D. Cabello

1.4k citations
108 papers · 947 · h-index 17

Impact in

Papers in

D. Cabello

97 papers receiving 887 citations

Peers

D. Cabello
Comparison fields: 5 of 104
  • Computer Vision and Pattern Recognition 192
  • Instrumentation 24
  • Media Technology 57
  • Artificial Intelligence 205
  • Cardiology and Cardiovascular Medicine 145
Replace Ghada M. El‐Banby with:
Ghada M. El‐Banby Egypt
Ki‐Doo Kim South Korea
Sheng Li China
Hao Shen Germany
Guoli Wang China
Junliang Liu China
Stamatios Georgoulis Switzerland
Hongqing Liu China
Xiaohua Zhu China
N. Nandhakumar United States
D. Cabello relative to Ghada M. El‐Banby Egypt Ghada M. El‐Banby's profile →
Citations per field
00.5×5.4×
Ghada M. El‐Banby · 1×
Citations per year

Countries citing papers authored by D. Cabello

Since Specialization
Citations

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

Fields of papers citing papers by D. Cabello

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 108 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1998148
2 1989129
3 200137
4 199835
5 200427
6 200326
7 201825
8 199124
9 199823
10 201923
11 201523
12 202122
13 201721
14 200419
15 201219
16 201618
17 199716
18 201613
19 200213
20 200512

About D. Cabello

D. Cabello is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence, Computer Vision and Pattern Recognition, Computer Networks and Communications and Biomedical Engineering, having authored 108 papers that have together received 947 indexed citations. Recurring topics across this work include CCD and CMOS Imaging Sensors (25 papers), Advanced Memory and Neural Computing (18 papers), Neural Networks and Applications (18 papers), Neural Networks Stability and Synchronization (16 papers), Medical Image Segmentation Techniques (11 papers), Advancements in Semiconductor Devices and Circuit Design (9 papers), Image Processing Techniques and Applications (8 papers) and AI in cancer detection (8 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (192 citations), Instrumentation (24 citations), Media Technology (57 citations), Artificial Intelligence (205 citations) and Cardiology and Cardiovascular Medicine (145 citations). D. Cabello has collaborated with scholars based in Spain, Germany and Chile. Frequent co-authors include V.M. Brea, P. López, A. Mosquera, Marı́a J. Carreira, Manuel G. Penedo, R. Ruı́z, Senén Barro, D.L. Vilariño, José Mira and Xosé M. Pardo. Their work appears in journals such as International Journal of Circuit Theory and Applications, Pattern Recognition Letters, IEEE Sensors Journal, Image and Vision Computing and Semiconductor Science and Technology.

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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