Johannes Haug
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition
- Small Animals top 10%
- Health Information Management top 5%
- Animal Science and Zoology
- Co-authors
- Gjergji KasneciKathrin SeßlerMartin PawelczykVadim BorisovTobias LeemannBernd BrueggeAlexander BraunJonathan J. Harrington
- Topics
- Data Stream Mining Techniques (2 papers)Machine Learning and Data Classification (2 papers)Animal Behavior and Welfare Studies (1 paper)
- Journals
- IEEE Transactions on Neural Networks and Learning SystemsScientific DataNew Zealand journal of forestry science
- Partner nations
- Germany
In The Last Decade
Johannes Haug
6 papers receiving 493 citations
Hit Papers
Peers
Comparison fields: 5 of 128
- Artificial Intelligence 216
- Computer Vision and Pattern Recognition 52
- Small Animals 37
- Health Information Management 33
- Animal Science and Zoology 32
Countries citing papers authored by Johannes Haug
This map shows the geographic impact of Johannes Haug'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 Johannes Haug with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Johannes Haug more than expected).
Fields of papers citing papers by Johannes Haug
This network shows the impact of papers produced by Johannes Haug. 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 Johannes Haug. The network helps show where Johannes Haug may publish in the future.
Co-authorship network of co-authors of Johannes Haug
This figure shows the co-authorship network connecting the top 25 collaborators of Johannes Haug. A scholar is included among the top collaborators of Johannes Haug 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 Johannes Haug. Johannes Haug is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Deep Neural Networks and Tabular Data: A Surveybreakdown → | 428 |
| 2 | 8 | |
| 3 | 2 | |
| 4 | 8 | |
| 5 | 57 | |
| 6 | Spatial variation in spiral grain: a single stem of Pinus radiata D.Don | 4 |
About Johannes Haug
Johannes Haug is a scholar working on Human-Computer Interaction, Small Animals and Animal Science and Zoology, having authored 6 papers that have together received 507 indexed citations. Recurring topics across this work include Data Stream Mining Techniques (2 papers), Machine Learning and Data Classification (2 papers) and Animal Behavior and Welfare Studies (1 paper). The work is most often cited by research in Health Informatics (11 citations), Health Information Management (33 citations) and Artificial Intelligence (216 citations). Johannes Haug has collaborated with scholars based in Germany. Frequent co-authors include Gjergji Kasneci, Kathrin Seßler, Martin Pawelczyk, Vadim Borisov, Tobias Leemann, Bernd Bruegge, Alexander Braun, Jonathan J. Harrington, Klaus Broelemann and Franz Wortha. Their work appears in journals such as IEEE Transactions on Neural Networks and Learning Systems, Scientific Data and New Zealand journal of forestry science.
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.