László Kozma
- Genetics top 10%
- Endocrinology, Diabetes and Metabolism top 10%
- Immunology
- Pathology and Forensic Medicine
- Computer Vision and Pattern Recognition
- Co-authors
- V. StenszkyNadir R. FaridJ. C. BearM.F. FreckerArto KlamiSamuel KaskiL. BarthaFerenc Juhász
- Topics
- Diabetes and associated disorders (8 papers)T-cell and B-cell Immunology (7 papers)Algorithms and Data Compression (5 papers)
In The Last Decade
László Kozma
45 papers receiving 469 citations
Peers
Comparison fields: 5 of 105
- Genetics 190
- Endocrinology, Diabetes and Metabolism 181
- Immunology 148
- Pathology and Forensic Medicine 77
- Computer Vision and Pattern Recognition 46
Countries citing papers authored by László Kozma
This map shows the geographic impact of László Kozma'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 László Kozma with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites László Kozma more than expected).
Fields of papers citing papers by László Kozma
This network shows the impact of papers produced by László Kozma. 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 László Kozma. The network helps show where László Kozma may publish in the future.
Co-authorship network of co-authors of László Kozma
This figure shows the co-authorship network connecting the top 25 collaborators of László Kozma. A scholar is included among the top collaborators of László Kozma 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 László Kozma. László Kozma is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 2 | |
| 4 | 1 | |
| 5 | 2 | |
| 6 | 2 | |
| 7 | 6 | |
| 8 | k Nearest Neighbors algorithm (kNN) | 32 |
| 9 | Synthesis of the synchronization of general pipeline systems | 1 |
| 10 | 7 | |
| 11 | 1 | |
| 12 | 23 | |
| 13 | The association of IgG heavy-chain allotypes (Gm) with Graves' disease in Hungary. | 6 |
| 14 | 4 | |
| 15 | Determination of density sums by means of digital photometry with a 4 D-type densitometer and the Densitron system | 1 |
| 16 | 1 | |
| 17 | 2 | |
| 18 | 3 | |
| 19 | 1 | |
| 20 | 8 |
About László Kozma
László Kozma is a scholar working on Theoretical Computer Science, Immunology and Computer Graphics and Computer-Aided Design, having authored 51 papers that have together received 504 indexed citations. Recurring topics across this work include Diabetes and associated disorders (8 papers), T-cell and B-cell Immunology (7 papers) and Algorithms and Data Compression (5 papers). The work is most often cited by research in Endocrinology, Diabetes and Metabolism (181 citations), Immunology (148 citations) and Genetics (190 citations). László Kozma has collaborated with scholars based in Hungary, Canada and Germany. Frequent co-authors include V. Stenszky, Nadir R. Farid, J. C. Bear, M.F. Frecker, Arto Klami, Samuel Kaski, L. Bartha, Ferenc Juhász, G Szegedi and Tapani Raiko. Their work appears in journals such as The Journal of Clinical Endocrinology & Metabolism, Cancer and Clinical Endocrinology.
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.