Hossein Karshenas
- Artificial Intelligence top 5%
- Computational Theory and Mathematics top 5%
- Computer Vision and Pattern Recognition
- Control and Systems Engineering
- Molecular Biology
- Co-authors
- Roberto SantanaConcha BielzaPedro LarrañagaAmin NikanjamAdel Torkaman RahmaniMehrtash HarandiSoumava Kumar RoyKamal Nasrollahi
- Topics
- Bayesian Modeling and Causal Inference (4 papers)Multimodal Machine Learning Applications (4 papers)Domain Adaptation and Few-Shot Learning (4 papers)
- Cited by
- Computational Theory and MathematicsArtificial IntelligenceStatistics, Probability and Uncertainty
- Partner nations
- IranSpainSwitzerland
In The Last Decade
Hossein Karshenas
22 papers receiving 379 citations
Peers
Comparison fields: 5 of 74
- Artificial Intelligence 248
- Computational Theory and Mathematics 126
- Computer Vision and Pattern Recognition 52
- Control and Systems Engineering 44
- Molecular Biology 42
Countries citing papers authored by Hossein Karshenas
This map shows the geographic impact of Hossein Karshenas'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 Hossein Karshenas with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hossein Karshenas more than expected).
Fields of papers citing papers by Hossein Karshenas
This network shows the impact of papers produced by Hossein Karshenas. 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 Hossein Karshenas. The network helps show where Hossein Karshenas may publish in the future.
Co-authorship network of co-authors of Hossein Karshenas
This figure shows the co-authorship network connecting the top 25 collaborators of Hossein Karshenas. A scholar is included among the top collaborators of Hossein Karshenas 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 Hossein Karshenas. Hossein Karshenas is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 1 | |
| 3 | 0 | |
| 4 | 1 | |
| 5 | 8 | |
| 6 | 11 | |
| 7 | 7 | |
| 8 | 10 | |
| 9 | 10 | |
| 10 | 3 | |
| 11 | 5 | |
| 12 | 3 | |
| 13 | 30 | |
| 14 | 1 | |
| 15 | 1 | |
| 16 | 0 | |
| 17 | 0 | |
| 18 | 110 | |
| 19 | An Interval-based Multiobjective Approach to Feature Subset Selection Using Joint Modeling of Objectives and Variables | 2 |
| 20 | 66 |
About Hossein Karshenas
Hossein Karshenas is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Statistics and Probability, having authored 25 papers that have together received 396 indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (4 papers), Multimodal Machine Learning Applications (4 papers) and Domain Adaptation and Few-Shot Learning (4 papers). The work is most often cited by research in Computational Theory and Mathematics (126 citations), Artificial Intelligence (248 citations) and Statistics, Probability and Uncertainty (22 citations). Hossein Karshenas has collaborated with scholars based in Iran, Spain and Switzerland. Frequent co-authors include Roberto Santana, Concha Bielza, Pedro Larrañaga, Amin Nikanjam, Adel Torkaman Rahmani, Mehrtash Harandi, Soumava Kumar Roy, Kamal Nasrollahi, Qingfu Zhang and Thomas B. Moeslund. Their work appears in journals such as PLoS ONE, IEEE Transactions on Geoscience and Remote Sensing and Expert Systems with Applications.
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.