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Artificial Neural Networks and Machine Learning – ICANN 2022

31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings; Part IV

  • Conference proceedings
  • © 2022

Overview

Editors:
  1. Elias Pimenidis

    1. University of the West of England, Bristol, UK

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  2. Plamen Angelov

    1. Lancaster University, Lancaster, UK

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  3. Chrisina Jayne

    1. Digital Innovation, Teeside University, Middlesbrough, UK

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  4. Antonios Papaleonidas

    1. Democritus University of Thrace, Xanthi, Greece

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  5. Mehmet Aydin

    1. The University of the West of England, Bristol, UK

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 13532)

Included in the following conference series:

Conference proceedings info: ICANN 2022.

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About this book

The 4-volumes set of LNCS 13529, 13530, 13531, and 13532 constitutes the proceedings of the 31st International Conference on Artificial Neural Networks, ICANN 2022, held in Bristol, UK, in September 2022.
The total of 255 full papers presented in these proceedings was carefully reviewed and selected from 561 submissions. ICANN 2022 is a dual-track conference featuring tracks in brain inspired computing and machine learning and artificial neural networks, with strong cross-disciplinary interactions and applications.

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Table of contents (65 papers)

  1. Front Matter

    Pages i-xxii
    Download chapter PDF

  2. Analysing the Predictivity of Features to Characterise the Search Space

    • Rafet Durgut, Mehmet Emin Aydin, Hisham Ihshaish, Abdur Rakib
    Pages 1-13

  3. Boosting Feature-Aware Network for Salient Object Detection

    • Jianwei Zheng, Yubin Gu, Yuchao Feng, **shan Xu, Meiyu Zhang
    Pages 14-26

  4. Continual Learning Based on Knowledge Distillation and Representation Learning

    • **u-yan Chen, Jian-wei Liu, Wen-tao Li
    Pages 27-38

  5. Deep Feature Learning for Medical Acoustics

    • Alessandro Maria Poirè, Federico Simonetta, Stavros Ntalampiras
    Pages 39-50

  6. Feature Fusion Distillation

    • Chao Tan, Jie Liu
    Pages 51-63

  7. Feature Recalibration Network for Salient Object Detection

    • Zhenshan Tan, **aodong Gu
    Pages 64-75

  8. Feature Selection for Trustworthy Regression Using Higher Moments

    • Fabian Hinder, Johannes Brinkrolf, Barbara Hammer
    Pages 76-87

  9. Fire Detection Based on Improved-YOLOv5s

    • Mengdong Zhou, Jianjun Li, Shuai Liu
    Pages 88-100

  10. Heterogeneous Graph Neural Network for Multi-behavior Feature-Interaction Recommendation

    • Li Ma, Zheng Chen, Yingxun Fu, Yang Li
    Pages 101-112

  11. JointFusionNet: Parallel Learning Human Structural Local and Global Joint Features for 3D Human Pose Estimation

    • Zhiwei Yuan, Ya** Yan, Songlin Du, Takeshi Ikenaga
    Pages 113-125

  12. Multi-scale Feature Extraction and Fusion for Online Knowledge Distillation

    • Panpan Zou, Yinglei Teng, Tao Niu
    Pages 126-138

  13. Multi-scale Vertical Cross-layer Feature Aggregation and Attention Fusion Network for Object Detection

    • Wenting Gao, **aojuan Li, Yu Han, Yue Liu
    Pages 139-150

  14. Multi-spectral Dynamic Feature Encoding Network for Image Demoiréing

    • Qiang Dai, ** Cheng, Li Zhang
    Pages 151-162

  15. Ranking Feature-Block Importance in Artificial Multiblock Neural Networks

    • Anna Jenul, Stefan Schrunner, Bao Ngoc Huynh, Runar Helin, Cecilia Marie Futsæther, Kristian Hovde Liland et al.
    Pages 163-175

  16. Robust Sparse Learning Based Sensor Array Optimization for Multi-feature Fusion Classification

    • Leilei Zhao, Fengchun Tian, Junhui Qian, Ran Liu, Anyan Jiang
    Pages 176-186

  17. Stimulates Potential for Knowledge Distillation

    • Haifeng Qing, Jialiang Tang, **aoyan Yang, **nlei Huang, Honglin Zhu, Ning Jiang
    Pages 187-198

  18. Adaptive Compatibility Matrix for Superpixel-CRF

    • Boquan Zhou, Chunpeng Li
    Pages 199-211

  19. BERT-Based Scientific Paper Quality Prediction

    • Taiki Sasaki, Yasuaki Ito, Koji Nakano, Akihiko Kasagi
    Pages 212-223

  20. Effective ML-Block and Weighted IoU Loss for Object Detection

    • Zhenxin Yuan, **n **ao, Shuai Zhao, Linlin Jiang, Zhongtao Li
    Pages 224-235
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Other volumes

  1. Artificial Neural Networks and Machine Learning – ICANN 2022

  2. Artificial Neural Networks and Machine Learning – ICANN 2022

  3. Artificial Neural Networks and Machine Learning – ICANN 2022

  4. Artificial Neural Networks and Machine Learning – ICANN 2022

Editors and Affiliations

  • University of the West of England, Bristol, UK

    Elias Pimenidis

  • Lancaster University, Lancaster, UK

    Plamen Angelov

  • Digital Innovation, Teeside University, Middlesbrough, UK

    Chrisina Jayne

  • Democritus University of Thrace, Xanthi, Greece

    Antonios Papaleonidas

  • The University of the West of England, Bristol, UK

    Mehmet Aydin

Bibliographic Information

  • Book Title: Artificial Neural Networks and Machine Learning – ICANN 2022

  • Book Subtitle: 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings; Part IV

  • Editors: Elias Pimenidis, Plamen Angelov, Chrisina Jayne, Antonios Papaleonidas, Mehmet Aydin

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-031-15937-4

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022

  • Softcover ISBN: 978-3-031-15936-7Published: 07 September 2022

  • eBook ISBN: 978-3-031-15937-4Published: 06 September 2022

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XXII, 795

  • Number of Illustrations: 21 b/w illustrations, 259 illustrations in colour

  • Topics: Artificial Intelligence, Computer Applications, Image Processing and Computer Vision, Information Systems and Communication Service

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