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To ensure structural safety and integrity, a novel framework is developed for detecting the loosening of multi-bolt connections using wavelet entropy of vibro-acoustic modulation (VAM) signals. Wavelet entropy is employed as the dynamic index to capture the intricate time-frequency characteristics that are indicative of the connection status. Taking the wavelet entropy vectors as input, the proposed framework distinguishes itself by integrating a Transformer model for high-dimensional feature extraction with the recursive feature elimination (RFE) for essential feature selection, followed by a support vector machine (SVM) model for classification. Specifically, the Transformer model with innovative positional encoding capability helps to extract the time-dependent transient features that are sensitive to the bolt loosening. The RFE process reduces the data dimensionality while discerning the diagnostic information for more accurate classification. Through the experiment on a four-bolt joint, the identification results with cross-validation showed high accuracy and robustness of the proposed framework across various loosening cases. It outperformed the traditional SVM, long short-term memory network (LSTM), convolutional neural network (CNN)-SVM models without and with RFE, as well as the Transformer-SVM model without RFE, achieving an accuracy increase of 15.72%, 11.74%, 9.47%, 5.49%, and 5.06%, respectively. The proposed framework was demonstrated to be able to learn the damage-sensitive features more effectively from wavelet entropy data, marking a significant advancement in the health monitoring of engineering structures with high-strength bolt connections. [ABSTRACT FROM AUTHOR]

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Each person has a unique gait, which is their method or trait for walking. This movement follows a basic structure, although there are variances that differ from person to person. Gait analysis examines a number of elements of a person's gait pattern as they run or walk. The measurement tools utilized have a significant impact on the gait analysis's validity and reliability. The effectiveness of the machine learning algorithms K-Nearest Neighbour (KNN), Support Vector Machine (SVM), and Random Forest for biometric detection utilizing individual gaits was examined in this study. With the input data being an output signal from a gyroscope sensor integrated into a smartphone, multilevel wavelet entropy (MWE) is employed as a feature extraction technique. The results of the performance testing revealed that 85% accuracy was the greatest level for identifying gait data. These conclusions were reached by classifying data using either the KNN or Random Forest algorithms with MWE and Db2 mother wavelets at all decomposition levels, from 1 to 5. With 10 data for each subject, the suggested method was evaluated on 20 subjects. [ABSTRACT FROM AUTHOR]

Qian Yang,1 Lingfeng Liu,1 Jing Wang,2 Ying Zhang,2 Nan Jiang,3 Meiyun Zhang2 1Tianjin Union Medical Center, Tianjin Medical University, Tianjin, 300070, People’s Republic of China; 2Department of Neurology, Tianjin Union Medical Center, Tianjin, 300121, People’s Republic of China; 3School of Mechanical Engineering, Tianjin University, Tianjin, 300354, People’s Republic of ChinaCorrespondence: Meiyun Zhang, Email zmy22202@aliyun.comObjective: To investigate the changes in the wavelet entropy during wake and different sleep stages in patients with insomnia disorder.Methods: Sixteen patients with insomnia disorder and sixteen normal controls were enrolled. They underwent scale assessment and two consecutive nights of polysomnography (PSG). Wavelet entropy analysis of electroencephalogram (EEG) signals recorded from all participants in the two groups was performed. The changes in the integral wavelet entropy (En) and individual-scale wavelet entropy (En(a)) during wake and different sleep stages in the two groups were observed, and the differences between the two groups were compared.Results: The insomnia disorder group exhibited lower En during the wake stage, and higher En during the N3 stage compared with the normal control group (all P < 0.001). In terms of En(a), patients with insomnia disorder exhibited lower En(a) in the β and α frequency bands during the wake stage compared with normal controls (β band, P < 0.01; α band, P < 0.001), whereas they showed higher En(a) in the β and α frequency bands during the N3 stage than normal controls (β band, P < 0.001; α band, P < 0.001).Conclusion: Wavelet entropy can reflect the changes in the complexity of EEG signals during wake and different sleep stages in patients with insomnia disorder, which provides a new method and insights about understanding of pathophysiological mechanisms of insomnia disorder. Wavelet entropy provides an objective indicator for assessing sleep quality.Keywords: insomnia disorder, sleep stages, wavelet entropy, polysomnography

The purpose of this work is to study the mechanism of running-in during friction and to determine the informative parameters characterizing the degree of its completion. During friction, contact interaction of rough surfaces causes various wave phenomena covering a wide range of frequencies, the subsequent frequency analysis can provide information about the sizes of wave sources and thereby clarify the mechanism of interaction between surface roughness. The using of the wavelet transform for processing the signals of audible acoustic emission made it possible to determine the beginning and the end of the change in the frequency ranges of the interaction of roughness. The code developed by the authors was used to analyze the acoustic emission signals by using wavelet energy and entropy criteria. The mother wavelet was chosen by carefully evaluating the effectiveness of 54 preliminary candidates for the mother wavelet from 7 wavelet families, according to three criteria: (1) maximum wavelet energy; (2) Shannon entropy minimum; and (3) maximum energy-to-Shannon entropy ratio. [ABSTRACT FROM AUTHOR]

With a global COVID-19 pandemic, the number of confirmed patients increases rapidly, leaving the world with very few medical resources. Therefore, the fast diagnosis and monitoring of COVID-19 are one of the world's most critical challenges today. Artificial intelligence-based CT image classification models can quickly and accurately distinguish infected patients from healthy populations. Our research proposes a deep learning model (WE-SAJ) using wavelet entropy for feature extraction, two-layer FNNs for classification and the adaptive Jaya algorithm as a training algorithm. It achieves superior performance compared to the Jaya-based model. The model has a sensitivity of 85.47±1.84, specificity of 87.23±1.67 precision of 87.03±1.34, an accuracy of 86.35±0.70, and F1 score of 86.23±0.77, Matthews correlation coefficient of 72.75±1.38, and feature mutual information of 86.24±0.76. Our experiments demonstrate the potential of artificial intelligence techniques for COVID-19 diagnosis and the effectiveness of the Self-adaptive Jaya algorithm compared to the Jaya algorithm for medical image classification tasks.

Among the most significant characteristics of human beings is their ability to feel emotions. In recent years, human-machine interface (HM) research has centered on ways to empower the classification of emotions. Mainly, human-computer interaction (HCI) research concentrates on methods that enable computers to reveal the emotional states of humans. In this research, an emotion detection system based on visual IAPPS pictures through EMOTIV EPOC EEG signals was proposed. We employed EEG signals acquired from channels (AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8, AF4) for individuals in a visual induced setting (IAPS fear and neutral aroused pictures). The wavelet packet transform (WPT) combined with the wavelet entropy algorithm was applied to the EEG signals. The entropy values were extracted for every two classes. Finally, these feature matrices were fed into the SVM (Support Vector Machine) type classifier to generate the classification model. Also, we evaluated the proposed algorithm as area under the ROC (Receiver Operating Characteristic) curve, or simply AUC (Area under the curve) was utilized as an alternative single-number measure. Overall classification accuracy was obtained at 91.0%. For classification, the AUC value given for SVM was 0.97. The calculations confirmed that the proposed approaches are successful for the detection of the emotion of fear stimuli via EMOTIV EPOC EEG signals and that the accuracy of the classification is acceptable.

This study aimed to uncover the impact of COVID-19 on the leading cryptocurrency (Bitcoin) and on sustainable finance with specific attention to their potential long memory properties. In this article, the application of the selected methodologies is based on a fractal and entropy analysis of the econometric model in the financial market. To detect the regularity/irregularity property of a time series, approximate entropy is introduced to measure deterministic chaos. Using daily data for Bitcoin and sustainable finance, namely DJSW, Green Bond, Carbon, and Clean Energy, we examine long memory behaviour by employing a rescaled range statistic (R/S) methodology. The results of the research present that the returns of Bitcoin, the Dow Jones Sustainability World Index (DJSW), Green Bond, Carbon, and Clean Energy have a significant long memory. Contrastingly, an interdisciplinary approach, namely wavelet analysis, is also used to obtain complementary results. Wavelet analysis can provide warning information about turmoil phenomena and offer insights into co-movements in the time–frequency space. Our findings reveal that approximate entropy shows crisis (turmoil) conditions in the Bitcoin market, despite the nature of the pandemic's origin. Crucially, compared to Bitcoin assets, sustainable financial assets may play a better safe haven role during a pandemic turmoil period. The policy implications of this study could improve trading strategies for the sake of portfolio managers and investors during crisis and non-crisis periods. [ABSTRACT FROM AUTHOR]

The purpose of this work is to study the mechanism of running-in during friction and to determine the informative parameters characterizing the degree of its completion. During friction, contact interaction of rough surfaces causes various wave phenomena covering a wide range of frequencies, the subsequent frequency analysis can provide information about the sizes of wave sources and thereby clarify the mechanism of interaction between surface roughness. The using of the wavelet transform for processing the signals of audible acoustic emission made it possible to determine the beginning and the end of the change in the frequency ranges of the interaction of roughness. The code developed by the authors was used to analyze the acoustic emission signals by using wavelet energy and entropy criteria. The mother wavelet was chosen by carefully evaluating the effectiveness of 54 preliminary candidates for the mother wavelet from 7 wavelet families, according to three criteria: (1) maximum wavelet energy; (2) Shannon entropy minimum; and (3) maximum energy-to-Shannon entropy ratio.

Community-acquired pneumonia (CAP) is considered a sort of pneumonia developed outside hospitals and clinics. To diagnose community-acquired pneumonia (CAP) more efficiently, we proposed a novel neural network model. We introduce the 2-dimensional wavelet entropy (2d-WE) layer and an adaptive chaotic particle swarm optimization (ACP) algorithm to train the feed-forward neural network. The ACP uses adaptive inertia weight factor (AIWF) and Rossler attractor (RA) to improve the performance of standard particle swarm optimization. The final combined model is named WE-layer ACP-based network (WACPN), which attains a sensitivity of 91.87 ± 1.37%, a specificity of 90.70 ± 1.19%, a precision of 91.01 ± 1.12%, an accuracy of 91.29 ± 1.09%, F1 score of 91.43 ± 1.09%, an MCC of 82.59 ± 2.19%, and an FMI of 91.44 ± 1.09%. The AUC of this WACPN model is 0.9577. We find that the maximum deposition level chosen as four can obtain the best result. Experiments demonstrate the effectiveness of both AIWF and RA. Finally, this proposed WACPN is efficient in diagnosing CAP and superior to six state-of-the-art models. Our model will be distributed to the cloud computing environment. [ABSTRACT FROM AUTHOR]

The constant-Q Gabor atom is developed for spectral power, information, and uncertainty quantification from time–frequency representations. Stable multiresolution spectral entropy algorithms are constructed with continuous wavelet and Stockwell transforms. The recommended processing and scaling method will depend on the signature of interest, the desired information, and the acceptable levels of uncertainty of signal and noise features. Selected Lamb wave signatures and information spectra from the 2022 Tonga eruption are presented as representative case studies. Resilient transformations from physical to information metrics are provided for sensor-agnostic signal processing, pattern recognition, and machine learning applications. [ABSTRACT FROM AUTHOR]

Changing global climate suggests a warmer future which may alter the hydrological cycle, affecting surface water as well as groundwater resources. The wavelet entropy (WE) criterion is a new indicator for analyzing time series fluctuations. In this study, effective factors decreasing the groundwater level in Khorramabad city during the years 2005–2018 were evaluated by the use of WE criterion. In general, it can be said that the decreasing WE criterion or time series complexity of a phenomenon shows the time series decrease of natural fluctuation, which leads to an unfavorable trend. In this regard, in order to identify the factors affecting groundwater level decrease in Khorramabad, the groundwater level was divided into 4 time periods, and after being investigated, the monthly time series of runoff, temperature, and precipitation of this city were also divided into 4 periods. Each of these subsets were decomposed into several other subsets at different time scales under the wavelet transform, and finally, after calculation of the normalized wavelet energy for this subset, the WE criterion was calculated for each period. Investigation of WE complexity shows a 21.3% decrease in groundwater level in the second period, but in the third and fourth periods, it increased by 145 and 272%, respectively. Also, according to the results of analysis of WE changes for the precipitation time series, 35.2, 32.8, and 10.06% decrease in the second, third, and fourth periods were shown, respectively. The air temperature time series complexity decreased by 26.8% only in the third time period and in the second and fourth period, it shows an increase of 29.65 and 34.7%, respectively. However, the runoff time series did not show any reduction complexity according to the WE criterion. These results indicate that the impact of climatic factors has been more effective than human factors in reducing the groundwater level of Khorramabad. HIGHLIGHTS Temperature and precipitation parameters are considered as climatic factors.; Runoff parameters are considered as human factors.; Used the wavelet entropy criterion to investigate the cause of decrease in groundwater level.;

Exploring the shape and direction of hidden cracks in a tunnel lining structure is one of the main objectives of ground penetrating radar (GPR) map interpretation. The most important factor that restricts the migration imaging of hidden cracks is the propagation velocity of electromagnetic waves. Determining the optimal electromagnetic wave velocity is the key to truthfully restoring the actual shape of hidden cracks. To study the GPR characteristic response signals of hidden cracks, forward simulation and model experiments of different cracks were performed. Subsequently, a method to determine the optimal electromagnetic wave velocity based on the wavelet entropy theory was proposed, and the frequency wavenumber domain migration (F-K) and Kirchhoff integral migration imaging method were combined. Horizontal, S-type, and inclined hidden fractures were examined by migration imaging. The results show that the radar characteristic response images of different cracks can be simulated forward by using the finite difference time domain method to write the fracture model instruction. Based on the wavelet entropy theory, the error range between the estimated value and true value was controlled within 4%. Taking the optimal electromagnetic wave velocity as the velocity parameter of the conventional migration method can make the migration more effective and suppress the interference of echo signals so that the diffraction wave converges, and the energy is more concentrated; thus, the real fracture morphology can be restored to the greatest extent. The research results can provide technical support for the fine detection of hidden quality defects in tunnel lining structures by GPR mapping. [ABSTRACT FROM AUTHOR]

With a global COVID-19 pandemic, the number of confirmed patients increases rapidly, leaving the world with very few medical resources. Therefore, the fast diagnosis and monitoring of COVID-19 are one of the world's most critical challenges today. Artificial intelligence-based CT image classification models can quickly and accurately distinguish infected patients from healthy populations. Our research proposes a deep learning model (WE-SAJ) using wavelet entropy for feature extraction, two-layer FNNs for classification and the adaptive Jaya algorithm as a training algorithm. It achieves superior performance compared to the Jaya-based model. The model has a sensitivity of 85.47±1.84, specificity of 87.23±1.67 precision of 87.03±1.34, an accuracy of 86.35±0.70, and F1 score of 86.23±0.77, Matthews correlation coefficient of 72.75±1.38, and feature mutual information of 86.24±0.76. Our experiments demonstrate the potential of artificial intelligence techniques for COVID-19 diagnosis and the effectiveness of the Self-adaptive Jaya algorithm compared to the Jaya algorithm for medical image classification tasks. [ABSTRACT FROM AUTHOR]

Among the most significant characteristics of human beings is their ability to feel emotions. In recent years, human-machine interface (HM) research has centred on ways to empower the classification of emotions. Mainly, human-computer interaction (HCI) research concentrates on methods that enable computers to reveal the emotional states of humans. This research proposed an emotion detection system based on visual IAPPS pictures through EMOTIV EPOC EEG signals. We employed EEG signals acquired from channels (AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8, AF4) for individuals in a visually induced setting (IAPS fear and neutral aroused pictures). The wavelet packet transform (WPT) combined with the wavelet entropy algorithm was applied to the EEG signals. The entropy values were extracted for every two classes. Finally, these feature matrices were fed into the SVM (Support Vector Machine) type classifier to generate the classification model. Also, we evaluated the proposed algorithm as an area under the ROC (Receiver Operating Characteristic) curve, or simply AUC (Area under the curve) was utilised as an alternative single-number measure. Overall classification accuracy was obtained at 91.0%. For classification, the AUC value given for SVM was 0.97. The calculations confirmed that the proposed approaches successfully detect the emotion of fear stimuli via EMOTIV EPOC EEG signals and that the classification accuracy is acceptable. [ABSTRACT FROM AUTHOR]

Signal-based acoustic emission data are analysed in this research work for identifying the damage modes in carbon fibre–reinforced plastic (CFRP) composites. The research work is divided into three parts: analysis of the shifting in the spectral density of acoustic waveforms, use of waveform entropy for selecting the best wavelet and implementation of wavelet packet transform (WPT) for identifying the damage process. The first two methodologies introduced in this research work are novel. Shifting in the spectral density is introduced in analogous to 'flicker noise' which is popular in the field of waveform processing. The entropy-based wavelet selection is refined by using quadratic Renyi's entropy and comparing the spectral energy of the dominating frequency band of the acoustic waveforms. Based on the method, 'dmey' wavelet is selected for analysing the waveforms using WPT. The slope values of the shifting in spectral density coincide with the results obtained from WPT in characterising the damage modes. The methodologies introduced in this research work are promising. They serve the purpose of identifying the damage process effectively in the CFRP composites. [ABSTRACT FROM AUTHOR]

Among various types of brain computer interfaces (BCIs), steady state visually evoked potential (SSVEP) based BCIs can provide high information transfer rate (ITR), however the users could suffer serious fatigue that may induce discomfort, health hazards and deterioration of system performance. To overcome the fatigue obstacle, the first step is to detect the fatigue accurately, reliably and quickly. This paper proposes an approach based on the wavelet entropy of the measured EEG to fatigue detection in real time when using an SSVEP-BCI. Specifically, the wavelet analysis is first applied to the EEG, resulting in the approximation and detail components at different levels. The sample entropy values of these components are then calculated to generate features for classification. Experimental results identified the entropy of the lower frequency components (0 – 4.6875Hz) as the most important feature. The proposed wavelet entropy improved the fatigue detection accuracy to 87.7% from 65.1% by the traditional entropy method, when distinguishing subjects’ mental states between alert (before task) and fatigue (after task). Furthermore, the detection accuracy based on the state of art multiple conventional fatigue indices can be improved from 91.9% to 96.5% by replacing the delta band amplitude with the new wavelet entropy feature.

Reliable operation of synchronous generators in hydroelectric power plants is crucial for avoiding unplanned stoppages that can incur substantial costs. The damper winding of salient pole synchronous generators (SPSGs) contributes to machine operation only during transient periods; however, it is a critical component that preserves the dynamic stability and protects the rotor in case of a fault. Consequently, detection of a broken damper bar (BDB) fault is vital for safe operation. Current methods for the BDB detection depend on visual inspection or offline tests. However, most of the recently proposed approaches have used invasive sensors that can detect BDB faults only during transient operation. In this article, a novel method is proposed based on a noninvasive sensor with high sensitivity to BDB faults that can identify a BDB fault either during transient operation or in the steady-state (SS) period. The effectiveness of the proposed method is validated by finite-element modeling and by experimental results from a 100-kVA custom-made SPSG. The proposed method is confirmed to provide a reliable and sensitive diagnosis of BDB faults during transient or SS operation, even in noisy environments. [ABSTRACT FROM AUTHOR]

In recent years, COVID-19 has spread rapidly among humans. Chest CT is an effective means of diagnosing COVID-19. However, the diagnosis of CT images still depends on the doctor's visual judgment and medical experience. This takes a certain amount of time and may lead to misjudgment. In this paper, a new algorithm for automatic diagnosis of COVID-19 based on chest CT image data was proposed. The algorithm comprehensively uses WE to extract image features, uses ELM for training, and finally passes k-fold CV validation. After evaluating and detecting performance on 296 chest CT images, our proposed method is superior to state-of-the-art approaches in terms of sensitivity, specificity, precision, accuracy, F1, MCC and FMI. [ABSTRACT FROM AUTHOR]

Diagnostic ambiguity between chronic pulmonary diseases like asthma and Chronic Obstructive Pulmonary Disease (COPD) is very high, as they exhibit similar symptoms, which is the factor responsible for misdiagnosis, leading to heavy deaths every year. To prevent misdiagnosis, some useful work is highly required. This article presents the implementation of a computerized lung sound (LS) based method to classify asthma and COPD cases. The study is conducted on 80 asthma, 80 COPD and 80 healthy LSs. The LS denoising is carried out using empirical mode decomposition (EMD), Hurst analysis and spectral subtraction method. Wavelet entropy (WE) and wavelet packet energy (WPE) features of LS's are extracted. Various classifiers like support vector machine (SVM), decision tree (DT), k-nearest neighbor (KNN) and discriminant analysis (DA) are accessed to classify healthy, COPD and asthma using WE and WPE features of LS to produce better outcomes. Using the proposed algorithm, the study discriminates between healthy, asthma and COPD cases based on LS with a considerable classification accuracy of 99.3% using the decision tree (DT) classifier. Thus, the study confirms the successful differentiation of asthma and COPD based on LS. Future endeavours will be based on the validation of this algorithm to distinguish the real-time LS data acquired from asthmatic and COPD patients. [ABSTRACT FROM AUTHOR]

This work dives into the spectral realm of acoustic emission waveforms. The acoustic emission waveforms carry a footprint of source, its mechanism, and the information of the medium through which it travels. The idiosyncrasies of these waveforms cannot be visualized from the time-domain parameters. The complex fracture process of the heterogeneous composite, such as concrete, reflects in the spectral disorder of acoustic emission signals. The use of wavelet entropy is proposed to estimate the spectral disorder. To evaluate wavelet entropy, the relative energy distribution in frequency sub-bands is determined using the wavelet transform. The Shannon entropy formulation as a wavelet entropy is utilized for discriminating spatiotemporally distributed acoustic emission events according to their respective level of disorder. The possible twofold application of the wavelet entropy as a signal discriminator and a damage index is qualitatively demonstrated. The increase in the statistical variance of wavelet entropy distribution with the increase in stress level reveals the presence of multi-sources as well as multi-mechanistic fracture process. [ABSTRACT FROM AUTHOR]

Damage evolution in concrete is a complex phenomenon involving micro-cracks that originate from different material phases (mortar matrix, ITZ and aggregate), and culminate into a major crack. The information about the individual damaged material phases can provide major insights into the global failure behaviour, cracking path, and fracture processes. The evolution of damage, damage mechanisms and the material phase of origin of micro-cracks is highly influenced by the type of concrete (Normal and high strength). In order to investigate the evolution of damage in concrete of varying strength, notched beam specimens with different water-to-cement ratios have been prepared and tested under C M O D control. The evolution and growth of damage have been continuously monitored during testing through acoustic emission techniques. Based on acoustic emission results, it has been demonstrated that the damage progression in concrete can be better described as a function of the wavelet entropy of the AE events occurring during the entire loading duration, and therefore, an analytical model for the damage evolution has been proposed. The study reveals that, under the high-strength category, the level of deterioration reduces as the w / c ratio rises, while in normal-strength concrete, a reverse trend is observed. Furthermore, a novel approach has been proposed that utilizes wavelet entropy and amplitude to differentiate amongst various damaged material phases with the help of an unsupervised clustering algorithm. Normal-strength concrete with a w / c ratio of 0.4 has been used to demonstrate the proposed methodology and has been validated with the help of experiments performed on the mortar and the parent stone of aggregate. A higher occurrence of mode-I cracks compared to mode-II cracks across all material phases has been observed for the specimen. Additionally, ITZ and aggregate exhibit a greater proportion of tensile and shear cracking events than the matrix phase. In the end, a deep learning convolutional neural network (CNN) model is devised utilizing labelled scalogram images of the AE waveforms to discriminate between the various damaged material phases. A 10-fold training cross-validation of the dataset has been carried out to achieve a stable performance. The deep learning model performs well in discriminating the damaged material phases with high training, validation, and testing accuracy. • A damage variable based on wavelet entropy to evaluate the amount of deterioration in concrete has been proposed. • The damaged material phase identification with the help of acoustic emission event has been performed. • A convolutional neural network has been developed to discriminate between the AE events pertaining to different material phases. [ABSTRACT FROM AUTHOR]

Respiratory sounds reveal important information of the lungs of patients. However, the analysis of lung sounds depends significantly on the medical skills and diagnostic experience of the physicians and is a time-consuming process. The development of an automatic respiratory sound classification system based on machine learning would, therefore, be beneficial. In this study, 705 respiratory sound signals (240 crackles, 260 rhonchi, and 205 normal respiratory sounds) were acquired from 130 patients. We found that similarities between the original and wavelet decomposed signals reflected the frequency of the signals. The Gaussian kernel function was used to evaluate the wavelet signal similarity. We combined the wavelet signal similarity with the relative wavelet energy and wavelet entropy as the feature vector. A 5-fold cross-validation was applied to assess the performance of the system. The artificial neural network model, which was applied, achieved the classification accuracy and classified the respiratory sound signals with an accuracy of 85.43%.

INTRODUCTION: Image processing technology is widely used in lip recognition, which can automatically detect and analyse the unstable shape of human lips. OBJECTIVES: In this paper, we propose a new algorithm using Wavelet entropy (WE) and K-nearest neighbor (KNN) improves the accuracy of lip recognition. METHODS: At present, the two most commonly used technologies are wavelet transform and 𝐾𝐾-nearest neighbor algorithm. Wavelet transform is a set of image descriptors, and the 𝐾𝐾-nearest neighbor algorithm has high accuracy. After a large number of experiments, we propose a lip recognition method based on Wavelet entropy and 𝐾𝐾-nearest neighbor, which combines Wavelet entropy, 𝐾𝐾-nearest neighbor and K-fold cross validation. RESULTS: This method reduces the calculation time and improves the training speed. The best result of the experiment improves the accuracy to 80.08%. CONCLUSION: Therefore, our algorithm is superior to other state-of-the-art approaches of lip recognition.

INTRODUCTION: Image processing technology is widely used in lip recognition, which can automatically detect and analyse the unstable shape of human lips. OBJECTIVES: In this paper, we propose a new algorithm using Wavelet entropy (WE) and K-nearest neighbor (KNN) improves the accuracy of lip recognition. METHODS: At present, the two most commonly used technologies are wavelet transform and 𝐾𝐾-nearest neighbor algorithm. Wavelet transform is a set of image descriptors, and the 𝐾𝐾-nearest neighbor algorithm has high accuracy. After a large number of experiments, we propose a lip recognition method based on Wavelet entropy and 𝐾𝐾-nearest neighbor, which combines Wavelet entropy, 𝐾𝐾-nearest neighbor and K-fold cross validation. RESULTS: This method reduces the calculation time and improves the training speed. The best result of the experiment improves the accuracy to 80.08%. CONCLUSION: Therefore, our algorithm is superior to other state-of-the-art approaches of lip recognition. [ABSTRACT FROM AUTHOR]