Michael Hund

14 papers receiving 216 citations

Peers

Michael Hund
Comparison fields: 5 of 91
  • Cell Biology 59
  • Computer Vision and Pattern Recognition 72
  • Urology 14
  • Pathology and Forensic Medicine 35
  • Computer Graphics and Computer-Aided Design 6
Replace M. H. Ahmad Fadzil with:
M. H. Ahmad Fadzil Malaysia
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Sang-Kwang Lee South Korea
Chris Hughes United Kingdom
Min Fu China
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Citations per field
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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

14 of 14 papers shown
#Work
1 202093
2 201632
3 201622
4
Getting there first : real-time detection of real-world incidents on Twitter
201221
5 201619
6 201210
7 20216
8
HistoBankVis : Detecting Language Change via Data Visualization
20175
9 20164
10
Visual Quality Assessment of Subspace Clusterings
20163
11
Visual Analytics for the Prediction of Movie Rating and Box Office Performance
20132
12 20132
13
MooVis -- A Visual Analytics Tool for the Prediction of Movie Viewer Ratings and Boxoffice
20131
14 20171

About Michael Hund

Michael Hund is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Statistical and Nonlinear Physics, Signal Processing and Literature and Literary Theory, having authored 14 papers that have together received 221 indexed citations. Recurring topics across this work include Data Visualization and Analytics (9 papers), Complex Network Analysis Techniques (3 papers), Data Analysis with R (2 papers), Video Analysis and Summarization (2 papers), Media Influence and Health (2 papers), Autoimmune Bullous Skin Diseases (1 paper), Advanced Text Analysis Techniques (1 paper) and Time Series Analysis and Forecasting (1 paper). The work is most often cited by research in Cell Biology (59 citations), Computer Vision and Pattern Recognition (72 citations), Urology (14 citations), Pathology and Forensic Medicine (35 citations) and Computer Graphics and Computer-Aided Design (6 citations). Michael Hund has collaborated with scholars based in Germany, United States and Austria. Frequent co-authors include Daniel A. Keim, Anna L. Bruckner, A. Reha, Hjalmar Lagast, Nita Patel, Christian Rohrdantz, Dédée F. Murrell, Tobias Schreck, Andreas Weiler and Miloš Krstajić. Their work appears in journals such as Computer Graphics Forum, Orphanet Journal of Rare Diseases, IEEE Transactions on Visualization and Computer Graphics, Brain Informatics and ACM Transactions on Computing Education.

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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