Caetano Traina

4.9k citations
222 papers · 2.2k · h-index 21

Impact in

Papers in

Caetano Traina

203 papers receiving 2.0k citations

Peers

Caetano Traina
Comparison fields: 5 of 133
  • Signal Processing 763
  • Computer Vision and Pattern Recognition 1.0k
  • Artificial Intelligence 825
  • Occupational Therapy 75
  • Information Systems 391
Replace Agma J. M. Traina with:
Agma J. M. Traina Brazil
En Zhu China
Wei Peng United States
Floriana Esposito Italy
Jin Huang China
Sheng‐De Wang Taiwan
Ben Kao Hong Kong
Honghua Dai Australia
Bryan Hooi Singapore
Jyotsna Kumar Mandal India
Caetano Traina relative to Agma J. M. Traina Brazil Agma J. M. Traina's profile →
Citations per field
00.5×1.5×
Agma J. M. Traina · 1×
Citations per year

Countries citing papers authored by Caetano Traina

Since Specialization
Citations

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

Fields of papers citing papers by Caetano Traina

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 222 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2002114
2 2011111
3 2011106
4 201183
5 200276
6 200374
7 201557
8 200657
9 200053
10 200848
11 202045
12 201937
13 200333
14 200030
15 200730
16 201828
17 201825
18 200523
19 201822
20 200922

About Caetano Traina

Caetano Traina is a scholar working on Computer Vision and Pattern Recognition, Signal Processing, Artificial Intelligence, Computer Networks and Communications and Information Systems, having authored 222 papers that have together received 2.2k indexed citations. Recurring topics across this work include Data Management and Algorithms (86 papers), Advanced Image and Video Retrieval Techniques (78 papers), Image Retrieval and Classification Techniques (77 papers), Advanced Database Systems and Queries (36 papers), Data Mining Algorithms and Applications (31 papers), Algorithms and Data Compression (21 papers), Time Series Analysis and Forecasting (18 papers) and Data Visualization and Analytics (17 papers). The work is most often cited by research in Signal Processing (763 citations), Computer Vision and Pattern Recognition (1.0k citations), Artificial Intelligence (825 citations), Occupational Therapy (75 citations) and Information Systems (391 citations). Caetano Traina has collaborated with scholars based in Brazil, United States and Moldova. Frequent co-authors include Agma J. M. Traina, Christos Faloutsos, Paulo Mazzoncini de Azevedo‐Marques, Marcela Xavier Ribeiro, Bernhard Seeger, Humberto Razente, Marcos R. Vieira, Maria Camila N. Barioni, Joaquim Cezar Felipe and Robson L. F. Cordeiro. Their work appears in journals such as Information Systems, Data & Knowledge Engineering, Computer Methods and Programs in Biomedicine, Information Sciences and Computers in Biology and Medicine.

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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