V. Rapševičius
Impact in
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- Geological Modeling and Analysis
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- Geochemistry and Geologic Mapping
- Data Stream Mining Techniques
- Advanced Clustering Algorithms Research
- Anomaly Detection Techniques and Applications
Papers in
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- Particle Detector Development and Performance 3
- Particle physics theoretical and experimental studies 3
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- Distributed and Parallel Computing Systems 3
- Co-authors
- W. Badgett (1 shared paper)I. Chakaberia (1 shared paper)S. Maruyama (1 shared paper)Mantas Stankevičius (3 shared papers)Sachiko Toda (1 shared paper)U. Behrens (1 shared paper)J. Patrick (1 shared paper)K. Maeshima (1 shared paper)
- Journals
- Nonlinear Analysis Modelling and Control (1 paper)Journal of Physics Conference Series (3 papers)Laba (Lietuvos akademinių bibliotekų direktorių asociacija) (1 paper)
- Partner nations
- LithuaniaUnited StatesSouth Korea
In The Last Decade
V. Rapševičius
4 papers receiving 10 citations
Peers
Comparison fields: 5 of 18
- Geochemistry and Petrology 2
- Artificial Intelligence 8
- Health Information Management 1
- Signal Processing 2
- Information Systems 4
Countries citing papers authored by V. Rapševičius
This map shows the geographic impact of V. Rapševičius'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 V. Rapševičius with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites V. Rapševičius more than expected).
Fields of papers citing papers by V. Rapševičius
This network shows the impact of papers produced by V. Rapševičius. 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 V. Rapševičius. The network helps show where V. Rapševičius may publish in the future.
Co-authors
The 9 scholars most cited alongside V. Rapševičius, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2001 | 5 | |
| 2 | Clustering of descriptive-textual data of Silurian rocks of Lithuania | 2006 | 3 |
| 3 | 2011 | 3 | |
| 4 | 2020 | 1 | |
| 5 | Comparison of Supervised Machine Learning Techniques for CERN CMS Offline Data Certification. | 2018 | 0 |
| 6 | 2017 | 0 |
About V. Rapševičius
V. Rapševičius is a scholar working on Nuclear and High Energy Physics, Computer Networks and Communications, Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems and Management, having authored 6 papers that have together received 12 indexed citations. Recurring topics across this work include Particle Detector Development and Performance (3 papers), Distributed and Parallel Computing Systems (3 papers), Particle physics theoretical and experimental studies (3 papers), Geochemistry and Geologic Mapping (2 papers), Geotechnical and Geomechanical Engineering (1 paper), Parallel Computing and Optimization Techniques (1 paper), Machine Learning and Data Classification (1 paper) and Image Processing and 3D Reconstruction (1 paper). The work is most often cited by research in Geochemistry and Petrology (2 citations), Artificial Intelligence (8 citations), Health Information Management (1 citation), Signal Processing (2 citations) and Information Systems (4 citations). V. Rapševičius has collaborated with scholars based in Lithuania, United States and South Korea. Frequent co-authors include W. Badgett, I. Chakaberia, S. Maruyama, Mantas Stankevičius, Sachiko Toda, U. Behrens, J. Patrick, K. Maeshima and Virginijus Marcinkevičius. Their work appears in journals such as Nonlinear Analysis Modelling and Control, Journal of Physics Conference Series and Laba (Lietuvos akademinių bibliotekų direktorių asociacija).
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.