Pierre Beauseroy

874 total citations
30 papers, 432 citations indexed

About

Pierre Beauseroy is a scholar working on Artificial Intelligence, Control and Systems Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Pierre Beauseroy has authored 30 papers receiving a total of 432 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 8 papers in Control and Systems Engineering and 8 papers in Computer Vision and Pattern Recognition. Recurrent topics in Pierre Beauseroy's work include Fault Detection and Control Systems (5 papers), Face and Expression Recognition (5 papers) and Neural Networks and Applications (4 papers). Pierre Beauseroy is often cited by papers focused on Fault Detection and Control Systems (5 papers), Face and Expression Recognition (5 papers) and Neural Networks and Applications (4 papers). Pierre Beauseroy collaborates with scholars based in France, Lebanon and Argentina. Pierre Beauseroy's co-authors include Régis Lengelle, Paul Honeiné, Patrick Nader, Patricio Yankilevich, Nicolas Lefèbvre, Didier Maquin, Gilles Mourot, Xiandong Chen, José Ragot and Xiyan He and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, The FASEB Journal and IEEE Transactions on Signal Processing.

In The Last Decade

Pierre Beauseroy

27 papers receiving 418 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Pierre Beauseroy France 11 151 111 82 76 67 30 432
Hamed Afshari Iran 11 183 1.2× 159 1.4× 45 0.5× 103 1.4× 57 0.9× 38 569
Dongyue Chen China 10 277 1.8× 291 2.6× 26 0.3× 102 1.3× 113 1.7× 31 669
Mohammad Taheri Iran 10 243 1.6× 29 0.3× 28 0.3× 49 0.6× 84 1.3× 63 497
Ghodrat Sepidnam Iran 4 198 1.3× 59 0.5× 42 0.5× 92 1.2× 67 1.0× 11 462
Donglin Zhu China 14 298 2.0× 86 0.8× 136 1.7× 98 1.3× 150 2.2× 69 701
Zhikun Wang China 10 212 1.4× 56 0.5× 16 0.2× 106 1.4× 88 1.3× 39 588
Russ Eberhart United States 4 297 2.0× 110 1.0× 53 0.6× 42 0.6× 36 0.5× 6 582
Latha Pemula Germany 1 469 3.1× 85 0.8× 49 0.6× 134 1.8× 155 2.3× 2 622
Peng Shao China 12 130 0.9× 61 0.5× 22 0.3× 33 0.4× 95 1.4× 35 341

Countries citing papers authored by Pierre Beauseroy

Since Specialization
Citations

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

Fields of papers citing papers by Pierre Beauseroy

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pierre Beauseroy

This figure shows the co-authorship network connecting the top 25 collaborators of Pierre Beauseroy. A scholar is included among the top collaborators of Pierre Beauseroy 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 Pierre Beauseroy. Pierre Beauseroy 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.
Beauseroy, Pierre, et al.. (2024). Study of an Expansion Method Based on an Image-Specific Classifier and Multi-Features for Weakly Supervised Semantic Segmentation. SPIRE - Sciences Po Institutional REpository. 402–409.
2.
Birregah, Babiga, et al.. (2023). Missing body measurements prediction in fashion industry: a comparative approach. Fashion and Textiles. 10(1). 5 indexed citations
3.
Beauseroy, Pierre, et al.. (2023). Neural Network-Based Approach for Supervised Nonlinear Feature Selection. SPIRE - Sciences Po Institutional REpository. 431–439.
4.
Bodey, Andrew J., Sébastien Blaise, Béatrice Romier, et al.. (2021). X‐ray microtomography reveals a lattice‐like network within aortic elastic lamellae. The FASEB Journal. 35(10). e21844–e21844. 5 indexed citations
5.
Beauseroy, Pierre, et al.. (2020). Coupled Mass-Spectrometry-Based Lipidomics Machine Learning Approach for Early Detection of Clear Cell Renal Cell Carcinoma. Journal of Proteome Research. 20(1). 841–857. 20 indexed citations
6.
Beauseroy, Pierre, et al.. (2020). Unsupervised adversarial deep domain adaptation method for potato defects classification. Computers and Electronics in Agriculture. 174. 105501–105501. 28 indexed citations
7.
Nikiforov, Igor V., et al.. (2020). Sequential detection of a total instantaneous blockage occurred in a single subassembly of a sodium-cooled fast reactor. Nuclear Engineering and Design. 366. 110733–110733. 8 indexed citations
8.
Beauseroy, Pierre, et al.. (2019). A pan-cancer somatic mutation embedding using autoencoders. BMC Bioinformatics. 20(1). 655–655. 8 indexed citations
9.
Khalil, Mohamad, et al.. (2018). Efficiency and stability of EN-ReliefF, a new method for feature selection. International Journal of Computer Aided Engineering and Technology. 10(3). 320–320. 1 indexed citations
10.
He, Xiyan, et al.. (2015). Dynamic Feature Subspaces Selection for Decision in a Nonstationary Environment. International Journal of Pattern Recognition and Artificial Intelligence. 29(6). 1551009–1551009. 1 indexed citations
11.
Beauseroy, Pierre, et al.. (2014). Operational Metrics to Assess Performances of a Prognosis Function. Application to Lubricant of a Turbofan Engine Over- Consumption Prognosis. PHM Society European Conference. 2(1). 1 indexed citations
12.
Beauseroy, Pierre, et al.. (2014). Degradation prognosis based on a model of Gamma process mixture. PHM Society European Conference. 2(1). 1 indexed citations
13.
Nader, Patrick, Paul Honeiné, & Pierre Beauseroy. (2014). <inline-formula><tex-math notation="TeX">${l_p}$</tex-math></inline-formula>-norms in One-Class Classification for Intrusion Detection in SCADA Systems. IEEE Transactions on Industrial Informatics. 10(4). 2308–2317. 89 indexed citations
14.
Beauseroy, Pierre, et al.. (2014). Elastic strips normalisation model for higher iris recognition performance. IET Biometrics. 3(4). 190–197. 5 indexed citations
15.
Beauseroy, Pierre, et al.. (2012). Real shape inner iris boundary segmentation using active contour without edges. HAL (Le Centre pour la Communication Scientifique Directe). 14–19. 2 indexed citations
16.
Beauseroy, Pierre, et al.. (2009). Gene‐Based Multiclass Cancer Diagnosis with Class‐Selective Rejections. BioMed Research International. 2009(1). 608701–608701. 1 indexed citations
17.
Beauseroy, Pierre, et al.. (2008). Optimal Decision Rule with Class-Selective Rejection and Performance Constraints. IEEE Transactions on Pattern Analysis and Machine Intelligence. 31(11). 2073–2082. 10 indexed citations
18.
Lengelle, Régis, et al.. (2008). A Novel Approach to Real Time Tire-Road Grip and Slip Monitoring. IFAC Proceedings Volumes. 41(2). 7104–7109. 2 indexed citations
19.
Beauseroy, Pierre, et al.. (2007). A Kernel Based Rejection Method For Supervised Classification. Zenodo (CERN European Organization for Nuclear Research). 1(12). 3907–3916. 10 indexed citations
20.
Beauseroy, Pierre, et al.. (2002). Mutual information-based feature extraction on the time-frequency plane. IEEE Transactions on Signal Processing. 50(4). 779–790. 20 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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