Miloš Brajović

120 total papers · 844 total citations
64 papers, 486 citations indexed

About

Miloš Brajović is a scholar working on Computer Vision and Pattern Recognition, Computational Mechanics and Biomedical Engineering. According to data from OpenAlex, Miloš Brajović has authored 64 papers receiving a total of 486 indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Computer Vision and Pattern Recognition, 29 papers in Computational Mechanics and 17 papers in Biomedical Engineering. Recurrent topics in Miloš Brajović's work include Image and Signal Denoising Methods (28 papers), Sparse and Compressive Sensing Techniques (27 papers) and Advanced Graph Neural Networks (12 papers). Miloš Brajović is often cited by papers focused on Image and Signal Denoising Methods (28 papers), Sparse and Compressive Sensing Techniques (27 papers) and Advanced Graph Neural Networks (12 papers). Miloš Brajović collaborates with scholars based in Montenegro, United Kingdom and France. Miloš Brajović's co-authors include Miloš Daković, Ljubiša Stanković, Danilo P. Mandic, Irena Orović, Srdjan Stanković, A.G. Constantinides, Shengxi Li, Vesna Popović–Bugarin, Igor Djurović and Slobodan Djukanović and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Access and IEEE Transactions on Aerospace and Electronic Systems.

In The Last Decade

Miloš Brajović

59 papers receiving 479 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Miloš Brajović 156 142 137 105 99 64 486
Luis F. Chaparro 162 1.0× 121 0.9× 172 1.3× 59 0.6× 120 1.2× 46 529
Allan Martins 102 0.7× 119 0.8× 154 1.1× 173 1.6× 29 0.3× 57 532
M.A. Masnadi-Shirazi 70 0.4× 158 1.1× 110 0.8× 130 1.2× 80 0.8× 39 536
E.I. Plotkin 174 1.1× 96 0.7× 131 1.0× 61 0.6× 39 0.4× 84 446
L.F. Chaparro 145 0.9× 41 0.3× 178 1.3× 57 0.5× 39 0.4× 67 456
G.D. Cain 121 0.8× 223 1.6× 302 2.2× 23 0.2× 85 0.9× 58 463
Pitikhate Sooraksa 123 0.8× 36 0.3× 79 0.6× 94 0.9× 55 0.6× 84 495
Wei Xue 111 0.7× 92 0.6× 214 1.6× 146 1.4× 33 0.3× 62 449
Marci Hoffman 80 0.5× 99 0.7× 157 1.1× 101 1.0× 56 0.6× 50 476
Masao Yamagishi 132 0.8× 256 1.8× 156 1.1× 72 0.7× 27 0.3× 49 421

Countries citing papers authored by Miloš Brajović

Since Specialization
Citations

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

Fields of papers citing papers by Miloš Brajović

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Miloš Brajović

This figure shows the co-authorship network connecting the top 25 collaborators of Miloš Brajović. A scholar is included among the top collaborators of Miloš Brajović based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Miloš Brajović. Miloš Brajović is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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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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