World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
34
Citations
6533
World Ranking
12006
National Ranking
49

Teresa B. Ludermir 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 Teresa B. Ludermir 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: 352 publications — 82nd percentile

82% of scientists in this discipline score the same or lower.

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

Teresa B. Ludermir 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 Teresa B. Ludermir 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: 34 D-Index — 16th percentile

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

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

Overview

Teresa B. Ludermir is affiliated with the Federal University of Pernambuco in Brazil. Their research primarily focuses on computer science, with a strong emphasis on artificial intelligence and related subfields.

Their publication record includes contributions to multiple areas, notably artificial intelligence, computer vision and pattern recognition, computational theory and mathematics, management science and operations research, and electrical and electronic engineering.

Key topics addressed in their work include:

  • Neural Networks and Applications
  • Metaheuristic Optimization Algorithms Research
  • Advanced Neural Network Applications
  • Machine Learning and Data Classification
  • Evolutionary Algorithms and Applications
  • Stochastic Gradient Optimization Techniques
  • Advanced Multi-Objective Optimization Algorithms

Some of the recent papers authored by Teresa B. Ludermir are as follows:

  • Inteligência Artificial e Aprendizado de Máquina: estado atual e tendências, 2021, Estudos Avançados

Other influential recent papers in related domains by frequent collaborators or in prominent venues include:

  • A systematic literature review on general parameter control for evolutionary and swarm-based algorithms, 2020, Swarm and Evolutionary Computation
  • Configurable sublinear circuits for quantum state preparation, 2023, Quantum Information Processing
  • Entropic Out-of-Distribution Detection: Seamless Detection of Unknown Examples, 2021, IEEE Transactions on Neural Networks and Learning Systems
  • An evaluation of k-means as a local search operator in hybrid memetic group search optimization for data clustering, 2020, Natural Computing

Teresa B. Ludermir has frequently collaborated with several researchers, including:

  • David Macêdo
  • Cleber Zanchettin
  • Luciano D. S. Pacífico
  • Felipe Farias
  • Carmelo J. A. Bastos-Filho

Their work is often published in a variety of venues, with notable recurring contributions appearing in:

  • arXiv (Cornell University)
  • Applied Soft Computing
  • Estudos Avançados
  • Swarm and Evolutionary Computation
  • Quantum Information Processing

Teresa B. Ludermir's research contributes extensively to the advancement of artificial intelligence through studies in neural networks, optimization algorithms, and machine learning, integrating computational theory with practical applications and optimization techniques.

Best Publications

  • Redes neurais artificiais: teoria e aplicações

    Antonio de Pádua Braga;Teresa Bernarda Ludermir;André Carlos Ponce de Leon Ferreira Carvalho

  • Clustering cancer gene expression data: a comparative study

    Marcílio Carlos Pereira de Souto;Marcílio Carlos Pereira de Souto;Ivan G. Costa;Ivan G. Costa;Daniel S. A. de Araujo;Daniel S. A. de Araujo;Teresa Bernarda Ludermir

  • An Optimization Methodology for Neural Network Weights and Architectures

    T.B. Ludermir;A. Yamazaki;C. Zanchettin

  • Forecasting models for interval-valued time series

    André Luis S. Maia;Francisco de A. T. de Carvalho;Teresa B. Ludermir

  • Meta-learning approaches to selecting time series models

    Ricardo B.C. Prudêncio;Teresa B. Ludermir

  • Weightless neural models

    Teresa B Ludermir;Wilson R de Oliveira

  • Quantum perceptron over a field and neural network architecture selection in a quantum computer

    Adenilton José da Silva;Teresa Bernarda Ludermir;Wilson Rosa de Oliveira

  • Many Objective Particle Swarm Optimization

    E.M.N. Figueiredo;T.B. Ludermir;C.J.A. Bastos-Filho

  • Ranking and selecting clustering algorithms using a meta-learning approach

    M.C.P. de Souto;R.B.C. Prudencio;R.G.F. Soares;D.S.A. de Araujo

  • Particle Swarm Optimization of Neural Network Architectures andWeights

    M. Carvalho;T.B. Ludermir

  • Comparison of new activation functions in neural network for forecasting financial time series

    Gecynalda S. da S. Gomes;Teresa B. Ludermir;Leyla M. M. R. Lima

  • A multi-objective memetic and hybrid methodology for optimizing the parameters and performance of artificial neural networks

    Leandro M. Almeida;Teresa B. Ludermir

  • A hybrid evolutionary decomposition system for time series forecasting

    João F.L. de Oliveira;Teresa B. Ludermir

  • Fundamentos de redes neurais artificiais

    André Carlos Ponce de Leon Ferreira Carvalho;Antonio de Pádua Braga;Teresa Bernarda Ludermir

  • Optimization of neural network weights and architectures for odor recognition using simulated annealing

    A. Yamazaki;M.C.P. de Souto;T.B. Ludermir

  • Inteligência Artificial e Aprendizado de Máquina: estado atual e tendências

    Teresa Bernarda Ludermir

  • Comparative study on normalization procedures for cluster analysis of gene expression datasets

    M.C.P. de Souto;D.S.A. de Araujo;I.G. Costa;R. Soares

  • An evolutionary extreme learning machine based on group search optimization

    D. N. G. Silva;L. D. S. Pacifico;T. B. Ludermir

  • An approach to reservoir computing design and training

    Aida A. Ferreira;Teresa B. Ludermir;Ronaldo R. B. De Aquino

  • Hybrid Training Method for MLP: Optimization of Architecture and Training

    C. Zanchettin;T. B. Ludermir;L. M. Almeida

  • Polypyrrole based aroma sensor

    J.E.G. de Souza;B.B. Neto;F.L. dos Santos;C.P. de Melo

  • Investigating the use of alternative topologies on performance of the PSO-ELM

    Elliackin M. N. Figueiredo;Teresa B. Ludermir

Frequent Co-Authors

André C. P. L. F. de Carvalho
André C. P. L. F. de Carvalho Universidade de São Paulo
Carlos Soares
Carlos Soares University of Porto
Alex A. Freitas
Alex A. Freitas University of Kent
Jose A. Lozano
Jose A. Lozano Basque Center for Applied Mathematics
Barbara Hammer
Barbara Hammer Bielefeld University
João Gama
João Gama University of Porto
Fakhri Karray
Fakhri Karray Mohamed bin Zayed University of Artificial Intelligence
Janusz Kacprzyk
Janusz Kacprzyk Systems Research Institute
Haibo He
Haibo He University of Rhode Island

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