World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
37
Citations
8340
World Ranking
10535
National Ranking
77

Irene Yu-Hua Gu 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 Irene Yu-Hua Gu 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: 239 publications — 59th percentile

59% of scientists in this discipline score the same or lower.

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

Irene Yu-Hua Gu 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 Irene Yu-Hua Gu 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: 37 D-Index — 27th percentile

27% of scientists in this discipline score the same or lower.

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

Overview

Irene Yu-Hua Gu is affiliated with Chalmers University of Technology in Sweden. Their research primarily spans the fields of Engineering and Computer Science, with a strong emphasis on Electrical and Electronic Engineering, Neurology, and Artificial Intelligence. Their work integrates advanced methodologies within Computer Vision and Pattern Recognition as well as Radiology, Nuclear Medicine, and Imaging.

The main topics of their research focus on Brain Tumor Detection and Classification, Energy Load and Power Forecasting, Radiomics and Machine Learning in Medical Imaging, Power Quality and Harmonics, Glioma Diagnosis and Treatment, Advanced Neural Network Applications, and Machine Fault Diagnosis Techniques.

Frequent co-authors in their work have included Asgeir Store Jakola, Chenjie Ge, Mitchel S. Berger, Muhaddisa Barat Ali, and Georg Widhalm. The scientist has published multiple papers in venues such as Energies, IET Conference Proceedings, IEEE Access, BMC Medical Imaging, and Brain Sciences.

Notable recent papers include:

  • "Enlarged Training Dataset by Pairwise GANs for Molecular-Based Brain Tumor Classification" (2020, IEEE Access)
  • "Deep semi-supervised learning for brain tumor classification" (2020, BMC Medical Imaging)
  • "Domain Mapping and Deep Learning from Multiple MRI Clinical Datasets for Prediction of Molecular Subtypes in Low Grade Gliomas" (2020, Brain Sciences)
  • "Deep Feature Clustering for Seeking Patterns in Daily Harmonic Variations" (2020, IEEE Transactions on Instrumentation and Measurement)
  • "Unsupervised deep learning and analysis of harmonic variation patterns using big data from multiple locations" (2021, Electric Power Systems Research)

These publications reflect a blend of interdisciplinary research combining medical imaging, machine learning, and engineering applications.

Best Publications

  • Signal processing of power quality disturbances

    Math H. J. Bollen;Irene Yu-Hua Gu

  • Statistical modeling of complex backgrounds for foreground object detection

    Liyuan Li;Weimin Huang;Irene Yu-Hua Gu;Qi Tian

  • Foreground object detection from videos containing complex background

    Liyuan Li;Weimin Huang;Irene Y. H. Gu;Qi Tian

  • Expert System for Classification and Analysis of Power System Events

    E. Styvaktakis;M.H.J. Bollen;I.Y.H. Gu

  • Support Vector Machine for Classification of Voltage Disturbances

    P.G.V. Axelberg;Irene Yu-Hua Gu;M.H.J. Bollen

  • Estimating Interharmonics by Using Sliding-Window ESPRIT

    I.Y.-H. Gu;M.H.J. Bollen

  • Bridging the gap between signal and power

    M.H.J. Bollen;I.Y.H. Gu;S. Santoso;M.F. Mcgranaghan

  • Robust Visual Object Tracking Using Multi-Mode Anisotropic Mean Shift and Particle Filters

    Z H Khan;I Y Gu;A G Backhouse

  • An efficient 3D deep convolutional network for Alzheimer's disease diagnosis using MR images

    Karl Backstrom;Mahmood Nazari;Irene Yu-Hua Gu;Asgeir Store Jakola

  • Artificial intelligence and ambient intelligence

    Matjaz Gams;Irene Yu-Hua Gu;Aki Härmä;Andrés Muñoz

  • Classification of underlying causes of power quality disturbances: deterministic versus statistical methods

    Math H. J. Bollen;Irene Y. H. Gu;Peter G. V. Axelberg;Emmanouil Styvaktakis

  • Automatic classification of power system events using RMS voltage measurements

    E. Styvaktakis;M.H.J. Bollen;I.Y.H. Gu

  • Enlarged Training Dataset by Pairwise GANs for Molecular-Based Brain Tumor Classification

    Chenjie Ge;Irene Yu-Hua Gu;Asgeir Store Jakola;Jie Yang

  • Deepside: A general deep framework for salient object detection

    Keren Fu;Qijun Zhao;Irene Yu-Hua Gu;Jie Yang

  • A Robust Transform-Domain Deep Convolutional Network for Voltage Dip Classification

    Azam Bagheri;Irene Y. H. Gu;Math H. J. Bollen;Ebrahim Balouji

  • Foreground object detection in changing background based on color co-occurrence statistics

    Liyuan Li;Weimin Huang;I.Y.H. Gu;Qi Tian

  • Bridge the gap: signal processing for power quality applications

    Irene Yu-Hua Gu;Emmanouil Styvaktakis

  • Deep Learning and Multi-Sensor Fusion for Glioma Classification Using Multistream 2D Convolutional Networks

    Chenjie Ge;Irene Yu-Hua Gu;Asgeir Store Jakola;Jie Yang

  • Wood defect classification based on image analysis and support vector machines

    Irene Yu-Hua Gu;Henrik Andersson;Raul Vicen

  • Deep semi-supervised learning for brain tumor classification.

    Chenjie Ge;Irene Yu-Hua Gu;Asgeir Store Jakola;Jie Yang

  • Object Tracking using Incremental 2D-PCA Learning and ML Estimation

    Tiesheng Wang;I. Y. H. Gu;Pengfei Shi

Frequent Co-Authors

Math Bollen
Math Bollen Luleå University of Technology
Qi Tian
Qi Tian Huawei Technologies (China)
Wei Min Huang
Wei Min Huang Nanyang Technological University
Mats Viberg
Mats Viberg Chalmers University of Technology
Jie Yang
Jie Yang Shanghai Jiao Tong University
Surya Santoso
Surya Santoso The University of Texas at Austin
Fredrik Kahl
Fredrik Kahl Chalmers University of Technology
Yu Qiao
Yu Qiao Chinese Academy of Sciences
Zabih Ghassemlooy
Zabih Ghassemlooy Northumbria University
Gary W. Chang
Gary W. Chang National Chung Cheng University

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