Michael Hund

413 citations
17 papers · 269 · h-index 8

Impact in

Papers in

Michael Hund

17 papers receiving 261 citations

Peers

Michael Hund
Comparison fields: 5 of 95
  • Computer Vision and Pattern Recognition 89
  • Cell Biology 60
  • Health Informatics 3
  • Health Information Management 9
  • Artificial Intelligence 71
Replace Zheng Yuan with:
Zheng Yuan China
Scott Markel United States
Suliman Aladhadh Saudi Arabia
Vandana Jagtap India
Adrienne Heinrich Netherlands
Mohammad D. Alahmadi Saudi Arabia
Zeeshan Shaukat China
Benjamin Hescott United States
Jiawei Su China
Loïc Cerf Brazil
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Citations per field
00.5×12×
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Citations per year

Countries citing papers authored by Michael Hund

Since Specialization
Citations

This map shows the geographic impact of Michael Hund'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 Michael Hund with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Hund more than expected).

Fields of papers citing papers by Michael Hund

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael Hund. 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 Michael Hund. The network helps show where Michael Hund may publish in the future.

Co-authors

The 25 scholars most cited alongside Michael Hund, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Michael Hund Line = papers co-authored together Michael Hund links everyone, so they are left out of the graph.

All Works

17 of 17 papers shown
#Work
1 202095
2 201643
3
Getting there first : real-time detection of real-world incidents on Twitter
201226
4 201624
5 201623
6 201212
7 20159
8 20157
9 20216
10
HistoBankVis : Detecting Language Change via Data Visualization
20175
11 20144
12 20164
13
Visual Analytics for the Prediction of Movie Rating and Box Office Performance
20134
14
Visual Quality Assessment of Subspace Clusterings
20163
15 20132
16 20171
17
MooVis -- A Visual Analytics Tool for the Prediction of Movie Viewer Ratings and Boxoffice
20131

About Michael Hund

Michael Hund is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Molecular Biology, Statistical and Nonlinear Physics and Signal Processing, having authored 17 papers that have together received 269 indexed citations. Recurring topics across this work include Data Visualization and Analytics (10 papers), Complex Network Analysis Techniques (3 papers), Media Influence and Health (2 papers), Data Analysis with R (2 papers), Video Analysis and Summarization (2 papers), Biomedical Text Mining and Ontologies (2 papers), Image Retrieval and Classification Techniques (2 papers) and Data Management and Algorithms (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (89 citations), Cell Biology (60 citations), Health Informatics (3 citations), Health Information Management (9 citations) and Artificial Intelligence (71 citations). Michael Hund has collaborated with scholars based in Germany, Austria and United States. Frequent co-authors include Daniel A. Keim, Tobias Schreck, Christian Rohrdantz, Hjalmar Lagast, Nita Patel, Anna L. Bruckner, Dédée F. Murrell, A. Reha, Miloš Krstajić and Andreas Weiler. Their work appears in journals such as Brain Informatics, ACM Transactions on Computing Education, Orphanet Journal of Rare Diseases, Computer Graphics Forum and IEEE Transactions on Visualization and Computer Graphics.

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.

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