World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
37
Citations
8340
World Ranking
10537
National Ranking
78

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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