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.
Plant identification using deep neural networks via optimization of transfer learning parameters
2017391 citationsMostafa Mehdipour Ghazi, Berrin Yanıkoğlu et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
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Countries citing papers authored by Berrin Yanıkoğlu
Since
Specialization
Citations
This map shows the geographic impact of Berrin Yanıkoğlu'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 Berrin Yanıkoğlu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Berrin Yanıkoğlu more than expected).
Fields of papers citing papers by Berrin Yanıkoğlu
This network shows the impact of papers produced by Berrin Yanıkoğlu. 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 Berrin Yanıkoğlu. The network helps show where Berrin Yanıkoğlu may publish in the future.
Co-authorship network of co-authors of Berrin Yanıkoğlu
This figure shows the co-authorship network connecting the top 25 collaborators of Berrin Yanıkoğlu.
A scholar is included among the top collaborators of Berrin Yanıkoğlu 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 Berrin Yanıkoğlu. Berrin Yanıkoğlu is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Ahmed, Sara Atito Ali, Berrin Yanıkoğlu, & Erchan Aptoula. (2017). Plant Identification with Large Number of Classes: SabanciU-GebzeTU System in PlantCLEF 2017.. CLEF (Working Notes).3 indexed citations
8.
Ghazi, Mostafa Mehdipour, Berrin Yanıkoğlu, & Erchan Aptoula. (2016). Open-set plant identification using an ensemble of deep convolutional neural networks. CLEF (Working Notes). 518–524.5 indexed citations
9.
Ghazi, Mostafa Mehdipour, et al.. (2015). Sabanci-Okan system in LifeCLEF 2015 plant identification competition. Sabanci University.5 indexed citations
Erdoğan, Hakan, et al.. (2013). Biohashing with fingerprint spectral minutiae. Sabanci University. 1–12.3 indexed citations
12.
Yanıkoğlu, Berrin, et al.. (2013). Sabanci-Okan system at imageclef 2013 plant identification competition. Sabanci University. 771–777.3 indexed citations
13.
Yanıkoğlu, Berrin, et al.. (2013). SU-Sentilab : A Classification System for Sentiment Analysis in Twitter. Scuola Normale Superiore di Pisa. 2. 471–477.12 indexed citations
14.
Yanıkoğlu, Berrin, et al.. (2012). New features for sentiment analysis: Do sentences matter?. Sabanci University. 5–15.13 indexed citations
15.
Yanıkoğlu, Berrin, et al.. (2012). Sabanci-Okan system at ImageClef 2012: combining features and classifiers for plant identification. Sabanci University.14 indexed citations
16.
Yanıkoğlu, Berrin, et al.. (2012). Memory conscious sketched symbol recognition.
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.