Aura Conci

3.9k citations
179 papers · 2.6k · h-index 27

Impact in

Papers in

Aura Conci

165 papers receiving 2.4k citations

Peers

Aura Conci
Comparison fields: 5 of 172
  • Radiology, Nuclear Medicine and Imaging 992
  • Mechanics of Materials 710
  • Computer Vision and Pattern Recognition 541
  • Health Informatics 32
  • General Dentistry 43
Replace Kuo‐Sheng Cheng with:
Kuo‐Sheng Cheng Taiwan
Jun Shi China
V. Rajinikanth India
Heinz Handels Germany
Anselmo Cardoso de Paiva Brazil
Chee‐Kong Chui Singapore
U. Raghavendra India
Ulaş Bağcı United States
Francesco Amato Italy
J. Shin United States
Aura Conci relative to Kuo‐Sheng Cheng Taiwan Kuo‐Sheng Cheng's profile →
Citations per field
00.5×8.6×
Kuo‐Sheng Cheng · 1×
Citations per year

Countries citing papers authored by Aura Conci

Since Specialization
Citations

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

Fields of papers citing papers by Aura Conci

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 179 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2013224
2 2014145
3 2012133
4 198981
5 201176
6 202173
7 199869
8 201267
9 202057
10 200856
11 201555
12 202049
13 201648
14 201445
15 199042
16 202140
17 202036
18 202133
19 202130
20 201730

About Aura Conci

Aura Conci is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Mechanics of Materials, Artificial Intelligence and Physiology, having authored 179 papers that have together received 2.6k indexed citations. Recurring topics across this work include Infrared Thermography in Medicine (49 papers), Thermography and Photoacoustic Techniques (33 papers), Thermoregulation and physiological responses (16 papers), Medical Image Segmentation Techniques (15 papers), Image Retrieval and Classification Techniques (15 papers), AI in cancer detection (9 papers), Industrial Vision Systems and Defect Detection (8 papers) and Image and Object Detection Techniques (8 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (992 citations), Mechanics of Materials (710 citations), Computer Vision and Pattern Recognition (541 citations), Health Informatics (32 citations) and General Dentistry (43 citations). Aura Conci has collaborated with scholars based in Brazil, Spain and Ecuador. Frequent co-authors include Débora Christina Muchaluat Saade, Ángel Sánchez, Érick Oliveira Rodrigues, Panos Liatsis, Rita de Cássia Fernandes de Lima, Renato Bravo, Flávio Luiz Seixas, Débora C. Muchaluat-Saade, Anselmo Cardoso de Paiva and Marcelo Gattass. Their work appears in journals such as Sensors, Computers in Biology and Medicine, Integrated Computer-Aided Engineering, International Journal for Numerical Methods in Engineering and Signal Processing.

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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