Sven Giesselbach
- Artificial Intelligence top 10%
- Control and Systems Engineering top 10%
- Electrical and Electronic Engineering
- Mechanical Engineering
- Biomedical Engineering
- Co-authors
- Christian BauckhageKatharina BeckhBirgit KirschSebastian MayerRaoul HeeseMichał WalczakRajkumar RamamurthyJannis Schuecker
- Topics
- Topic Modeling (3 papers)Gait Recognition and Analysis (2 papers)Anomaly Detection Techniques and Applications (2 papers)
In The Last Decade
Sven Giesselbach
9 papers receiving 523 citations
Hit Papers
Peers
Comparison fields: 5 of 118
- Artificial Intelligence 201
- Control and Systems Engineering 70
- Electrical and Electronic Engineering 57
- Mechanical Engineering 55
- Biomedical Engineering 53
Countries citing papers authored by Sven Giesselbach
This map shows the geographic impact of Sven Giesselbach'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 Sven Giesselbach with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sven Giesselbach more than expected).
Fields of papers citing papers by Sven Giesselbach
This network shows the impact of papers produced by Sven Giesselbach. 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 Sven Giesselbach. The network helps show where Sven Giesselbach may publish in the future.
Co-authorship network of co-authors of Sven Giesselbach
This figure shows the co-authorship network connecting the top 25 collaborators of Sven Giesselbach. A scholar is included among the top collaborators of Sven Giesselbach 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 Sven Giesselbach. Sven Giesselbach 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 | 6 | |
| 3 | 0 | |
| 4 | 3 | |
| 5 | 17 | |
| 6 | 1 | |
| 7 | Informed Machine Learning - A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systemsbreakdown → | 469 |
| 8 | 41 | |
| 9 | Systematic Comparison of the Influence of Different Data Preprocessing Methods on the Classification of Gait Using Machine Learning. | 1 |
| 10 | 5 |
About Sven Giesselbach
Sven Giesselbach is a scholar working on Health Informatics, Family Practice and Physical Therapy, Sports Therapy and Rehabilitation, having authored 10 papers that have together received 544 indexed citations. Recurring topics across this work include Topic Modeling (3 papers), Gait Recognition and Analysis (2 papers) and Anomaly Detection Techniques and Applications (2 papers). The work is most often cited by research in Health Informatics (15 citations), Artificial Intelligence (201 citations) and Statistical and Nonlinear Physics (46 citations). Sven Giesselbach has collaborated with scholars based in Germany, Egypt and China. Frequent co-authors include Christian Bauckhage, Katharina Beckh, Birgit Kirsch, Sebastian Mayer, Raoul Heese, Michał Walczak, Rajkumar Ramamurthy, Jannis Schuecker, Laura von Rueden and Bogdan Georgiev. Their work appears in journals such as IEEE Transactions on Knowledge and Data Engineering, Computers in Biology and Medicine and IEEE Software.
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.