Adrian Calma

477 citations
17 papers · 265 · h-index 8

Impact in

Papers in

    • Machine Learning and Algorithms 12
    • Machine Learning and Data Classification 8
    • Data Stream Mining Techniques 5
    • Algorithms and Data Compression 2
    • Imbalanced Data Classification Techniques 1
    • Intelligent Tutoring Systems and Adaptive Learning 1
    • AI-based Problem Solving and Planning 1
    • Mobile Crowdsensing and Crowdsourcing 5

Adrian Calma

17 papers receiving 252 citations

Peers

Adrian Calma
Comparison fields: 5 of 69
  • Health Informatics 9
  • Computer Science Applications 36
  • Artificial Intelligence 148
  • Safety Research 34
  • Management Information Systems 26
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Citations per field
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Citations per year

Countries citing papers authored by Adrian Calma

Since Specialization
Citations

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

Fields of papers citing papers by Adrian Calma

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 15 scholars most cited alongside Adrian Calma, 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 Adrian Calma Line = papers co-authored together Adrian Calma links everyone, so they are left out of the graph.

All Works

17 of 17 papers shown
#Work
1 2019116
2 201433
3 201828
4
From Active Learning to Dedicated Collaborative Interactive Learning
201617
5 201714
6
Challenges of Reliable, Realistic and Comparable Active Learning Evaluation
201711
7 201611
8 20219
9 20186
10
Simulation of Annotators for Active Learning: Uncertain Oracles.
20174
11 20183
12 20183
13 20193
14 20182
15 20162
16 20182
17 20181

About Adrian Calma

Adrian Calma is a scholar working on Artificial Intelligence, Computer Science Applications, Software, Computer Networks and Communications and Management Information Systems, having authored 17 papers that have together received 265 indexed citations. Recurring topics across this work include Machine Learning and Algorithms (12 papers), Machine Learning and Data Classification (8 papers), Data Stream Mining Techniques (5 papers), Mobile Crowdsensing and Crowdsourcing (5 papers), Algorithms and Data Compression (2 papers), Imbalanced Data Classification Techniques (1 paper), Intelligent Tutoring Systems and Adaptive Learning (1 paper) and AI-based Problem Solving and Planning (1 paper). The work is most often cited by research in Health Informatics (9 citations), Computer Science Applications (36 citations), Artificial Intelligence (148 citations), Safety Research (34 citations) and Management Information Systems (26 citations). Adrian Calma has collaborated with scholars based in Germany, Switzerland and Netherlands. Frequent co-authors include Bernhard Sick, Philipp Ebel, Nikolaus Lipusch, Dominik Dellermann, Sven Tomforde, Jan Marco Leimeister, Sarah Oeste-Reiß, Albrecht Schmidt, Gerd Stumme and Paul Lukowicz. Their work appears in journals such as Information Sciences, IEEE Access, SSRN Electronic Journal, Repository KITopen (Karlsruhe Institute of Technology) and Alexandria (UniSG) (University of St.Gallen).

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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