Franz J. Király

2.1k citations
27 papers · 1.1k indexed · h-index 11

Franz J. Király

27 papers receiving 1.0k citations

Peers

Franz J. Király
Comparison fields: 5 of 154
  • Health Informatics 144
  • Hematology 149
  • Health Information Management 44
  • Signal Processing 84
  • Applied Psychology 40
Replace Moritz Lehne with:
Moritz Lehne Germany
Hanna Suominen Finland
Brian M. Bot United States
Ahmed Allam Switzerland
Megan Doerr United States
Mark Clowes United Kingdom
Yang Xiang China
Julian Varghese Germany
Michael Kellen United States
Feifan Liu United States
Franz J. Király relative to Moritz Lehne Germany Moritz Lehne's profile →
Citations per field
00.5×9.3×
Moritz Lehne · 1×
Citations per year

Countries citing papers authored by Franz J. Király

Since Specialization
Citations

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

Fields of papers citing papers by Franz J. Király

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Franz J. Király. 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 Franz J. Király. The network helps show where Franz J. Király may publish in the future.

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 202161
2
mlr3proba: Machine Learning Survival Analysis in R.
20201
3 202010
4 2020307
5
Kernels for sequentially ordered data
201934
6 20182
7 20183
8 20185
9 2018132
10 201666
11 201618
12 20141
13 20132
14
A Combinatorial Algebraic Approach for the Identifiability of Low-Rank Matrix Completion
201215
15
The Stationary Subspace Analysis Toolbox
201113
16 20115
17 20101
18 2010158
19 20095
20 2009187

About Franz J. Király

Franz J. Király is a scholar working on Health Informatics, Algebra and Number Theory, Signal Processing, Discrete Mathematics and Combinatorics and Artificial Intelligence, having authored 27 papers that have together received 1.1k indexed citations. Recurring topics across this work include Blind Source Separation Techniques (4 papers), Explainable Artificial Intelligence (XAI) (4 papers), Sparse and Compressive Sensing Techniques (3 papers), Polynomial and algebraic computation (3 papers), Artificial Intelligence in Healthcare and Education (3 papers), Impact of Technology on Adolescents (2 papers), Sports Analytics and Performance (2 papers) and Commutative Algebra and Its Applications (2 papers). The work is most often cited by research in Health Informatics (144 citations), Hematology (149 citations), Health Information Management (44 citations), Signal Processing (84 citations) and Applied Psychology (40 citations). Franz J. Király has collaborated with scholars based in Germany, United Kingdom and United States. Frequent co-authors include Paul von Bünau, Frank C. Meinecke, Klaus‐Robert Müller, Konstantinos Ioannidis, Christine Löchner, Samuel R. Chamberlain, Sarah A. Redden, Dan J. Stein, Jon E. Grant and Matthias S. Treder. Their work appears in journals such as Journal of Machine Learning Research, PLoS ONE, Addictive Behaviors, International Journal of Algebra and Computation and Journal of Psychiatric Research.

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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