This map shows the geographic impact of Erol Eğrioğlu'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 Erol Eğrioğlu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Erol Eğrioğlu more than expected).
This network shows the impact of papers produced by Erol Eğrioğlu. 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 Erol Eğrioğlu. The network helps show where Erol Eğrioğlu may publish in the future.
Co-authorship network of co-authors of Erol Eğrioğlu
This figure shows the co-authorship network connecting the top 25 collaborators of Erol Eğrioğlu.
A scholar is included among the top collaborators of Erol Eğrioğlu 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 Erol Eğrioğlu. Erol Eğrioğlu is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Eğrioğlu, Erol, et al.. (2017). Support Vector Machines vs Multiplicative Neuron Model Neural Network in Prediction of Bank Failures. 7(5). 125–131.2 indexed citations
8.
Eğrioğlu, Erol, et al.. (2016). Classification with Some Artificial Neural Network Classifiers Trained a Modified Particle Swarm Optimization. 6(3). 59–65.3 indexed citations
9.
Baş, Eren, et al.. (2016). Single Multiplicative Neuron Model Artificial Neuron Network Trained by Bat Algorithm for Time Series Forecasting. 6(3). 74–77.3 indexed citations
10.
Eğrioğlu, Erol, et al.. (2015). Recurrent Type Fuzzy Time Series Forecasting Method Based on Artificial Neural Networks. 5(5). 111–124.3 indexed citations
11.
Eğrioğlu, Erol, et al.. (2015). A New Hybrid Fuzzy Time Series Forecasting Approach Based on Intelligent Optimization. 5(4). 97–108.4 indexed citations
Eğrioğlu, Erol, et al.. (2014). Computing Cronbach Alpha Reliability Coefficient for Fuzzy Survey Data. 4(5). 204–213.6 indexed citations
14.
Uslu, Vedide Rezan, Erol Eğrioğlu, & Eren Baş. (2014). Finding Optimal Value for the Shrinkage Parameter in Ridge Regression via Particle Swarm Optimization. 4(4). 142–147.9 indexed citations
15.
Eğrioğlu, Erol, et al.. (2013). Comparisons of Logistic Regression and Artificial Neural Networks in Lung Cancer Data. 3(2). 71–74.5 indexed citations
16.
Uslu, Vedide Rezan, Eren Baş, Ufuk Yolcu, & Erol Eğrioğlu. (2013). A NEW FUZZY TIME SERIES ANALYSIS APPROACH BY USING DIFFERENTIAL EVOLUTION ALGORITHM AND CHRONOLOGICALLY-DETERMINED WEIGHTS. RePEc: Research Papers in Economics. 2(1). 18–30.4 indexed citations
17.
Baş, Eren, Vedide Rezan Uslu, Ufuk Yolcu, & Erol Eğrioğlu. (2013). A Fuzzy Time Series Analysis Approach by Using Differential Evolution Algorithm Based on the Number of Recurrences of Fuzzy Relations. 3(2). 75–82.2 indexed citations
18.
Uslu, Vedide Rezan, Ufuk Yolcu, Erol Eğrioğlu, Çağdaş Hakan Aladağ, & Murat Alper Başaran. (2012). Yüksek Dereceli Bulanık Zaman Serisi Yaklaşımı ile Türkiye Enflasyon Öngörüsü. DergiPark (Istanbul University). 27(1). 85–95.
19.
Aladağ, Çağdaş Hakan, Erol Eğrioğlu, & Cem Kadılar. (2010). Modeling Brain Wave Data by Using Artificial Neural Networks. Lancaster EPrints (Lancaster University).2 indexed citations
20.
Eğrioğlu, Erol, et al.. (2007). Tek Değişkenli Zaman Serileri Analizine Giriş. Hacettepe University Institutional Repository (hacettepe.edu.tr).1 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.