Marina Velikova

838 total citations
28 papers, 468 citations indexed

About

Marina Velikova is a scholar working on Artificial Intelligence, Control and Systems Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Marina Velikova has authored 28 papers receiving a total of 468 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Artificial Intelligence, 4 papers in Control and Systems Engineering and 4 papers in Computer Vision and Pattern Recognition. Recurrent topics in Marina Velikova's work include Bayesian Modeling and Causal Inference (10 papers), Neural Networks and Applications (4 papers) and Logic, Reasoning, and Knowledge (3 papers). Marina Velikova is often cited by papers focused on Bayesian Modeling and Causal Inference (10 papers), Neural Networks and Applications (4 papers) and Logic, Reasoning, and Knowledge (3 papers). Marina Velikova collaborates with scholars based in Netherlands, United States and Germany. Marina Velikova's co-authors include Hennie Daniels, Peter Lucas, Nico Karssemeijer, Maurice Samulski, Marc E. A. Spaanderman, Arjen Hommersom, Bernhard Lang, Ruben L. Smeets, Ad Feelders and Jacques Verriet and has published in prestigious journals such as Artificial Intelligence, Physics in Medicine and Biology and Computer.

In The Last Decade

Marina Velikova

27 papers receiving 444 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Marina Velikova Netherlands 11 273 62 51 44 38 28 468
Reza Monsefi Iran 15 428 1.6× 112 1.8× 39 0.8× 39 0.9× 62 1.6× 63 797
Thabo Semong Botswana 7 220 0.8× 44 0.7× 17 0.3× 41 0.9× 48 1.3× 16 681
Thabiso Maupong Botswana 8 186 0.7× 35 0.6× 17 0.3× 41 0.9× 41 1.1× 17 695
Elias Zafiropoulos Greece 10 191 0.7× 42 0.7× 22 0.4× 53 1.2× 47 1.2× 15 605
Chiao-Wen Liu Taiwan 10 247 0.9× 62 1.0× 19 0.4× 35 0.8× 34 0.9× 12 486
Bo Thiesson Denmark 11 458 1.7× 42 0.7× 15 0.3× 35 0.8× 65 1.7× 38 726
Ignacio Molina United States 7 199 0.7× 51 0.8× 15 0.3× 28 0.6× 56 1.5× 15 467
Oteng Tabona Botswana 3 179 0.7× 37 0.6× 12 0.2× 41 0.9× 41 1.1× 6 523
Banyatsang Mphago Botswana 4 182 0.7× 33 0.5× 12 0.2× 41 0.9× 38 1.0× 5 524
Francisco Javier Díez Spain 13 445 1.6× 31 0.5× 37 0.7× 113 2.6× 49 1.3× 38 660

Countries citing papers authored by Marina Velikova

Since Specialization
Citations

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

Fields of papers citing papers by Marina Velikova

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Marina Velikova

This figure shows the co-authorship network connecting the top 25 collaborators of Marina Velikova. A scholar is included among the top collaborators of Marina Velikova 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 Marina Velikova. Marina Velikova is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Hommersom, Arjen, et al.. (2015). A new probabilistic constraint logic programming language based on a generalised distribution semantics. Artificial Intelligence. 228. 1–44. 8 indexed citations
2.
Velikova, Marina, et al.. (2014). Prediction of pre-eclampsia by maternal characteristics : A case-controlled validation study of a Bayesian network model for risk identification of pre-eclampsia. TU/e Research Portal. 27. 351–352. 1 indexed citations
3.
Velikova, Marina, et al.. (2014). Smartphone‐based analysis of biochemical tests for health monitoring support at home. Healthcare Technology Letters. 1(3). 92–97. 10 indexed citations
4.
Hommersom, Arjen, et al.. (2013). Inference for a new probabilistic constraint logic. Radboud Repository (Radboud University). 2540–2546. 5 indexed citations
5.
Hommersom, Arjen, et al.. (2013). MoSHCA - my mobile and smart health care assistant. 188–192. 23 indexed citations
6.
Velikova, Marina, et al.. (2013). Learning Bayesian networks for clinical time series analysis. Journal of Biomedical Informatics. 48. 94–105. 34 indexed citations
7.
Velikova, Marina, et al.. (2013). A Decision Support Model for Uncertainty Reasoning in Safety and Security Tasks. 663–668. 6 indexed citations
8.
Velikova, Marina, Peter Lucas, Maurice Samulski, & Nico Karssemeijer. (2013). On the interplay of machine learning and background knowledge in image interpretation by Bayesian networks. Artificial Intelligence in Medicine. 57(1). 73–86. 29 indexed citations
9.
Velikova, Marina, et al.. (2013). Exploiting causal functional relationships in Bayesian network modelling for personalised healthcare. International Journal of Approximate Reasoning. 55(1). 59–73. 52 indexed citations
10.
Velikova, Marina, et al.. (2012). A Probabilistic Logic-based Model for Fusing Attribute Information of Objects Under Surveillance. Radboud Repository (Radboud University). 3 indexed citations
11.
Velikova, Marina, Peter Lucas, Maurice Samulski, & Nico Karssemeijer. (2012). A probabilistic framework for image information fusion with an application to mammographic analysis. Medical Image Analysis. 16(4). 865–875. 23 indexed citations
12.
Velikova, Marina, et al.. (2010). Using Local Context Information to Improve Automatic Mammographic Mass Detection. Studies in health technology and informatics. 160(Pt 2). 1291–5.
13.
Daniels, Hennie & Marina Velikova. (2010). Monotone and Partially Monotone Neural Networks. IEEE Transactions on Neural Networks. 21(6). 906–917. 119 indexed citations
14.
Velikova, Marina, Maurice Samulski, Peter Lucas, & Nico Karssemeijer. (2009). Improved mammographic CAD performance using multi-view information: a Bayesian network framework. Physics in Medicine and Biology. 54(5). 1131–1147. 40 indexed citations
15.
Velikova, Marina, et al.. (2009). Comparison of universal approximators incorporating partial monotonicity by structure. Neural Networks. 23(4). 471–475. 15 indexed citations
16.
Velikova, Marina. (2008). Monotone Prediction Models in Data Mining. Research portal (Tilburg University). 4 indexed citations
17.
Velikova, Marina, Hennie Daniels, & Ad Feelders. (2007). Solving Partially Monotone Problems With Neural Networks. Data Archiving and Networked Services (DANS). 12. 82–87. 4 indexed citations
18.
Feelders, Ad, Marina Velikova, & Hennie Daniels. (2006). Two polynomial algorithms for relabeling non-monotone data. Utrecht University Repository (Utrecht University). 3 indexed citations
19.
Daniels, Hennie & Marina Velikova. (2006). Derivation of monotone decision models from noisy data. IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews). 36(5). 705–710. 35 indexed citations
20.
Velikova, Marina & Hennie Daniels. (2004). Decision trees for monotone price models. Computational Management Science. 1(3-4). 231–244. 14 indexed citations

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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