Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
A review of machine learning for the optimization of production processes
2019232 citationsStefan Rüping, Stefan Wrobel et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Stefan Rüping'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 Stefan Rüping with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stefan Rüping more than expected).
This network shows the impact of papers produced by Stefan Rüping. 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 Stefan Rüping. The network helps show where Stefan Rüping may publish in the future.
Co-authorship network of co-authors of Stefan Rüping
This figure shows the co-authorship network connecting the top 25 collaborators of Stefan Rüping.
A scholar is included among the top collaborators of Stefan Rüping based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Stefan Rüping. Stefan Rüping is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Giesselbach, Sven, et al.. (2019). Improving Word Embeddings Using Kernel PCA. Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft). 200–208.5 indexed citations
4.
Rossi, Simona, Stefan Rüping, Francesca M. Buffa, et al.. (2016). ecancermedicalscience. ecancermedicalscience. 5. 218–218.8 indexed citations
5.
Stenzhorn, Holger, Marian Taylor, Hena R. Ramay, et al.. (2016). ecancermedicalscience. ecancermedicalscience. 8. 399–399.3 indexed citations
6.
Rüping, Stefan. (2015). Big Data in Medizin und Gesundheitswesen. Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz. 58(8). 794–798.15 indexed citations
7.
Kondylakis, Haridimos, Lefteris Koumakis, Stefan Rüping, et al.. (2014). PMIR: A Personal Medical Information Recommender.. Fraunhofer-Publica (Fraunhofer-Gesellschaft). 1193.3 indexed citations
8.
Rüping, Stefan. (2010). SVM Classifier Estimation from Group Probabilities.. Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft). 129–135.34 indexed citations
Rüping, Stefan, Mario Porrmann, & Ulrich Rückert. (1997). SOM Hardware-Accelerator. PUB – Publications at Bielefeld University (Bielefeld University). 141.8 indexed citations
19.
Schutte, Flip, S. Beineke, H. Grotstollen, et al.. (1997). Structure-and Parameter Identification for a Two-Mass-System With Backlash and Friction Using a Self-Organizing Map. PUB – Publications at Bielefeld University (Bielefeld University). 3.12 indexed citations
20.
Rüping, Stefan, K. Goser, & Ulrich Rückert. (1994). A Chip for Selforganizing Feature Maps. PUB – Publications at Bielefeld University (Bielefeld University).11 indexed citations
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.