Daniel Braun
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering
- Information Systems
- Radiology, Nuclear Medicine and Imaging
- Co-authors
- Florian MatthesFriedhelm SchwenkerDavid S. FischerAdvaith SiddharthanEhud ReiterSungwook YangJoseph N. MartelBrian C. Becker
- Topics
- Natural Language Processing Techniques (9 papers)Topic Modeling (8 papers)Artificial Intelligence in Law (6 papers)
- Partner nations
- GermanyNetherlandsUnited Kingdom
In The Last Decade
Daniel Braun
47 papers receiving 402 citations
Peers
Comparison fields: 5 of 112
- Artificial Intelligence 209
- Computer Vision and Pattern Recognition 46
- Electrical and Electronic Engineering 46
- Information Systems 43
- Radiology, Nuclear Medicine and Imaging 33
Countries citing papers authored by Daniel Braun
This map shows the geographic impact of Daniel Braun'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 Daniel Braun with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Braun more than expected).
Fields of papers citing papers by Daniel Braun
This network shows the impact of papers produced by Daniel Braun. 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 Daniel Braun. The network helps show where Daniel Braun may publish in the future.
Co-authorship network of co-authors of Daniel Braun
This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Braun. A scholar is included among the top collaborators of Daniel Braun 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 Daniel Braun. Daniel Braun 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 | 0 | |
| 3 | 1 | |
| 4 | 1 | |
| 5 | 1 | |
| 6 | 0 | |
| 7 | 2 | |
| 8 | 4 | |
| 9 | 7 | |
| 10 | 3 | |
| 11 | 1 | |
| 12 | 43 | |
| 13 | 31 | |
| 14 | Feature and Deep Learning Based Approaches for Automatic Report Generation and Severity Scoring of Lung Tuberculosis from CT Images. | 3 |
| 15 | Customer-centered LegalTech: Automated Analysis of Standard Form Contracts | 5 |
| 16 | Convolutional Neural Networks for Multidrug-resistant and Drug-sensitive Tuberculosis Distinction. | 3 |
| 17 | 106 | |
| 18 | Finding Trees in Mountains - Outlier Detection on Polygonal Chains. | 0 |
| 19 | Extraction of Solids of Revolution from Point Cloud Scenes for Grasp Planning Tasks | 2 |
| 20 | 23 |
About Daniel Braun
Daniel Braun is a scholar working on Artificial Intelligence, Computer Science Applications and Developmental Biology, having authored 53 papers that have together received 426 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (9 papers), Topic Modeling (8 papers) and Artificial Intelligence in Law (6 papers). The work is most often cited by research in Artificial Intelligence (209 citations), Computer Science Applications (20 citations) and Health Informatics (5 citations). Daniel Braun has collaborated with scholars based in Germany, Netherlands and United Kingdom. Frequent co-authors include Florian Matthes, Friedhelm Schwenker, David S. Fischer, Advaith Siddharthan, Ehud Reiter, Sungwook Yang, Joseph N. Martel, Brian C. Becker, Patrick Thiam and Johannes Huwer. Their work appears in journals such as Water Research, Sustainability and Neural Computation.
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.