Marko V. Jankovic
- Electrical and Electronic Engineering
- Endocrinology, Diabetes and Metabolism top 10%
- Artificial Intelligence
- Statistics, Probability and Uncertainty top 5%
- Signal Processing top 10%
- Co-authors
- Stavroula MougiakakouSrdjan SrdicZoran RadakovićLia BallyChristoph StettlerHaruo OgawaPeter DiemMasashi Sugiyama
- Topics
- Neural Networks and Applications (11 papers)Blind Source Separation Techniques (8 papers)Multilevel Inverters and Converters (5 papers)
- Cited by
- Statistics, Probability and UncertaintyHealth Information ManagementEndocrinology, Diabetes and Metabolism
- Journals
- IEEE Transactions on Power ElectronicsIEEE Transactions on Energy ConversionIEEE Signal Processing Letters
- Partner nations
- SerbiaJapanSwitzerland
In The Last Decade
Marko V. Jankovic
26 papers receiving 388 citations
Peers
Comparison fields: 5 of 69
- Electrical and Electronic Engineering 162
- Endocrinology, Diabetes and Metabolism 125
- Artificial Intelligence 81
- Statistics, Probability and Uncertainty 78
- Signal Processing 49
Countries citing papers authored by Marko V. Jankovic
This map shows the geographic impact of Marko V. Jankovic'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 Marko V. Jankovic with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marko V. Jankovic more than expected).
Fields of papers citing papers by Marko V. Jankovic
This network shows the impact of papers produced by Marko V. Jankovic. 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 Marko V. Jankovic. The network helps show where Marko V. Jankovic may publish in the future.
Co-authorship network of co-authors of Marko V. Jankovic
This figure shows the co-authorship network connecting the top 25 collaborators of Marko V. Jankovic. A scholar is included among the top collaborators of Marko V. Jankovic 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 Marko V. Jankovic. Marko V. Jankovic is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 10 | |
| 2 | 105 | |
| 3 | 51 | |
| 4 | 8 | |
| 5 | 1 | |
| 6 | 1 | |
| 7 | 5 | |
| 8 | 6 | |
| 9 | A new probabilistic approach to on-line learning in artificial neural networks | 2 |
| 10 | 3 | |
| 11 | 2 | |
| 12 | 15 | |
| 13 | 5 | |
| 14 | 2 | |
| 15 | 4 | |
| 16 | 11 | |
| 17 | 4 | |
| 18 | 1 | |
| 19 | 1 | |
| 20 | 9 |
About Marko V. Jankovic
Marko V. Jankovic is a scholar working on Signal Processing, Artificial Intelligence and Endocrinology, Diabetes and Metabolism, having authored 27 papers that have together received 400 indexed citations. Recurring topics across this work include Neural Networks and Applications (11 papers), Blind Source Separation Techniques (8 papers) and Multilevel Inverters and Converters (5 papers). The work is most often cited by research in Statistics, Probability and Uncertainty (78 citations), Health Information Management (47 citations) and Endocrinology, Diabetes and Metabolism (125 citations). Marko V. Jankovic has collaborated with scholars based in Serbia, Japan and Switzerland. Frequent co-authors include Stavroula Mougiakakou, Srdjan Srdic, Zoran Radaković, Lia Bally, Christoph Stettler, Haruo Ogawa, Peter Diem, Masashi Sugiyama, Branimir Reljin and Hidemitsu Ogawa. Their work appears in journals such as IEEE Transactions on Power Electronics, IEEE Transactions on Energy Conversion and IEEE Signal Processing Letters.
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.