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Stationary Wavelet Entropy and Cat Swarm Optimization to Detect COVID-19

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Bioinspired Systems for Translational Applications: From Robotics to Social Engineering (IWINAC 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14675))

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Abstract

Accurate and efficient approaches are urgently needed to cope with the rapid spread of COVID-19 worldwide. A novel approach is presented in this paper, which combines Stationary Wavelet Entropy (SWE) and Cat Swarm Optimization (CSO) to enhance the precision and effectiveness of COVID-19 detection. SWE, a signal processing technique, extracts informative features from medical data. At the same time, CSO, a bio-inspired optimization algorithm, is used to fine-tune the parameters of a feed-forward neural network. Integrating these two techniques within our methodology addresses the complex and evolving nature of COVID-19 detection tasks. SWE efficiently captures irregularities and patterns in medical data, providing valuable inputs to the neural network, while CSO automates parameter tuning, optimizing the network’s performance. Experimental results demonstrate the efficacy of our approach, showcasing its ability to accurately identify COVID-19 cases in diverse medical datasets. The synergy between SWE and CSO offers a promising avenue for enhancing COVID-19 detection, contributing to the global effort to combat the pandemic.

Supported by the open project of State Key Laboratory of Millimeter Waves (Grant No. K202218), the “Qinglan Project" of Jiangsu University and it is part of the PID2022-137451OB-I00 project funded by the CIN/AEI/10.13039/501100011033 and by FSE+.

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Authors and Affiliations

  1. School of Computer Engineering, Jiangsu Second Normal University, Nanjing, Jiangsu, 211200, China

    Meng Wu, Shuwen Chen & Jiaji Wang

  2. State Key Laboratory of Millimeter Waves, Southeast University, Nanjing, Jiangsu, 210096, China

    Shuwen Chen

  3. School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, UK

    Jiaji Wang & Yudong Zhang

  4. Department of Biological Sciences, Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu, 215123, China

    Shuihua Wang

  5. Department of Signal Theory, Networking and Communications, University of Granada, Granada, 52005, Spain

    Juan Manuel Gorriz

  6. Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    Yudong Zhang

Authors
  1. Meng Wu
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  3. Jiaji Wang
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  4. Shuihua Wang
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  5. Juan Manuel Gorriz
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  6. Yudong Zhang
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Corresponding authors

Correspondence to Shuwen Chen or Yudong Zhang .

Editor information

Editors and Affiliations

  1. Universidad Politécnica de Cartagena, Cartagena, Spain

    José Manuel Ferrández Vicente

  2. Polytechnic University of Valencia, Valencia, Spain

    Mikel Val Calvo

  3. Ohio State University, Columbus, OH, USA

    Hojjat Adeli

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Wu, M., Chen, S., Wang, J., Wang, S., Gorriz, J.M., Zhang, Y. (2024). Stationary Wavelet Entropy and Cat Swarm Optimization to Detect COVID-19. In: Ferrández Vicente, J.M., Val Calvo, M., Adeli, H. (eds) Bioinspired Systems for Translational Applications: From Robotics to Social Engineering. IWINAC 2024. Lecture Notes in Computer Science, vol 14675. Springer, Cham. https://doi.org/10.1007/978-3-031-61137-7_15

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  • DOI: https://doi.org/10.1007/978-3-031-61137-7_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-61136-0

  • Online ISBN: 978-3-031-61137-7

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