Lucas C. Ribas

519 citations
26 papers · 283 indexed · h-index 10

Lucas C. Ribas

26 papers receiving 279 citations

Peers

Lucas C. Ribas
Comparison fields: 5 of 90
  • Modeling and Simulation 25
  • Computer Vision and Pattern Recognition 74
  • Health Informatics 4
  • Media Technology 23
  • Statistical and Nonlinear Physics 31
Replace Leonardo Scabini with:
Leonardo Scabini Brazil
Wouter Lueks Netherlands
Manoj Sharma India
Yufan Huang China
Mahmood Akhtar Australia
Christian E. Schaerer Paraguay
Oleg A. Markelov Russia
Limin Wang China
Shabieh Farwa Pakistan
Jong-Ho Kim South Korea
Lucas C. Ribas relative to Leonardo Scabini Brazil Leonardo Scabini's profile →
Citations per field
00.5×1.5×2.3×
Leonardo Scabini · 1×
Citations per year

Countries citing papers authored by Lucas C. Ribas

Since Specialization
Citations

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

Fields of papers citing papers by Lucas C. Ribas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Lucas C. Ribas. 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 Lucas C. Ribas. The network helps show where Lucas C. Ribas may publish in the future.

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20253
2 20252
3 20251
4 20242
5 20241
6 20241
7 202318
8 20231
9 20226
10 202136
11 20218
12 202046
13 202048
14 202019
15 20194
16 201913
17 201814
18 20187
19 20184
20 20159

About Lucas C. Ribas

Lucas C. Ribas is a scholar working on Media Technology, Computer Vision and Pattern Recognition and Statistical and Nonlinear Physics, having authored 26 papers that have together received 283 indexed citations. Recurring topics across this work include Image Retrieval and Classification Techniques (10 papers), Advanced Image and Video Retrieval Techniques (9 papers), Remote-Sensing Image Classification (5 papers), Generative Adversarial Networks and Image Synthesis (4 papers), Complex Network Analysis Techniques (4 papers), Cellular Automata and Applications (4 papers), Video Analysis and Summarization (3 papers) and Opinion Dynamics and Social Influence (3 papers). The work is most often cited by research in Modeling and Simulation (25 citations), Computer Vision and Pattern Recognition (74 citations) and Health Informatics (4 citations). Lucas C. Ribas has collaborated with scholars based in Brazil, Belgium and Moldova. Frequent co-authors include Odemir Martinez Bruno, Leonardo Scabini, Wesley Nunes Gonçalves, Jarbas Joaci de Mesquita Sá, Osvaldo N. Oliveira, Rabia Riad, Juliana Coatrini Soares, Matias Eliseo Melendez, Andrey Coatrini Soares and Valquíria Cruz Rodrigues. Their work appears in journals such as Expert Systems with Applications, Pattern Recognition and Information Sciences.

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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