Maciej Kusy
- Artificial Intelligence top 10%
- Biomedical Engineering
- Control and Systems Engineering top 10%
- Mechanics of Materials
- Electrical and Electronic Engineering
- Co-authors
- Piotr A. KowalskiJacek KluskaBogdan ObrzutMarek JaszczurLeszek PetrykaRobert HanusMarcin ZychAndrzej Semczuk
- Topics
- Neural Networks and Applications (12 papers)Fault Detection and Control Systems (6 papers)Machine Learning and Data Classification (6 papers)
- Journals
- SHILAP Revista de lepidopterologíaInformation SciencesIEEE Transactions on Neural Networks and Learning Systems
- Partner nations
- PolandIranUnited Kingdom
In The Last Decade
Maciej Kusy
25 papers receiving 481 citations
Peers
Comparison fields: 5 of 98
- Artificial Intelligence 188
- Biomedical Engineering 91
- Control and Systems Engineering 88
- Mechanics of Materials 71
- Electrical and Electronic Engineering 57
Countries citing papers authored by Maciej Kusy
This map shows the geographic impact of Maciej Kusy'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 Maciej Kusy with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maciej Kusy more than expected).
Fields of papers citing papers by Maciej Kusy
This network shows the impact of papers produced by Maciej Kusy. 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 Maciej Kusy. The network helps show where Maciej Kusy may publish in the future.
Co-authorship network of co-authors of Maciej Kusy
This figure shows the co-authorship network connecting the top 25 collaborators of Maciej Kusy. A scholar is included among the top collaborators of Maciej Kusy 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 Maciej Kusy. Maciej Kusy 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 | 2 | |
| 3 | 1 | |
| 4 | 2 | |
| 5 | 11 | |
| 6 | 8 | |
| 7 | 19 | |
| 8 | 5 | |
| 9 | 3 | |
| 10 | 7 | |
| 11 | 2 | |
| 12 | 14 | |
| 13 | 43 | |
| 14 | 5 | |
| 15 | 47 | |
| 16 | 16 | |
| 17 | 33 | |
| 18 | 66 | |
| 19 | 47 | |
| 20 | 2 |
About Maciej Kusy
Maciej Kusy is a scholar working on Health Information Management, Artificial Intelligence and Obstetrics and Gynecology, having authored 25 papers that have together received 496 indexed citations. Recurring topics across this work include Neural Networks and Applications (12 papers), Fault Detection and Control Systems (6 papers) and Machine Learning and Data Classification (6 papers). The work is most often cited by research in Health Informatics (11 citations), Artificial Intelligence (188 citations) and Health Information Management (26 citations). Maciej Kusy has collaborated with scholars based in Poland, Iran and United Kingdom. Frequent co-authors include Piotr A. Kowalski, Jacek Kluska, Bogdan Obrzut, Marek Jaszczur, Leszek Petryka, Robert Hanus, Marcin Zych, Andrzej Semczuk, Tomasz Żabiński and Szymon Łukasik. Their work appears in journals such as SHILAP Revista de lepidopterología, Information Sciences and IEEE Transactions on Neural Networks and Learning Systems.
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.