World's Best Scientists 2026 revealed!
José C. Riquelme

José C. Riquelme

D-Index & Metrics

Computer Science

D-Index
36
Citations
5528
World Ranking
11261
National Ranking
189

José C. Riquelme 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 José C. Riquelme 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: 249 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: 559 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: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 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: 155 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.

José C. Riquelme 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 José C. Riquelme sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 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: 36 D-Index — 23rd percentile

23% of scientists in this discipline score the same or lower.

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

Overview

José C. Riquelme is affiliated with the University of Seville in Spain. Their research spans across the fields of Computer Science and Engineering, with a particular concentration on Artificial Intelligence, Electrical and Electronic Engineering, Signal Processing, Management Science and Operations Research, and Computer Vision and Pattern Recognition.

Their scientific output covers a broad range of topics including:

  • Energy Load and Power Forecasting
  • Time Series Analysis and Forecasting
  • Data Stream Mining Techniques
  • Solar Radiation and Photovoltaics
  • Anomaly Detection Techniques and Applications
  • Stock Market Forecasting Methods
  • Advanced Neural Network Applications

José C. Riquelme has contributed to several recent papers, among which the following are notable:

  • "Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting," 2020, Applied Sciences
  • "Temporal Convolutional Networks Applied to Energy-related Time Series Forecasting," 2020, Preprints.org
  • "Asynchronous dual-pipeline deep learning framework for online data stream classification," 2020, Integrated Computer-Aided Engineering
  • "Enhancing object detection for autonomous driving by optimizing anchor generation and addressing class imbalance," 2021, Neurocomputing
  • "Autoencoded DNA methylation data to predict breast cancer recurrence: Machine learning models and gene-weight significance," 2020, Artificial Intelligence in Medicine

Frequent co-authors collaborating with José C. Riquelme include:

  • Manuel Carranza-García
  • José María Luna-Romera
  • Pedro Lara-Benítez
  • Jorge García-Gutiérrez
  • M. Martínez-Ballesteros

Their work has appeared repeatedly in certain publication venues, which include:

  • Applied Sciences
  • Integrated Computer-Aided Engineering
  • Neurocomputing
  • Heliyon
  • Paste/\x98P\x9caste

Best Publications

  • An Experimental Review on Deep Learning Architectures for Time Series Forecasting.

    Pedro Lara-Benítez;Manuel Carranza-García;José C. Riquelme

  • Incremental wrapper-based gene selection from microarray data for cancer classification

    Roberto Ruiz;José C. Riquelme;Jesús S. Aguilar-Ruiz

  • Energy Time Series Forecasting Based on Pattern Sequence Similarity

    Francisco Martinez Alvarez;A Troncoso;J C Riquelme;Jesus S Aguilar Ruiz

  • Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting

    Pedro Lara-Benítez;Manuel Carranza-García;José M. Luna-Romera;José C. Riquelme

  • A Framework for Evaluating Land Use and Land Cover Classification Using Convolutional Neural Networks

    Manuel Carranza-García;Jorge García-Gutiérrez;José C. Riquelme

  • A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting

    Francisco Martínez-Álvarez;Alicia Troncoso;Gualberto Asencio-Cortés;José C. Riquelme

  • Coronavirus Optimization Algorithm: A Bioinspired Metaheuristic Based on the COVID-19 Propagation Model.

    Francisco Martínez-Álvarez;Gualberto Asencio-Cortés;José F. Torres;David Gutiérrez-Avilés

  • Minería de Datos: Conceptos y Tendencias

    José C. Riquelme;Roberto Ruiz;Karina Gilbert;Área de Lenguajes

  • Preliminary comparison of techniques for dealing with imbalance in software defect prediction

    Daniel Rodriguez;Israel Herraiz;Rachel Harrison;Javier Dolado

  • Evolutionary learning of hierarchical decision rules

    J.S. Aguilar-Ruiz;J.C. Riquelme;M. Toro

  • An evolutionary algorithm to discover numeric association rules

    J. Mata;J. L. Alvarez;J. C. Riquelme

  • Advances in Artificial Intelligence — IBERAMIA 2002

    Francisco J. Garijo;José C. Riquelme;Miguel Toro

  • An evolutionary approach to estimating software development projects

    Jesús S. Aguilar-Ruiz;Isabel Ramos;José C. Riquelme;Miguel Toro

  • Finding representative patterns with ordered projections

    José C. Riquelme;Jesús S. Aguilar-Ruiz;Miguel Toro

  • Big Data Analytics for Discovering Electricity Consumption Patterns in Smart Cities

    Rubén Pérez-Chacón;José M. Luna-Romera;Alicia Troncoso;Francisco Martínez-Álvarez

  • A comparison of machine learning regression techniques for LiDAR-derived estimation of forest variables

    J. García-Gutiérrez;F. Martínez-Álvarez;A. Troncoso;J.C. Riquelme

  • Local models-based regression trees for very short-term wind speed prediction

    A. Troncoso;S. Salcedo-Sanz;C. Casanova-Mateo;J.C. Riquelme

  • Mining Numeric Association Rules with Genetic Algorithms

    J. Mata;J. L. Alvarez;J. C. Riquelme

  • Evolutionary Generalized Radial Basis Function neural networks for improving prediction accuracy in gene classification using feature selection

    Francisco Fernández-Navarro;César Hervás-Martínez;Roberto Ruiz;Jose C. Riquelme

  • Mining quantitative association rules based on evolutionary computation and its application to atmospheric pollution

    M. Martínez-Ballesteros;A. Troncoso;F. Martínez-Álvarez;J. C. Riquelme

Frequent Co-Authors

Francisco Martínez-Álvarez
Francisco Martínez-Álvarez Pablo de Olavide University
Alicia Troncoso
Alicia Troncoso Pablo de Olavide University
Miguel A. Toro
Miguel A. Toro Technical University of Madrid
César Hervás-Martínez
César Hervás-Martínez University of Córdoba
Eduardo F. Camacho
Eduardo F. Camacho University of Seville
Sancho Salcedo-Sanz
Sancho Salcedo-Sanz University of Alcalá
Mario Piattini
Mario Piattini University of Castilla-La Mancha
Paul R. Cohen
Paul R. Cohen University of Pittsburgh
Jaume Bacardit
Jaume Bacardit Newcastle University
Francisco Herrera
Francisco Herrera University of Granada

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