World's Best Scientists 2026 revealed!
Erol Egrioglu

Erol Egrioglu

D-Index & Metrics

Computer Science

D-Index
38
Citations
4879
World Ranking
10388
National Ranking
16

Erol Egrioglu publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Erol Egrioglu sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 250 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 560 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 424 scientists 242–251 publications: 408 scientists 252–261 publications: 378 scientists 262–271 publications: 300 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 156 publications — 29th percentile

29% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Erol Egrioglu D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Erol Egrioglu sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 984 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 969 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 765 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 517 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 38 D-Index — 30th percentile

30% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Erol Egrioglu is affiliated with Giresun University in Turkey and has contributed extensively to research in the fields of decision sciences, computer science, and engineering. Their work spans numerous subfields, including management science and operations research, artificial intelligence, electrical and electronic engineering, statistics and probability, and economics and econometrics.

Their research has addressed a range of topics, primarily focusing on forecasting methods and neural network applications. Key areas of interest include stock market forecasting methods, energy load and power forecasting, neural networks and applications, forecasting techniques and applications, fuzzy logic and control systems, fuzzy systems and optimization, and metaheuristic optimization algorithms research.

Egrioglu's publication record includes papers in various academic venues. Frequent outlets for their research include:

  • Granular Computing
  • SSRN Electronic Journal
  • Computational Economics
  • Information Sciences
  • Library Hi Tech

Notable recent publications include:

  • Recurrent dendritic neuron model artificial neural network for time series forecasting (2022), published in Information Sciences
  • Recurrent fuzzy time series functions approaches for forecasting (2021), published in Granular Computing
  • Robust training of median dendritic artificial neural networks for time series forecasting (2023), published in Expert Systems with Applications
  • Training simple recurrent deep artificial neural network for forecasting using particle swarm optimization (2021), published in Granular Computing
  • Intuitionistic fuzzy time series functions approach for time series forecasting (2020), published in Granular Computing

Egrioglu frequently collaborates with other researchers in the field, including Eren Baş, Mu-Yen Chen, Ufuk Yolcu, Edwin Lughofer, and Turan Cansu. These collaborations likely reflect interdisciplinary approaches and joint investigation into forecasting techniques and artificial intelligence.

Best Publications

  • Forecasting in high order fuzzy times series by using neural networks to define fuzzy relations

    Cagdas H. Aladag;Murat A. Basaran;Erol Egrioglu;Ufuk Yolcu

  • Forecasting nonlinear time series with a hybrid methodology

    Cagdas Hakan Aladag;Erol Egrioglu;Cem Kadilar

  • Fuzzy time series forecasting with a novel hybrid approach combining fuzzy c-means and neural networks

    Erol Egrioglu;Cagdas Hakan Aladag;Ufuk Yolcu

  • A new linear & nonlinear artificial neural network model for time series forecasting

    Ufuk Yolcu;Erol Egrioglu;Cagdas H. Aladag

  • A new approach for determining the length of intervals for fuzzy time series

    Ufuk Yolcu;Erol Egrioglu;Vedide R. Uslu;Murat A. Basaran

  • A new time invariant fuzzy time series forecasting method based on particle swarm optimization

    Cagdas Hakan Aladag;Ufuk Yolcu;Erol Egrioglu;Ali Z. Dalar

  • A new approach based on artificial neural networks for high order multivariate fuzzy time series

    Erol Egrioglu;Cagdas Hakan Aladag;Ufuk Yolcu;Vedide R. Uslu

  • Finding an optimal interval length in high order fuzzy time series

    Erol Egrioglu;Cagdas Hakan Aladag;Ufuk Yolcu;Vedide R. Uslu

  • Recurrent Multiplicative Neuron Model Artificial Neural Network for Non-linear Time Series Forecasting

    Erol Egrioglu;Ufuk Yolcu;Cagdas Hakan Aladag;Eren Bas

  • Original articles: A high order fuzzy time series forecasting model based on adaptive expectation and artificial neural networks

    Cagdas Hakan Aladag;Ufuk Yolcu;Erol Egrioglu

  • A new hybrid approach based on SARIMA and partial high order bivariate fuzzy time series forecasting model

    Erol Egrioglu;Cagdas Hakan Aladag;Ufuk Yolcu;Murat A. Basaran

  • A new approach based on the optimization of the length of intervals in fuzzy time series

    Erol Egrioglu;Cagdas Hakan Aladag;Murat A. Basaran;Ufuk Yolcu

  • A new model selection strategy in artificial neural networks

    Erol Eğrioğlu;Çağdaş Hakan Aladağ;Süleyman Günay

  • Fuzzy time series forecasting method based on Gustafson-Kessel fuzzy clustering

    E. Egrioglu;C. H. Aladag;U. Yolcu;V. R. Uslu

  • A new hybrid method for time series forecasting: AR–ANFIS

    Unknown

  • A modified genetic algorithm for forecasting fuzzy time series

    Eren Bas;Vedide Rezan Uslu;Ufuk Yolcu;Erol Egrioglu

  • Training simple recurrent deep artificial neural network for forecasting using particle swarm optimization

    Unknown

  • Determining the most proper number of cluster in fuzzy clustering by using artificial neural networks

    N. Alp Erilli;Ufuk Yolcu;Erol Eğrioğlu;Ç. Hakan Aladağ

  • High order fuzzy time series method based on pi-sigma neural network

    Eren Bas;Eren Bas;Eren Bas;Crina Grosan;Crina Grosan;Erol Egrioglu;Ufuk Yolcu

  • Comparison of intraoral radiography and cone-beam computed tomography for the detection of horizontal root fractures: an in vitro study

    Hakan Avsever;Kaan Gunduz;Kaan Orhan;Ismail Uzun

  • Time-series forecasting with a novel fuzzy time-series approach: an example for Istanbul stock market

    Ufuk Yolcu;Cagdas Hakan Aladag;Erol Egrioglu;Vedide R. Uslu

  • High order fuzzy time series forecasting method based on an intersection operation

    Ozge Cagcag Yolcu;Ufuk Yolcu;Erol Egrioglu;C. Hakan Aladag

Frequent Co-Authors

Crina Grosan
Crina Grosan King's College London
Edwin Lughofer
Edwin Lughofer Johannes Kepler University of Linz
Yaochu Jin
Yaochu Jin Westlake University

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