World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
43
Citations
8920
World Ranking
7904
National Ranking
471

TM McGinnity 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 TM McGinnity 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: 410 publications — 88th percentile

88% of scientists in this discipline score the same or lower.

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

TM McGinnity 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 TM McGinnity 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: 43 D-Index — 46th percentile

46% of scientists in this discipline score the same or lower.

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

Overview

TM McGinnity is affiliated with the University of Ulster in the United Kingdom and has contributed extensively to multiple fields, including neuroscience, computer science, and engineering. Their research spans a variety of specialized subfields such as cognitive neuroscience, artificial intelligence, molecular biology, electrical and electronic engineering, and biomedical engineering.

Their work focuses on several main topics including time series analysis and forecasting, neural dynamics and brain function, anomaly detection techniques and applications, EEG and brain-computer interfaces, autism spectrum disorder research, retinal development and disorders, and the broader field of neuroscience and neural engineering.

TM McGinnity has published their research in a variety of venues. Frequent publication sources include arXiv (Cornell University), IEEE Transactions on Cognitive and Developmental Systems, IEEE Transactions on Neural Networks and Learning Systems, IEEE Access, and Nottingham Trent University's Institutional Repository.

Key recent papers by TM McGinnity encompass:

  • Deep learning in mining biological data, 2021, Nottingham Trent University's Institutional Repository (Nottingham Trent Repository)
  • AdaBoost-CNN: An adaptive boosting algorithm for convolutional neural networks to classify multi-class imbalanced datasets using transfer learning, 2020, Neurocomputing
  • Variations in the oral microbiome are associated with depression in young adults, 2021, Scientific Reports
  • The Challenges and Opportunities of Human-Centered AI for Trustworthy Robots and Autonomous Systems, 2021, IEEE Transactions on Cognitive and Developmental Systems
  • Probabilistic, Recurrent, Fuzzy Neural Network for Processing Noisy Time-Series Data, 2021, IEEE Transactions on Neural Networks and Learning Systems

The scientist frequently collaborates with several co-authors, including Sonya Coleman, Mufti Mahmud, Damien Coyle, Richard Gault, and Kieran Woodward.

Best Publications

  • Deep Learning in Mining Biological Data

    Mufti Mahmud;M. Shamim Kaiser;T. Martin McGinnity;Amir Hussain

  • A review of learning in biologically plausible spiking neural networks

    Aboozar Taherkhani;Ammar Belatreche;Yuhua Li;Georgina Cosma

  • Comparative Analysis of Spectral Approaches to Feature Extraction for EEG-Based Motor Imagery Classification

    P. Herman;G. Prasad;T.M. McGinnity;D. Coyle

  • An approach for on-line extraction of fuzzy rules using a self-organising fuzzy neural network

    Gang Leng;Thomas Martin McGinnity;Girijesh Prasad

  • Predicting a chaotic time series using a fuzzy neural network

    L. P. Maguire;B. Roche;T. M. McGinnity;L. J. McDaid

  • Fault diagnosis of electronic systems using intelligent techniques: a review

    W.G. Fenton;T.M. McGinnity;L.P. Maguire

  • AdaBoost-CNN: An Adaptive Boosting algorithm for Convolutional Neural Networks to classify Multi-Class Imbalanced datasets using Transfer Learning

    Aboozar Taherkhani;Georgina Cosma;T.M. McGinnity;T.M. McGinnity

  • Challenges for large-scale implementations of spiking neural networks on FPGAs

    L. P. Maguire;T. M. McGinnity;B. Glackin;A. Ghani

  • An on-line algorithm for creating self-organizing fuzzy neural networks

    Gang Leng;Girijesh Prasad;Thomas Martin McGinnity

  • Design for Self-Organizing Fuzzy Neural Networks Based on Genetic Algorithms

    Gang Leng;T.M. McGinnity;G. Prasad

  • Forecasting price movements using technical indicators: Investigating the impact of varying input window length

    Yauheniya Shynkevich;T.M. McGinnity;T.M. McGinnity;Sonya A. Coleman;Ammar Belatreche

  • Quantum Neural Network-Based EEG Filtering for a Brain–Computer Interface

    Vaibhav Gandhi;Girijesh Prasad;Damien Coyle;Laxmidhar Behera

  • A time-series prediction approach for feature extraction in a brain-computer interface

    D. Coyle;G. Prasad;T.M. McGinnity

  • Adaptive Hidden Markov Model With Anomaly States for Price Manipulation Detection

    Yi Cao;Yuhua Li;Sonya Coleman;Ammar Belatreche

  • EEG-Based Mobile Robot Control Through an Adaptive Brain–Robot Interface

    Vaibhav Gandhi;Girijesh Prasad;Damien Coyle;Laxmidhar Behera

  • A Distributed Task Allocation Algorithm for a Multi-Robot System in Healthcare Facilities

    Gautham P. Das;Thomas M. Mcginnity;Sonya A. Coleman;Laxmidhar Behera

  • The sound-induced flash illusion reveals dissociable age-related effects in multisensory integration.

    David P. McGovern;Eugenie Roudaia;John Stapleton;T. Martin McGinnity

  • An online supervised learning method for spiking neural networks with adaptive structure

    Jinling Wang;Ammar Belatreche;Liam Maguire;Thomas Martin Mcginnity

  • Forecasting movements of health-care stock prices based on different categories of news articles using multiple kernel learning

    Yauheniya Shynkevich;T.M. McGinnity;Sonya A. Coleman;Ammar Belatreche

  • EEG-based continuous control of a game using a 3 channel motor imagery BCI: BCI game

    Damien Coyle;Jhonatan Garcia;Abdul R Satti;T Martin McGinnity

  • Faster Self-Organizing Fuzzy Neural Network Training and a Hyperparameter Analysis for a Brain–Computer Interface

    D. Coyle;G. Prasad;T.M. McGinnity

Frequent Co-Authors

Liam Maguire
Liam Maguire University of Ulster
Girijesh Prasad
Girijesh Prasad University of Ulster
Damien Coyle
Damien Coyle University of Ulster
Laxmidhar Behera
Laxmidhar Behera Indian Institute of Technology Kanpur
Christian Hölscher
Christian Hölscher Henan University
Stefano Chessa
Stefano Chessa University of Pisa
Alessandro Saffiotti
Alessandro Saffiotti Örebro University
Mufti Mahmud
Mufti Mahmud Nottingham Trent University
J. A. Scott Kelso
J. A. Scott Kelso Florida Atlantic University
Tobi Delbruck
Tobi Delbruck ETH Zurich

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