World's Best Scientists 2026 revealed!
Julián Luengo

Julián Luengo

D-Index & Metrics

Computer Science

D-Index
33
Citations
13949
World Ranking
12346
National Ranking
226

Julián Luengo 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 Julián Luengo 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: 124 publications — 16th percentile

16% of scientists in this discipline score the same or lower.

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

Julián Luengo 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 Julián Luengo 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: 33 D-Index — 13th percentile

13% of scientists in this discipline score the same or lower.

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

Overview

Julián Luengo is affiliated with the University of Granada in Spain and has a research focus primarily in Computer Science, with 68 publications in this main field. Their work spans several subfields, including Artificial Intelligence, Signal Processing, Computer Vision and Pattern Recognition, Computer Networks and Communications, and Industrial and Manufacturing Engineering.

Their research covers a range of topics, notably:

  • Anomaly Detection Techniques and Applications
  • Time Series Analysis and Forecasting
  • Network Security and Intrusion Detection
  • Machine Learning and Data Classification
  • Advanced Clustering Algorithms Research
  • Explainable Artificial Intelligence (XAI)
  • Advanced Neural Network Applications

Julián Luengo has contributed to several recent publications including:

  • "A tutorial on the segmentation of metallographic images: Taxonomy, new MetalDAM dataset, deep learning-based ensemble model, experimental analysis and challenges" (2021) in Information Fusion
  • "COVIDGR Dataset and COVID-SDNet Methodology for Predicting COVID-19 Based on Chest X-Ray Images" (2020) in IEEE Journal of Biomedical and Health Informatics (authored by Siham Tabik, a frequently collaborating researcher)

The publications occur frequently in several venues such as:

  • arXiv (Cornell University) with 8 publications
  • Neurocomputing with 4 publications
  • Information Sciences with 3 publications
  • Information Fusion with 2 publications
  • Applied Soft Computing with 2 publications

Collaboration is an important aspect of Julián Luengo's work, with frequent coauthors including Francisco Herrera, Ignacio Aguilera-Martos, Iván Sevillano-García, José-Ramón Cano, and Salvador García.

The scientist's output in anomaly detection, machine learning, and artificial intelligence is supported by work in time series and clustering methodologies. Their involvement with both theoretical and applied research is reflected through contributions to datasets, new methodologies, and frameworks for evaluation, indicating a breadth of focus within their area of expertise.

Best Publications

  • KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework

    J. Alcalá-Fdez;A. Fernández;J. Luengo;J. Derrac

  • Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power

    Salvador García;Alberto Fernández;Julián Luengo;Francisco Herrera

  • Data Preprocessing in Data Mining

    Salvador Garca;Julin Luengo;Francisco Herrera

  • A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability

    S. García;A. Fernández;J. Luengo;F. Herrera

  • Big data preprocessing: methods and prospects

    Salvador García;Sergio Ramírez-Gallego;Julián Luengo;José Manuel Benítez

  • SMOTE-IPF

    José A. Sáez;Julián Luengo;Jerzy Stefanowski;Francisco Herrera

  • A Survey of Discretization Techniques: Taxonomy and Empirical Analysis in Supervised Learning

    Salvador Garcia;J. Luengo;José Antonio Sáez;Victoria López

  • COVIDGR Dataset and COVID-SDNet Methodology for Predicting COVID-19 Based on Chest X-Ray Images

    S. Tabik;A. Gomez-Rios;J. L. Martin-Rodriguez;I. Sevillano-Garcia

  • Tutorial on practical tips of the most influential data preprocessing algorithms in data mining

    Salvador García;Salvador García;Julián Luengo;Francisco Herrera;Francisco Herrera

  • On the choice of the best imputation methods for missing values considering three groups of classification methods

    Julián Luengo;Salvador García;Francisco Herrera

  • KEEL 3.0: An Open Source Software for Multi-Stage Analysis in Data Mining

    Isaac Triguero;Sergio González;Jose M. Moyano;Salvador García

  • Addressing data complexity for imbalanced data sets: analysis of SMOTE-based oversampling and evolutionary undersampling

    Julián Luengo;Alberto Fernández;Salvador García;Francisco Herrera

  • Genetics-Based Machine Learning for Rule Induction: State of the Art, Taxonomy, and Comparative Study

    A Fernández;S García;J Luengo;E Bernadó-Mansilla

  • Transforming big data into smart data: An insight on the use of the k-nearest neighbors algorithm to obtain quality data

    Isaac Triguero;Diego García-Gil;Jesús Maillo;Julián Luengo

  • A study on the use of statistical tests for experimentation with neural networks

    Julián Luengo;Salvador García;Francisco Herrera

  • Analyzing the presence of noise in multi-class problems: alleviating its influence with the One-vs-One decomposition

    José A. Sáez;Mikel Galar;Julián Luengo;Francisco Herrera

  • Enabling Smart Data: Noise filtering in Big Data classification

    Diego García-Gil;Julián Luengo;Salvador García;Francisco Herrera;Francisco Herrera

  • Towards highly accurate coral texture images classification using deep convolutional neural networks and data augmentation

    Anabel Gómez-Ríos;Siham Tabik;Julián Luengo;A. S. M. Shihavuddin

  • Tackling the problem of classification with noisy data using Multiple Classifier Systems: Analysis of the performance and robustness

    José A. SáEz;Mikel Galar;JuliáN Luengo;Francisco Herrera

  • A study on the use of imputation methods for experimentation with Radial Basis Function Network classifiers handling missing attribute values: The good synergy between RBFNs and EventCovering method

    Julián Luengo;Salvador García;Francisco Herrera

  • Predicting noise filtering efficacy with data complexity measures for nearest neighbor classification

    José A. SáEz;JuliáN Luengo;Francisco Herrera

  • On the characterization of noise filters for self-training semi-supervised in nearest neighbor classification

    Isaac Triguero;José A. Sáez;Julián Luengo;Salvador García

Frequent Co-Authors

Francisco Herrera
Francisco Herrera University of Granada
Salvador García
Salvador García University of Granada
Alberto Fernández
Alberto Fernández University of Granada
Isaac Triguero
Isaac Triguero University of Nottingham
Mikel Galar
Mikel Galar Universidad Publica De Navarra
André C. P. L. F. de Carvalho
André C. P. L. F. de Carvalho Universidade de São Paulo
Bartosz Krawczyk
Bartosz Krawczyk Rochester Institute of Technology
María José del Jesus
María José del Jesus University of Jaén
Jerzy Stefanowski
Jerzy Stefanowski Poznań University of Technology
José Manuel Benítez
José Manuel Benítez University of Granada

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring Computer Science can open the door to a variety of related fields, many of which now offer flexible online degree programs. For those interested in applying computational skills to the physical world, an online mechanical engineering degree offers a strong combination of engineering principles and technology—ideal for careers in robotics, manufacturing, or automotive design.

Students fascinated by theoretical concepts or research may want to consider an online physics degree, which blends mathematics, coding, and scientific investigation. This path often leads to opportunities in academia, R&D, or high-tech industries.

The booming tech workforce also needs data experts, making data science degrees an attractive option for students eyeing roles in analytics, artificial intelligence, and machine learning.

Another rewarding direction is pursuing an electrical engineering degree online admissions. This route merges hardware, software, and systems design, preparing graduates for highly sought-after roles in electronics, communications, and energy sectors.

Best Scientists Citing Julián Luengo

Trending Scientists

Recently Published Articles