2022
DOI: 10.3390/app122312011
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Optimization of Discrete Wavelet Transform Feature Representation and Hierarchical Classification of G-Protein Coupled Receptor Using Firefly Algorithm and Particle Swarm Optimization

Abstract: Ineffective protein feature representation poses problems in protein classification in hierarchical structures. Discrete wavelet transform (DWT) is a feature representation method which generates global and local features based on different wavelet families and decomposition levels. To represent protein sequences, the proper wavelet family and decomposition level must be selected. This paper proposed a hybrid optimization method using particle swarm optimization and the firefly algorithm (FAPSO) to choose the … Show more

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Cited by 8 publications
(4 citation statements)
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“…In the wavelet transform, the scale factor a and the time shift b are taken to be discrete according to certain rules, also known as the discrete wavelet transform (DWT). If the scale factor a and the time shift b are chosen according to a power of 2, the analysis of the signal will become more accurate and efficient [15,16]. The wavelet function can be written as [14].…”
Section: Wavelet Transformmentioning
confidence: 99%
“…In the wavelet transform, the scale factor a and the time shift b are taken to be discrete according to certain rules, also known as the discrete wavelet transform (DWT). If the scale factor a and the time shift b are chosen according to a power of 2, the analysis of the signal will become more accurate and efficient [15,16]. The wavelet function can be written as [14].…”
Section: Wavelet Transformmentioning
confidence: 99%
“…In the process of performing the wavelet transform, the scale factor a and the time shift b are theoretically continuously varying, but this is a calculation that a computer cannot complete in a finite amount of time, so in the wavelet transform, the scale factor a and the time shift b are taken to be discrete according to certain rules, also known as the discrete wavelet transform (DWT). The scale factor a and the time shift b are chosen according to a power of 2, and the analysis of the signal becomes more accurate and efficient [16,17]. The wavelet function can be further written as.…”
Section: Wavelet Transformmentioning
confidence: 99%
“…Therefore, it is necessary to study noise reduction processing for UAV flight data and combine it with deep learning models to improve the Accuracy of the anomaly detection algorithm in detecting noisy data. Wavelet decomposition is a signal processing technology based on wavelet transform, which can be used to remove noise in signals and has been widely used in many research fields [43][44][45][46][47][48].…”
Section: Introductionmentioning
confidence: 99%