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.
Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils
2010370 citationsIşık Yılmaz, Oğuz KaynarExpert Systems with Applicationsprofile →
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 Oğuz Kaynar'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 Oğuz Kaynar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Oğuz Kaynar more than expected).
This network shows the impact of papers produced by Oğuz Kaynar. 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 Oğuz Kaynar. The network helps show where Oğuz Kaynar may publish in the future.
Co-authorship network of co-authors of Oğuz Kaynar
This figure shows the co-authorship network connecting the top 25 collaborators of Oğuz Kaynar.
A scholar is included among the top collaborators of Oğuz Kaynar 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 Oğuz Kaynar. Oğuz Kaynar is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Kaynar, Oğuz, et al.. (2019). Ders Programı Çizelgeleme Probleminin Genetik Algoritma ile Optimizasyonu. DergiPark (Istanbul University). 1(1). 9–14.1 indexed citations
Kaynar, Oğuz, et al.. (2017). GENETIC ALGORITHM BASED SENTENCE EXTRACTION FOR AUTOMATIC TEXT SUMMARIZATION. DergiPark (Istanbul University).1 indexed citations
Kaynar, Oğuz, et al.. (2017). Forecasting of Turkey’s Electricity Consumption with Support Vector Regression and Chaotic Particle Swarm Algorithm. Yönetim Bilimleri Dergisi. 15(29). 211–224.1 indexed citations
Kaynar, Oğuz, et al.. (2010). Forecasting of natural gas consumption with neural network and neuro fuzzy system. EGUGA. 7781.49 indexed citations
16.
Yılmaz, Işık & Oğuz Kaynar. (2010). Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils. EGU General Assembly Conference Abstracts. 43.285 indexed citations
Yılmaz, Işık & Oğuz Kaynar. (2010). Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils. Expert Systems with Applications. 38(5). 5958–5966.370 indexed citations breakdown →
20.
Kaynar, Oğuz, et al.. (2005). Veri Zarflama Analizi ile OECD Ülkelerinin Telekomünikasyon Sektörlerinin Etkinliğinin Ölçülmesi. 6(1). 37–57.2 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.