Florence Sèdes

1.3k citations
61 papers · 345 · h-index 11

Impact in

Papers in

Florence Sèdes

52 papers receiving 327 citations

Peers

Florence Sèdes
Comparison fields: 5 of 62
  • Information Systems 180
  • Computer Networks and Communications 141
  • Computer Science Applications 31
  • Signal Processing 61
  • Artificial Intelligence 143
Replace Gabriele Tolomei with:
Gabriele Tolomei Italy
Qingyuan Gong China
Emaad Manzoor United States
Esko Nuutila Finland
Kenji Hatano Japan
Gunnar Kreitz Sweden
Sigalit Ur Israel
Ernesto Diaz-Aviles Germany
Sergey Chernov Finland
Shila Ofek-Koifman Israel
Florence Sèdes relative to Gabriele Tolomei Italy Gabriele Tolomei's profile →
Citations per field
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Gabriele Tolomei · 1×
Citations per year

Countries citing papers authored by Florence Sèdes

Since Specialization
Citations

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

Fields of papers citing papers by Florence Sèdes

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Florence Sèdes, 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 Florence Sèdes Line = papers co-authored together Florence Sèdes links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 61 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201747
2 202226
3 200321
4 201020
5 201919
6 200119
7 199318
8 201317
9 201714
10 201514
11 201014
12 20219
13 20168
14 20076
15 20216
16 20096
17
A fuzzy approach to flexible case-based querying: methodology and experimentation
20025
18 20185
19 20194
20 20244

About Florence Sèdes

Florence Sèdes is a scholar working on Information Systems, Artificial Intelligence, Computer Networks and Communications, Computer Vision and Pattern Recognition and Signal Processing, having authored 61 papers that have together received 345 indexed citations. Recurring topics across this work include Semantic Web and Ontologies (16 papers), Recommender Systems and Techniques (12 papers), Data Management and Algorithms (8 papers), Complex Network Analysis Techniques (8 papers), IoT and Edge/Fog Computing (7 papers), Blockchain Technology Applications and Security (5 papers), Web Data Mining and Analysis (5 papers) and Video Analysis and Summarization (5 papers). The work is most often cited by research in Information Systems (180 citations), Computer Networks and Communications (141 citations), Computer Science Applications (31 citations), Signal Processing (61 citations) and Artificial Intelligence (143 citations). Florence Sèdes has collaborated with scholars based in France, Tunisia and Romania. Frequent co-authors include Ikram Amous, Corinne Amel Zayani, Henri Prade, Aziz Qaroush, André Péninou, Sorina Dumitrescu, Didier Dubois, Didier Dubois, Dieudonné Tchuente and Eyke Hüllermeier. Their work appears in journals such as IEEE Multimedia, Social Network Analysis and Mining, Online Information Review, Data & Knowledge Engineering 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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