Markus Krause

707 citations
32 papers · 567 · h-index 11

Impact in

Papers in

    • Natural Language Processing Techniques 5
    • Topic Modeling 4
    • Data Stream Mining Techniques 4
    • Artificial Intelligence in Games 3
    • Mobile Crowdsensing and Crowdsourcing 13
    • Open Source Software Innovations 5
    • Online Learning and Analytics 4

Markus Krause

32 papers receiving 549 citations

Peers

Markus Krause
Comparison fields: 5 of 93
  • Computer Science Applications 158
  • Dermatology 218
  • Human-Computer Interaction 56
  • Rehabilitation 23
  • Developmental and Educational Psychology 34
Replace Constantinos Koutsojannis with:
Constantinos Koutsojannis Greece
Karin Danielsson Sweden
I‐Jung Chen Taiwan
Andrew Wright United Kingdom
Carlos A. Velasco Germany
Chen-Hsiang Yu United States
Ken Sutton Australia
Annie Wang United States
Nobuko Fujita Japan
Carmen Jiménez Fernández Spain
Markus Krause relative to Constantinos Koutsojannis Greece Constantinos Koutsojannis's profile →
Citations per field
00.5×10×14×
Constantinos Koutsojannis · 1×
Citations per year

Countries citing papers authored by Markus Krause

Since Specialization
Citations

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

Fields of papers citing papers by Markus Krause

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Markus Krause, 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 Markus Krause Line = papers co-authored together Markus Krause links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 32 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2010215
2 201164
3 201046
4 201744
5 200731
6 201116
7 201614
8 201613
9
Predicting Crowd-Based Translation Quality with Language-Independent Feature Vectors.
201211
10 201011
11 201411
12 201410
13 20159
14 20158
15 20158
16 20097
17 20117
18 20185
19 20135
20 20094

About Markus Krause

Markus Krause is a scholar working on Artificial Intelligence, Computer Science Applications, Sociology and Political Science, Information Systems and Computer Vision and Pattern Recognition, having authored 32 papers that have together received 567 indexed citations. Recurring topics across this work include Mobile Crowdsensing and Crowdsourcing (13 papers), Natural Language Processing Techniques (5 papers), Open Source Software Innovations (5 papers), Topic Modeling (4 papers), Data Stream Mining Techniques (4 papers), Online Learning and Analytics (4 papers), Artificial Intelligence in Games (3 papers) and Data Visualization and Analytics (3 papers). The work is most often cited by research in Computer Science Applications (158 citations), Dermatology (218 citations), Human-Computer Interaction (56 citations), Rehabilitation (23 citations) and Developmental and Educational Psychology (34 citations). Markus Krause has collaborated with scholars based in Germany, United States and Ireland. Frequent co-authors include Wolfram Sterry, Sylke Schneider‐Burrus, Rainer Malaka, Hans‐Dieter Volk, Conny Hoeflich, Katarzyna Warszawska, Ansgar Lukowsky, Ellen Witte, Katrin Witte and Stefanie Kunz. Their work appears in journals such as JDDG Journal der Deutschen Dermatologischen Gesellschaft, The Journal of Immunology, AI Magazine, IEEE Access and IEEE Transactions on Learning Technologies.

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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