Jakub Siłka
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 5%
- Radiology, Nuclear Medicine and Imaging top 10%
- Neurology top 10%
- Computer Networks and Communications top 10%
- Co-authors
- Marcin WoźniakMichał WieczorekMubarak AlrashoudMohammad Mehedi HassanSahil GargDawid PołapZongwen BaiQiao Ke
- Topics
- COVID-19 diagnosis using AI (3 papers)Anomaly Detection Techniques and Applications (3 papers)COVID-19 epidemiological studies (2 papers)
- Partner nations
- PolandSaudi ArabiaNetherlands
In The Last Decade
Jakub Siłka
12 papers receiving 845 citations
Hit Papers
Peers
Comparison fields: 5 of 128
- Computer Vision and Pattern Recognition 323
- Artificial Intelligence 305
- Radiology, Nuclear Medicine and Imaging 154
- Neurology 106
- Computer Networks and Communications 74
Countries citing papers authored by Jakub Siłka
This map shows the geographic impact of Jakub Siłka'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 Jakub Siłka with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jakub Siłka more than expected).
Fields of papers citing papers by Jakub Siłka
This network shows the impact of papers produced by Jakub Siłka. 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 Jakub Siłka. The network helps show where Jakub Siłka may publish in the future.
Co-authorship network of co-authors of Jakub Siłka
This figure shows the co-authorship network connecting the top 25 collaborators of Jakub Siłka. A scholar is included among the top collaborators of Jakub Siłka 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 Jakub Siłka. Jakub Siłka is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 57 | |
| 3 | BiLSTM deep neural network model for imbalanced medical data of IoT systemsbreakdown → | 105 |
| 4 | 36 | |
| 5 | 29 | |
| 6 | 56 | |
| 7 | 109 | |
| 8 | 178 | |
| 9 | 32 | |
| 10 | 29 | |
| 11 | 108 | |
| 12 | 0 | |
| 13 | 51 | |
| 14 | 86 |
About Jakub Siłka
Jakub Siłka is a scholar working on Modeling and Simulation, Software and Computer Vision and Pattern Recognition, having authored 14 papers that have together received 876 indexed citations. Recurring topics across this work include COVID-19 diagnosis using AI (3 papers), Anomaly Detection Techniques and Applications (3 papers) and COVID-19 epidemiological studies (2 papers). The work is most often cited by research in Modeling and Simulation (74 citations), Computer Vision and Pattern Recognition (323 citations) and Neurology (106 citations). Jakub Siłka has collaborated with scholars based in Poland, Saudi Arabia and Netherlands. Frequent co-authors include Marcin Woźniak, Michał Wieczorek, Mubarak Alrashoud, Mohammad Mehedi Hassan, Sahil Garg, Dawid Połap, Zongwen Bai, Qiao Ke and Robertas Damaševičius. Their work appears in journals such as SHILAP Revista de lepidopterología, PLoS ONE and Sensors.
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.