TY - GEN
T1 - Stationary Wavelet Entropy and Cat Swarm Optimization to Detect COVID-19
AU - Wu, Meng
AU - Chen, Shuwen
AU - Wang, Jiaji
AU - Wang, Shuihua
AU - Gorriz, Juan Manuel
AU - Zhang, Yudong
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - cat swarm optimization
KW - Covid-19
KW - machine learning
KW - optimization
KW - stationary wavelet entropy
UR - http://www.scopus.com/inward/record.url?scp=85197119188&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-61137-7_15
DO - 10.1007/978-3-031-61137-7_15
M3 - Conference Proceeding
AN - SCOPUS:85197119188
SN - 9783031611360
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 150
EP - 162
BT - Bioinspired Systems for Translational Applications
A2 - Ferrández Vicente, José Manuel
A2 - Val Calvo, Mikel
A2 - Adeli, Hojjat
PB - Springer Science and Business Media Deutschland GmbH
T2 - 10th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2024
Y2 - 4 June 2024 through 7 June 2024
ER -