEE Transactions on Intelligent Transportation Systems. 25(11). 15687–15701. 1 indexed citations
2.
Côme, Étienne, et al.. (2024). How Has the Paris Rail Public-Transportation Network Recovered After the COVID-19 Pandemic? Applying a Mixture of Regressions Model. Transportation Research Record Journal of the Transportation Research Board. 2679(3). 330–345.
3.
4.
Samé, Allou, et al.. (2022). Dynamic clustering and modeling of temporal data subject to common regressive effects. Neurocomputing. 500. 217–230. 1 indexed citations
5.
6.
Samé, Allou, et al.. (2021). Online common change-point detection in a set of nonstationary categorical time series. Neurocomputing. 439. 176–196. 2 indexed citations
7.
Meunier, Simon, et al.. (2020). Detection of cleaning interventions on photovoltaic modules with machine learning. Applied Energy. 263. 114642–114642. 39 indexed citations
8.
Attal, Ferhat, Abderrahmane Boubezoul, Allou Samé, Latifa Oukhellou, & Stéphane Espié. (2018). Powered Two-Wheelers Critical Events Detection and Recognition Using Data-Driven Approaches. IEEE Transactions on Intelligent Transportation Systems. 19(12). 4011–4022. 11 indexed citations
9.
Samé, Allou, et al.. (2017). Modeling and clustering water demand patterns from real-world smart meter data. SHILAP Revista de lepidopterología. 10(2). 75–82. 23 indexed citations
10.
Samé, Allou, et al.. (2016). Hourly Solar Irradiance Forecasting Based on Machine Learning Models. 441–446. 32 indexed citations
11.
Samé, Allou & Gérard Govaert. (2016). Segmental dynamic factor analysis for time series of curves. Statistics and Computing. 27(6). 1617–1637. 5 indexed citations
12.
Samé, Allou, et al.. (2016). A variational Expectation–Maximization algorithm for temporal data clustering. Computational Statistics & Data Analysis. 103. 206–228. 9 indexed citations
13.
Samé, Allou, et al.. (2014). Recognition of gait cycle phases using wearable sensors. Robotics and Autonomous Systems. 75. 50–59. 38 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.
Samé, Allou, et al.. (2012). A CUSUM approach for online change-point detection on curve sequences. The European Symposium on Artificial Neural Networks. 3 indexed citations
16.
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
17.
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
18.
Samé, Allou, Latifa Oukhellou, Étienne Côme, & Patrice Aknin. (2007). Mixture-model-based signal denoising. Advances in Data Analysis and Classification. 1(1). 39–51. 3 indexed citations
19.
Samé, Allou, et al.. (2007). Réseaux bayésiens dynamiques à variable exogène continue pour la classification des points singuliers d'une voie ferrée. Revue d intelligence artificielle. 21(3). 353–370. 1 indexed citations
20.
Samé, Allou, Christophe Ambroise, & Gérard Govaert. (2005). A classification EM algorithm for binned data. Computational Statistics & Data Analysis. 51(2). 466–480. 6 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