Boas-type theorems for the q-Bessel Fourier transform

Berkak, El Mehdi; Loualid, El Mehdi; Daher, Radouan

doi:10.1007/s13324-021-00542-z

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Wavelet Transform1

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2023

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Cited by 14 publications

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“…i.e., transforming the signal from time or spatial domain to the frequency domain. Nevertheless, FT has a severe downside [15]. It lost the time/spatial information of the signal.…”

Section: Wavelet Transformmentioning

confidence: 99%

Community-Acquired Pneumonia Recognition by Wavelet Entropy and Cat Swarm Optimization

2022

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Aim) Community-acquired pneumonia (CAP) is a type of pneumonia acquired outside the hospital.(Methods) To recognize CAP more efficiently and more precisely, we propose a novel method-wavelet entropy (WE) is used as the feature extractor, and cat swarm optimization (shortened as CSO) is used to train an artificial neural network (ANN). Our method is abbreviated as WE-ANN-CSO. (Results) This proposed WE-ANN-CSO algorithm yields a sensitivity of 91.64±0.99%, a specificity of 90.64±2.11%, a precision of 90.96±1.81%, an accuracy of 91.14±1.12%, an F1 score of 91.29±1.04%, an MCC of 82.31±2.22%, an FMI of 91.29±1.03%, and an AUC of 0.9527.(Conclusions) This proposed WE-ANN-CSO algorithm provides better performances than four state-of-the-art approaches.

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“…i.e., transforming the signal from time or spatial domain to the frequency domain. Nevertheless, FT has a severe downside [15]. It lost the time/spatial information of the signal.…”

Section: Wavelet Transformmentioning

confidence: 99%