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 metaverse assessment model for sustainable transportation using ordinal priority approach and Aczel-Alsina norms
2022142 citationsDragan Pamučar, Muhammet Deveci 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 Mario Köppen'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 Mario Köppen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mario Köppen more than expected).
This network shows the impact of papers produced by Mario Köppen. 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 Mario Köppen. The network helps show where Mario Köppen may publish in the future.
Co-authorship network of co-authors of Mario Köppen
This figure shows the co-authorship network connecting the top 25 collaborators of Mario Köppen.
A scholar is included among the top collaborators of Mario Köppen 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 Mario Köppen. Mario Köppen is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Bhattacharyya, Siddhartha, Jyoti Sekhar Banerjee, & Mario Köppen. (2024). Human-Centric Smart Computing. Smart innovation, systems and technologies.1 indexed citations
Yoshida, Kaori, Yuta Nakagawa, & Mario Köppen. (2010). Interactive genetic algorithm for font generation system. World Automation Congress. 1–6.6 indexed citations
12.
Köppen, Mario, Nikola Kasabov, & George Coghill. (2009). Advances in Neuro-Information Processing: 15th International Conference, ICONIP 2008, Auckland, New Zealand, November 25-28, 2008, Revised Selected Papers, Part II. Springer eBooks.1 indexed citations
13.
Abraham, Ajith, Bernard De Baets, Mario Köppen, & Bertram Nickolay. (2006). Applied Soft Computing Technologies: The Challenge of Complexity (Advances in Soft Computing). Springer eBooks.5 indexed citations
14.
Hoffmann, Frank, Mario Köppen, Frank Klawonn, & Rajkumar Roy. (2005). Soft Computing: Methodologies and Applications (Advances in Soft Computing) (Advances in Soft Computing). Springer eBooks.1 indexed citations
15.
Abraham, Ajith, Mario Köppen, & Katrin Franke. (2003). Design and Application of Hybrid Intelligent Systems. Acquire (CQUniversity).65 indexed citations
16.
Köppen, Mario & Evgenia Dimitriadou. (2003). Concurrent application of genetic algorithm in pattern recognition. 868–877.2 indexed citations
Franke, Katrin, Javier Ruiz‐del‐Solar, & Mario Köppen. (2002). Soft-Biometrics: Soft-Computing for Biometric-Applications. Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft).11 indexed citations
19.
Abraham, Ajith, Javier Ruiz‐del‐Solar, & Mario Köppen. (2002). Soft computing systems : design, management and applications.29 indexed citations
20.
Köppen, Mario, Javier Ruiz‐del‐Solar, & Pierre Soille. (1998). Texture Segmentation by Biologically-Inspired Use of Neural Networks and Mathematical Morphology.. Natural Computing. 267–272.9 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.