Mariofanna Milanova

208 total papers · 6.3k total citations
99 papers, 698 citations indexed

About

Mariofanna Milanova is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Mariofanna Milanova has authored 99 papers receiving a total of 698 indexed citations (citations by other indexed papers that have themselves been cited), including 46 papers in Computer Vision and Pattern Recognition, 26 papers in Artificial Intelligence and 11 papers in Signal Processing. Recurrent topics in Mariofanna Milanova's work include Neural Networks and Applications (14 papers), Advanced Data Compression Techniques (11 papers) and Image and Signal Denoising Methods (9 papers). Mariofanna Milanova is often cited by papers focused on Neural Networks and Applications (14 papers), Advanced Data Compression Techniques (11 papers) and Image and Signal Denoising Methods (9 papers). Mariofanna Milanova collaborates with scholars based in United States, Bulgaria and Portugal. Mariofanna Milanova's co-authors include Ranganathan Kumar, Tomasz G. Smolinski, Roumen Kountchev, D. Todorovsky, Pétia Georgieva, Stuart H. Rubin, Vladimir Todorov, Aboul Ella Hassanien, Nikolay Sirakov and Barbara G. Ryder and has published in prestigious journals such as SHILAP Revista de lepidopterología, Applied Physics Letters and Annals of the New York Academy of Sciences.

In The Last Decade

Mariofanna Milanova

89 papers receiving 659 citations

Author Peers

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

Author						Papers	Cites
Mariofanna Milanova	160	126	94	78	71	99	698
Jie Yu	324 2.0×	177 1.4×	55 0.6×	59 0.8×	23 0.3×	43	701
Jianjun Wang	102 0.6×	116 0.9×	51 0.5×	52 0.7×	35 0.5×	50	603
Jin Gou	84 0.5×	251 2.0×	37 0.4×	41 0.5×	49 0.7×	58	591
Bo‐Yeong Kang	135 0.8×	197 1.6×	35 0.4×	45 0.6×	23 0.3×	55	611
Yuan Guo	94 0.6×	155 1.2×	62 0.7×	26 0.3×	106 1.5×	67	681
Amit Banerjee	81 0.5×	137 1.1×	67 0.7×	103 1.3×	65 0.9×	58	569
Ming Zhang	105 0.7×	48 0.4×	66 0.7×	68 0.9×	123 1.7×	74	740
Xuan Xia	87 0.5×	175 1.4×	97 1.0×	53 0.7×	79 1.1×	59	687
Dong Wang	85 0.5×	51 0.4×	77 0.8×	120 1.5×	127 1.8×	71	624
Xue Wang	219 1.4×	129 1.0×	54 0.6×	14 0.2×	48 0.7×	99	727

Countries citing papers authored by Mariofanna Milanova

Since Specialization

Citations

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

Fields of papers citing papers by Mariofanna Milanova

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mariofanna Milanova

This figure shows the co-authorship network connecting the top 25 collaborators of Mariofanna Milanova. A scholar is included among the top collaborators of Mariofanna Milanova 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 Mariofanna Milanova. Mariofanna Milanova 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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