Alain Appriou

563 citations
16 papers · 239 indexed · h-index 9

Alain Appriou

14 papers receiving 218 citations

Peers

Alain Appriou
Comparison fields: 5 of 53
  • Management Science and Operations Research 61
  • Artificial Intelligence 151
  • Media Technology 31
  • Signal Processing 25
  • Statistics and Probability 17
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Citations per year

Countries citing papers authored by Alain Appriou

Since Specialization
Citations

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

Fields of papers citing papers by Alain Appriou

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 19 scholars most cited alongside Alain Appriou, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Alain Appriou Line = papers co-authored together Alain Appriou links everyone, so they are left out of the graph.

All Works

16 of 16 papers shown
#Work
1
Uncertainty Theories and Multisensor Data Fusion
20144
2 20148
3 20070
4
01 - Approche générique de la gestion de l’incertain dans les processus de fusion multisenseur
20052
5 20028
6 20029
7 200185
8 200113
9 20017
10 200138
11 200019
12 199829
13 19976
14
Utilisation de la théorie de dempster-shafer pour la fusion d'informations
19952
15
Formulation et traitement de l'incertain en analyse multi-senseurs
19938
16
Proc´edure d’aide `a la d´ecision multi-informateurs. Application `a la classification multi-capteurs de cibles
19881

About Alain Appriou

Alain Appriou is a scholar working on Media Technology, Management Science and Operations Research and Artificial Intelligence, having authored 16 papers that have together received 239 indexed citations. Recurring topics across this work include Target Tracking and Data Fusion in Sensor Networks (7 papers), Multi-Criteria Decision Making (4 papers), Remote-Sensing Image Classification (4 papers), Distributed Sensor Networks and Detection Algorithms (3 papers), Bayesian Modeling and Causal Inference (2 papers), Advanced Data Processing Techniques (1 paper), Risk and Safety Analysis (1 paper) and Fault Detection and Control Systems (1 paper). The work is most often cited by research in Management Science and Operations Research (61 citations), Artificial Intelligence (151 citations) and Media Technology (31 citations). Alain Appriou has collaborated with scholars based in France, Netherlands and Sweden. Frequent co-authors include Xavier Briottet, Sophie Fabre, Wojciech Pieczynski, H. Prade, Philippe Besnard, Alessandro Saffiotti, Michel Grabisch, Anthony Hunter, Roger Cooke and F. Cuppens. Their work appears in journals such as Aerospace Science and Technology, International Journal of Intelligent Systems, International Journal of Approximate Reasoning, Information Fusion and International Journal of Uncertainty Fuzziness and Knowledge-Based 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.

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