Ivan Lorencin
Impact in
- Health Informatics top 5%
- Modeling and Simulation top 5%
- COVID-19 epidemiological studies
Papers in
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- AI in cancer detection 8
- Evolutionary Algorithms and Applications 6
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- COVID-19 diagnosis using AI 10
- Radiomics and Machine Learning in Medical Imaging 5
- Co-authors
- Nikola Anđelić (45 shared papers)Zlatan Car (46 shared papers)Vedran Mrzljak (24 shared papers)Sandi Baressi Šegota (37 shared papers)Josip Španjol (5 shared papers)Milan Sága (1 shared paper)Tomislav Ćabov (4 shared papers)Tijana Šušteršič (8 shared papers)
In The Last Decade
Ivan Lorencin
61 papers receiving 841 citations
Peers
Comparison fields: 5 of 126
- Health Informatics 36
- Modeling and Simulation 86
- Radiology, Nuclear Medicine and Imaging 176
- Control and Systems Engineering 146
- Artificial Intelligence 197
Countries citing papers authored by Ivan Lorencin
This map shows the geographic impact of Ivan Lorencin'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 Ivan Lorencin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ivan Lorencin more than expected).
Fields of papers citing papers by Ivan Lorencin
This network shows the impact of papers produced by Ivan Lorencin. 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 Ivan Lorencin. The network helps show where Ivan Lorencin may publish in the future.
Co-authors
The 25 scholars most cited alongside Ivan Lorencin, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 73 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 137 | |
| 2 | 2019 | 105 | |
| 3 | 2019 | 76 | |
| 4 | 2020 | 55 | |
| 5 | 2021 | 35 | |
| 6 | 2021 | 34 | |
| 7 | 2019 | 30 | |
| 8 | 2023 | 26 | |
| 9 | 2019 | 26 | |
| 10 | 2023 | 23 | |
| 11 | 2020 | 23 | |
| 12 | 2019 | 23 | |
| 13 | 2021 | 22 | |
| 14 | 2019 | 22 | |
| 15 | 2022 | 20 | |
| 16 | 2021 | 17 | |
| 17 | 2021 | 14 | |
| 18 | 2020 | 13 | |
| 19 | 2021 | 12 | |
| 20 | 2021 | 11 |
About Ivan Lorencin
Ivan Lorencin is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Mechanical Engineering, Control and Systems Engineering and Electrical and Electronic Engineering, having authored 73 papers that have together received 878 indexed citations. Recurring topics across this work include COVID-19 diagnosis using AI (10 papers), AI in cancer detection (8 papers), Thermodynamic and Exergetic Analyses of Power and Cooling Systems (7 papers), Evolutionary Algorithms and Applications (6 papers), COVID-19 epidemiological studies (6 papers), Fault Detection and Control Systems (5 papers), Advanced Power Generation Technologies (5 papers) and Radiomics and Machine Learning in Medical Imaging (5 papers). The work is most often cited by research in Health Informatics (36 citations), Modeling and Simulation (86 citations), Radiology, Nuclear Medicine and Imaging (176 citations), Control and Systems Engineering (146 citations) and Artificial Intelligence (197 citations). Ivan Lorencin has collaborated with scholars based in Croatia, Serbia and Slovakia. Frequent co-authors include Nikola Anđelić, Zlatan Car, Vedran Mrzljak, Sandi Baressi Šegota, Josip Španjol, Milan Sága, Tomislav Ćabov, Tijana Šušteršič, Nenad Filipović and Paolo Blecich. Their work appears in journals such as Electronics, Applied Sciences, International Journal of Environmental Research and Public Health, Sensors and Biology.
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.