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Journal articles on the topic 'Wavelet approach JEL classification'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Abdessalem, Habiba, and Saloua Benammou. "A wavelet technique for the study of economic socio-political situations in a textual analysis framework." Journal of Economic Studies 45, no. 3 (2018): 586–97. http://dx.doi.org/10.1108/jes-08-2017-0231.

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Purpose The purpose of this paper is to apply the wavelet thresholding technique in order to analyze economic socio-political situations in Tunisia using textual data sets. This technique is used to remove noise from contingency table. A comparative study is done on correspondence analysis and classification results (using k-means algorithm) before and after denoising. Design/methodology/approach Textual data set is collected from an electronic newspaper that offers actual economic news about Tunisia. Both the hard and the soft-thresholding techniques are applied based on various Daubechies wa

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Mallikarjunamallu K. "Enhanced Arrhythmia Detection Using Filtered Data, CNN, Graph Convolutional Networks, and SVM on MIT-BIH and PTB Databases." Journal of Electrical Systems 20, no. 1 (2024): 511–24. http://dx.doi.org/10.52783/jes.6078.

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Arrhythmia classification and detection are essential for the early diagnosis of heart diseases, but accurately identifying arrhythmias is challenging due to the inherent noise in electrocardiogram (ECG) data. This study presents a novel method for arrhythmia detection that follows a systematic approach. First, ECG data from the MIT-BIH Arrhythmia Database and the PTB Diagnostic Database are preprocessed using three distinct filters: wavelet transform (WT), finite impulse response (FIR), and an innovative infinite impulse response (IIR) filter to remove noise. The filtered data are then proces

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Wael Abdulhassan Atiyah. "A Novel Approach for Diagnosing Transformer Internal Defects and Inrush Current Based on 1DCNN and LSTM Deep Learning." Journal of Electrical Systems 20, no. 4s (2024): 2557–72. http://dx.doi.org/10.52783/jes.3163.

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In power systems, power transformer (Pt) protection plays a vital role in ensuring that customers have a reliable power supply. Correctly recognizing inrush currents from internal defects and preventing differential relay malfunctions are two of biggest challenges facing the differential protection of power transformers. Although previous approaches suggested to overcome these issues have promising outcomes, increasing the accuracy and reducing the execution time and complexity of transformer differential relays are still interesting topics for researchers. Accordingly, a new fault diagnostic

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Aloy Anuja Mary G. "Emotion Detection Through Electrocardiogram Signal Classification in an IOT Environment with Deep Neural Networks." Journal of Electrical Systems 20, no. 3 (2024): 1620–30. http://dx.doi.org/10.52783/jes.3657.

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An ECG detects the health and rhythm of the heart by measuring the electric activity of the heart. It has also been demonstrated that a person's emotions may influence the electrical activity of the heart. As a result, studying the electrical behaviour of the heart may simply determine a person's cardiac state and emotional wellness. IoT is a new technology that is quickly gaining acceptance throughout the world. Anybody, at any time, from anywhere, may connect to any network or service because to the extraordinary power and capacity of IoT. IoT-enabled devices have revolutionized the medical

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Muna Hameed Khalaf. "Fault Location and Detection in Power Distribution Systems with Presence of Distributed Generations using Improved CNNs." Journal of Electrical Systems 20, no. 10s (2024): 7840–65. http://dx.doi.org/10.52783/jes.6999.

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This paper introduces a deep learning approach for addressing fault detection and location issues in power distribution grids. The proposed method utilizes a 20-layer deep neural network (CNN) that employs the ReLU activation function in the hidden layers and the Softmax function in the pre-classification layer. The network’s input layer dimensions are 224 x 224 pixels, with each input image being a composite of seven images, each sized 32 x 224 pixels. Data is gathered using continuous wavelet transform and Hilbert transform on each signal, followed by feature extraction and conversion into R

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T Abraham, Ajitha, and Yasim Khan M. "Age classification from fingerprints – wavelet approach." International Journal on Cybernetics & Informatics 5, no. 2 (2016): 265–74. http://dx.doi.org/10.5121/ijci.2016.5229.

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Chang, Chung, Yakuan Chen, and R. Todd Ogden. "Functional data classification: a wavelet approach." Computational Statistics 29, no. 6 (2014): 1497–513. http://dx.doi.org/10.1007/s00180-014-0503-4.

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Panda, Mrutyunjaya, Aboul Ella Hassanien, and Ajith Abraham. "Hybrid Data Mining Approach for Image Segmentation Based Classification." International Journal of Rough Sets and Data Analysis 3, no. 2 (2016): 65–81. http://dx.doi.org/10.4018/ijrsda.2016040105.

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Evolutionary harmony search algorithm is used for its capability in finding solution space both locally and globally. In contrast, Wavelet based feature selection, for its ability to provide localized frequency information about a function of a signal, makes it a promising one for efficient classification. Research in this direction states that wavelet based neural network may be trapped to fall in a local minima whereas fuzzy harmony search based algorithm effectively addresses that problem and able to get a near optimal solution. In this, a hybrid wavelet based radial basis function (RBF) ne

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PRABAKARAN, S., R. SAHU, and S. VERMA. "A WAVELET APPROACH FOR CLASSIFICATION OF MICROARRAY DATA." International Journal of Wavelets, Multiresolution and Information Processing 06, no. 03 (2008): 375–89. http://dx.doi.org/10.1142/s0219691308002409.

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Microarray technologies facilitate the generation of vast amount of bio-signal or genomic signal data. The major challenge in processing these signals is the extraction of the global characteristics of the data due to their huge dimension and the complex relationship among various genes. Statistical methods are used in broad spectrum in this domain. But, various limitations like extensive preprocessing, noise sensitiveness, requirement of critical input parameters and prior knowledge about the microarray dataset emphasise the need for better exploratory techniques. Transform oriented signal pr

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Daamouche, Abdelhamid, Latifa Hamami, Naif Alajlan, and Farid Melgani. "A wavelet optimization approach for ECG signal classification." Biomedical Signal Processing and Control 7, no. 4 (2012): 342–49. http://dx.doi.org/10.1016/j.bspc.2011.07.001.

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Learned, Rachel E., and Alan S. Willsky. "A Wavelet Packet Approach to Transient Signal Classification." Applied and Computational Harmonic Analysis 2, no. 3 (1995): 265–78. http://dx.doi.org/10.1006/acha.1995.1019.

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Birajadar, Parmeshwar, and Vikram Gadre. "A Scattering Wavelet Network-based Approach to Fingerprint Classification." SAMRIDDHI : A Journal of Physical Sciences, Engineering and Technology 14, no. 02 (2022): 130–38. http://dx.doi.org/10.18090/samriddhi.v14i02.1.

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In a large-scale automatic fingerprint identification system (AFIS), fingerprint classification is an essential indexing step to reduce the search time in a large database for accurate matching. Fingerprint classification is still a challenging machine learning problem due to large intra-class and small inter-class variability. Nonlinear elastic deformation is one of the main sources of intra-class variability, which occurs due to the non-uniform pressure applied during fingerprint acquisition and the elastic nature of the fingerprint itself. This paper proposes a novel approach to fingerprint

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JEMAI, OLFA, MOURAD ZAIED, CHOKRI BEN AMAR, and MOHAMED ADEL ALIMI. "PYRAMIDAL HYBRID APPROACH: WAVELET NETWORK WITH OLS ALGORITHM-BASED IMAGE CLASSIFICATION." International Journal of Wavelets, Multiresolution and Information Processing 09, no. 01 (2011): 111–30. http://dx.doi.org/10.1142/s0219691311003967.

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Taking advantage of both the scaling property of wavelets and the high learning ability of neural networks, wavelet networks have recently emerged as a powerful tool in many applications in the field of signal processing such as data compression, function approximation as well as image recognition and classification. A novel wavelet network-based method for image classification is presented in this paper. The method combines the Orthogonal Least Squares algorithm (OLS) with the Pyramidal Beta Wavelet Network architecture (PBWN). First, the structure of the Pyramidal Beta Wavelet Network is pro

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Srinivasa Rao, Y., G. Ravi Kumar, and G. Kesava Rao. "A New Approach for Classification of Fault in Transmission Line with Combination of Wavelet Multi Resolution Analysis and Neural Networks." International Journal of Power Electronics and Drive Systems (IJPEDS) 8, no. 1 (2017): 505. http://dx.doi.org/10.11591/ijpeds.v8.i1.pp505-511.

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An appropriate fault detection and classification of power system transmission line using discrete wavelet transform and artificial neural networks is performed in this paper. The analysis is carried out by applying discrete wavelet transform for obtained fault phase currents. The work represented in this paper are mainly concentrated on classification of fault and this classification is done based on the obtained energy values after applying discrete wavelet transform by taking this values as an input for the neural network. The proposed system and analysis is carried out in Matlab Simulink.

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Srinivasa Rao, Y., G. Ravi Kumar, and G. Kesava Rao. "A New Approach for Classification of Fault in Transmission Line with Combination of Wavelet Multi Resolution Analysis and Neural Networks." International Journal of Power Electronics and Drive Systems (IJPEDS) 8, no. 1 (2017): 505. http://dx.doi.org/10.11591/ijpeds.v8.i1.pp505-512.

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An appropriate fault detection and classification of power system transmission line using discrete wavelet transform and artificial neural networks is performed in this paper. The analysis is carried out by applying discrete wavelet transform for obtained fault phase currents. The work represented in this paper are mainly concentrated on classification of fault and this classification is done based on the obtained energy values after applying discrete wavelet transform by taking this values as an input for the neural network. The proposed system and analysis is carried out in Matlab Simulink.

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Xiao, Qiao, and Chaofeng Wang. "Adaptive wavelet base selection for deep learning-based ECG diagnosis: A reinforcement learning approach." PLOS ONE 20, no. 2 (2025): e0318070. https://doi.org/10.1371/journal.pone.0318070.

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Electrocardiogram (ECG) signals are crucial in diagnosing cardiovascular diseases (CVDs). While wavelet-based feature extraction has demonstrated effectiveness in deep learning (DL)-based ECG diagnosis, selecting the optimal wavelet base poses a significant challenge, as it directly influences feature quality and diagnostic accuracy. Traditional methods typically rely on fixed wavelet bases chosen heuristically or through trial-and-error, which can fail to cover the distinct characteristics of individual ECG signals, leading to suboptimal performance. To address this limitation, we propose a r

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van de Wouwer, G., P. Scheunders, D. van Dyck, M. de Bodt, F. Wuyts, and P. H. van de Heyning. "Voice Recognition from Spectrograms: A Wavelet Based Approach." Fractals 05, supp01 (1997): 165–72. http://dx.doi.org/10.1142/s0218348x97000735.

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The performance of a pattern recognition technique is usually determined by the ability of extracting useful features from the available data so as to effectively characterize and discriminate between patterns. We describe a novel method for feature extraction from speech signals. For this purpose, we generate spectrograms, which are time-frequency representations of the original signal. We show that, by considering this spectrogram as a textured image, a wavelet transform can be applied to generate useful features for recognizing the speech signal. This method is used for the classification o

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Charfi, Faiza, and Ali Kraiem. "An Approach for ECG Characterization and Classification Using the Combination of Wavelet Transform and Decision Tree Methods." International Journal of Systems Biology and Biomedical Technologies 1, no. 3 (2012): 72–81. http://dx.doi.org/10.4018/ijsbbt.2012070103.

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A new automated approach for Electrocardiogram (ECG) arrhythmias characterization and classification with the combination of Wavelet transform and Decision tree classification is presented. The approach is based on two key steps. In the first step, the authors adopt the wavelet transform to extract the ECG signals wavelet coefficients as first features and utilize the combination of Principal Component Analysis (PCA) and Fast Independent Component Analysis (FastICA) to transform the first features into uncorrelated and mutually independent new features. In the second step, they utilize some de

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KUMAR, SANJAY, DINESH K. KUMAR, ARUN SHARMA, and NEIL McLACHLAN. "VISUAL HAND GESTURES CLASSIFICATION USING WAVELET TRANSFORMS." International Journal of Wavelets, Multiresolution and Information Processing 01, no. 04 (2003): 373–92. http://dx.doi.org/10.1142/s0219691303000232.

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This paper presents a novel technique for classifying human hand gestures based on stationary wavelet transform (SWT) and compares the results with classification based on Hu moments. The technique uses view-based approach for representation of hand actions, and artificial neural networks (ANN) for classification. This approach uses a cumulative image-difference technique where the time between the sequences of images is implicitly captured in the representation of action. This results in the construction of Motion History Images (MHI). These MHI's are decomposed into four sub-images using SWT

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Seema, Punia, Kumar Atal Dinesh, and Singh Sarita. "ECG signal Analysis and Classification Techniques." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 4 (2020): 1388–294. https://doi.org/10.35940/ijeat.D7634.049420.

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Electrocardiogram is the measure of heart electrical activity. Our heart generate electrical signals which we used to calculate heart activity .The electrical signals of heart are transformed into waveforms which are used to measure various heart conditions. We have various techniques which we used to analyze and classified the ECG signals in MATLAB. There are many types of heart Arrhythmia like Tachycardia in which heart rate is too fast, Bradycardia in which heart rate is too slow, Atrial Fibrillation, Atrial Flutter, Ventricular Fibrillation, Permature contractions these all conditions can

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Dihin, Rasha Ali, Ebtesam AlShemmary, and Waleed Al-Jawher. "Diabetic Retinopathy Classification Using Swin Transformer with Multi Wavelet." Journal of Kufa for Mathematics and Computer 10, no. 2 (2023): 167–72. http://dx.doi.org/10.31642/jokmc/2018/100225.

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Diabetic retinopathy (DR) impacts over a third of individuals diagnosed with diabetes and stands as the leading cause of vision loss in working-age adults worldwide. Therefore, the early detection and treatment of DR can play a crucial role in minimizing vision loss. This research paper proposes a novel technique that combines Wavelet and multi-Wavelet transforms with Swin Transformer to automatically identify the progression level of diabetic retinopathy. A notable innovation of this study lies in the implementation of the multi-Wavelet transform for extracting relevant features. By incorpora

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Albkosh, Fthi M., Muhammad Suzuri Hitam, Wan Nural Jawahir Hj Wan Yussof, Abdul Aziz K. Abdul Hamid, and Rozniza Ali. "Optimization of discrete wavelet transform features using artificial bee colony algorithm for texture image classification." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (2019): 5253. http://dx.doi.org/10.11591/ijece.v9i6.pp5253-5262.

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Selection of appropriate image texture properties is one of the major issues in texture classification. This paper presents an optimization technique for automatic selection of multi-scale discrete wavelet transform features using artificial bee colony algorithm for robust texture classification performance. In this paper, an artificial bee colony algorithm has been used to find the best combination of wavelet filters with the correct number of decomposition level in the discrete wavelet transform. The multi-layered perceptron neural network is employed as an image texture classifier. The prop

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Fthi, M. Albkosh, Suzuri Hitam Muhammad, Nural Jawahir Hj Wan Yussof Wan, Aziz K. Abdul Hamid Abdul, and Ali Rozniza. "Optimization of discrete wavelet transform features using artificial bee colony algorithm for texture image classification." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (2019): 5253–62. https://doi.org/10.11591/ijece.v9i6.pp5253-5262.

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Selection of appropriate image texture properties is one of the major issues in texture classification. This paper presents an optimization technique for automatic selection of multi-scale discrete wavelet transform features using artificial bee colony algorithm for robust texture classification performance. In this paper, an artificial bee colony algorithm has been used to find the best combination of wavelet filters with the correct number of decomposition level in the discrete wavelet transform. The multi-layered perceptron neural network is employed as an image texture classifier. The prop

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Xu, Lei, Takuji Kinkyo, and Shigeyuki Hamori. "Predicting Currency Crises: A Novel Approach Combining Random Forests and Wavelet Transform." Journal of Risk and Financial Management 11, no. 4 (2018): 86. http://dx.doi.org/10.3390/jrfm11040086.

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We propose a novel approach that combines random forests and the wavelet transform to model the prediction of currency crises. Our classification model of random forests, built using both standard predictors and wavelet predictors, and obtained from the wavelet transform, achieves a demonstrably high level of predictive accuracy. We also use variable importance measures to find that wavelet predictors are key predictors of crises. In particular, we find that real exchange rate appreciation and overvaluation, which are measured over a horizon of 16–32 months, are the most important.

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Zhang, Yanyan, Gang Wang, Chaolin Teng, Zhongjiang Sun, and Jue Wang. "The Analysis of Hand Movement Distinction Based on Relative Frequency Band Energy Method." BioMed Research International 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/781769.

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For the purpose of successfully developing a prosthetic control system, many attempts have been made to improve the classification accuracy of surface electromyographic (SEMG) signals. Nevertheless, the effective feature extraction is still a paramount challenge for the classification of SEMG signals. The relative frequency band energy (RFBE) method based on wavelet packet decomposition was proposed for the prosthetic pattern recognition of multichannel SEMG signals. Firstly, the wavelet packet energy of SEMG signals in each subspace was calculated by using wavelet packet decomposition and the

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Chandrasekar, Perumal, and Vijayarajan Kamaraj. "Detection and Classification of Power Quality Disturbancewaveform Using MRA Based Modified Wavelet Transfrom and Neural Networks." Journal of Electrical Engineering 61, no. 4 (2010): 235–40. http://dx.doi.org/10.2478/v10187-010-0033-4.

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Detection and Classification of Power Quality Disturbancewaveform Using MRA Based Modified Wavelet Transfrom and Neural Networks In this paper, the modified wavelet based artificial neural network (ANN) is implemented and tested for power signal disturbances. The power signal is decomposed by using modified wavelet transform and the classification is carried by using ANN. Discrete modified wavelet transforms based signal decomposition technique is integrated with the back propagation artificial neural network model is proposed. Varieties of power quality events including voltage sag, swell, mo

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Siddiqi, Muhammad Hameed, Mohammad Azad, and Yousef Alhwaiti. "An Enhanced Machine Learning Approach for Brain MRI Classification." Diagnostics 12, no. 11 (2022): 2791. http://dx.doi.org/10.3390/diagnostics12112791.

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Magnetic Resonance Imaging (MRI) is a noninvasive technique used in medical imaging to diagnose a variety of disorders. The majority of previous systems performed well on MRI datasets with a small number of images, but their performance deteriorated when applied to large MRI datasets. Therefore, the objective is to develop a quick and trustworthy classification system that can sustain the best performance over a comprehensive MRI dataset. This paper presents a robust approach that has the ability to analyze and classify different types of brain diseases using MRI images. In this paper, global

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Daamouche, A., and F. Melgani. "Swarm Intelligence Approach to Wavelet Design for Hyperspectral Image Classification." IEEE Geoscience and Remote Sensing Letters 6, no. 4 (2009): 825–29. http://dx.doi.org/10.1109/lgrs.2009.2026191.

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Hiremath, P. S., and Rohini A. Bhusnurmath. "Texture classification using partial differential equation approach and wavelet transform." Pattern Recognition and Image Analysis 27, no. 3 (2017): 473–79. http://dx.doi.org/10.1134/s1054661817030154.

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Bapna, Sanjay, and Aryya Gangopadhyay. "A Wavelet-Based Approach to Preserve Privacy for Classification Mining." Decision Sciences 37, no. 4 (2006): 623–42. http://dx.doi.org/10.1111/j.1540-5414.2006.00141.x.

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Feng, Huanghao, Hosein M. Golshan, and Mohammad H. Mahoor. "A wavelet-based approach to emotion classification using EDA signals." Expert Systems with Applications 112 (December 2018): 77–86. http://dx.doi.org/10.1016/j.eswa.2018.06.014.

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Liu, Rui, Shiyuan Wen, and Yufei Xing. "An integrated approach for advanced vehicle classification." PLOS ONE 20, no. 2 (2025): e0318530. https://doi.org/10.1371/journal.pone.0318530.

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This study is dedicated to addressing the trade-off between receptive field size and computational efficiency in low-level vision. Conventional neural networks (CNNs) usually expand the receptive field by adding layers or inflation filtering, which often leads to high computational costs. Although expansion filtering was introduced to reduce the computational burden, the resulting receptive field is only a sparse sampling of the tessellated pattern in the input image due to the grid effect. To better trade-off between the size of the receptive field and the computational efficiency, a new mult

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Xu, Yuefan, Sen Zhang, Zhengtao Cao, Qinqin Chen, and Wendong Xiao. "Extreme Learning Machine for Heartbeat Classification with Hybrid Time-Domain and Wavelet Time-Frequency Features." Journal of Healthcare Engineering 2021 (January 11, 2021): 1–12. http://dx.doi.org/10.1155/2021/6674695.

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Automatic heartbeat classification via electrocardiogram (ECG) can help diagnose and prevent cardiovascular diseases in time. Many classification approaches have been proposed for heartbeat classification, based on feature extraction. However, the existing approaches face the challenges of high feature dimensions and slow recognition speeds. In this paper, we propose an efficient extreme learning machine (ELM) approach for heartbeat classification with multiple classes, based on the hybrid time-domain and wavelet time-frequency features. The proposed approach contains two sequential modules: (

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KUMAR, SANJAY, and DINESH K. KUMAR. "VISUAL HAND GESTURES CLASSIFICATION USING WAVELET TRANSFORM AND MOMENT BASED FEATURES." International Journal of Wavelets, Multiresolution and Information Processing 03, no. 01 (2005): 79–101. http://dx.doi.org/10.1142/s0219691305000762.

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This paper presents a novel technique for classifying human hand gestures based on stationary wavelet transform (SWT) and classification based on geometrical based moments and compares the results with the classification based on Hu-moments and wavelet approximate images. The technique uses view-based approach for representation of hand actions, and uses a cumulative image-difference technique where the time between the sequences of images is implicitly captured in the representation of action resulting in Motion History Images (MHI). These MHIs are decomposed into wavelet sub-images using SWT

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Hamdi, Nezha, Khalid Auhmani, and Moha M’Rabet Hassani. "A New Approach Based on Quantum Clustering and Wavelet Transform for Breast Cancer Classification: Comparative Study." International Journal of Electrical and Computer Engineering (IJECE) 5, no. 5 (2015): 1027. http://dx.doi.org/10.11591/ijece.v5i5.pp1027-1034.

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Feature selection involves identifying a subset of the most useful features that produce the same results as the original set of features. In this paper, we present a new approach for improving classification accuracy. This approach is based on quantum clustering for feature subset selection and wavelet transform for features extraction. The feature selection is performed in three steps. First the mammographic image undergoes a wavelet transform then some features are extracted. In the second step the original feature space is partitioned in clusters in order to group similar features. This op

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Darji, Ankit, and Divyang Pandya. "Fault diagnosis of SKF-6205 bearing with modified empirical mode decomposition." International Journal of Engineering, Science and Technology 13, no. 4 (2022): 12–20. http://dx.doi.org/10.4314/ijest.v13i4.2.

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Rolling element bearings are broadly used in the rotating machines to support static and dynamic loads. In this research, the advance signal processing techniques are use for processing of bearing fault signals. Experimental validation with genuine vibration signals calculated from bearings with seeded defects on bearing elements. The model-based fault diagnosis method has attempted to diagnose incipient fault detection and classification of bearing with data driven approach. Feature extraction technique has been developed with hybrid signal processing technique based on the band pass filter n

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ZOU, WEIBAO, ZHERU CHI, and KING CHUEN LO. "IMPROVEMENT OF IMAGE CLASSIFICATION USING WAVELET COEFFICIENTS WITH STRUCTURED-BASED NEURAL NETWORK." International Journal of Neural Systems 18, no. 03 (2008): 195–205. http://dx.doi.org/10.1142/s012906570800152x.

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Image classification is a challenging problem in organizing a large image database. However, an effective method for such an objective is still under investigation. A method based on wavelet analysis to extract features for image classification is presented in this paper. After an image is decomposed by wavelet, the statistics of its features can be obtained by the distribution of histograms of wavelet coefficients, which are respectively projected onto two orthogonal axes, i.e., x and y directions. Therefore, the nodes of tree representation of images can be represented by the distribution. T

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Swarnalatha, A., and M. Manikandan. "Intra-Vascular Ultra Sound Image Classification System for the Diagnosis of Coronary Artery Disease Using Adaptive Wavelet Transform with Support Vector Machine." Journal of Medical Imaging and Health Informatics 11, no. 5 (2021): 1509–16. http://dx.doi.org/10.1166/jmihi.2021.3811.

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In this study, an efficient Decision Support System (DSS) is presented to classify coronary artery disease using Intra-Vascular Ultra Sound (IVUS) images. IVUS images are commonly used to diagnose coronary artery diseases. Wavelet transform is a multiresolution texture analysis tool which is applied to various image analysis and classification systems. Unlike the wavelet transform, Empirical Wavelet Transform (EWT) is a dependent decomposition approach that provides superior temporal and frequency information. Hence, EWT is considered as a feature extraction approach in this study. Before extr

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Jyothsna, K. Amrutha. "Currency Classification Using Deep Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem33812.

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Among the uses of machine learning is the recognition of facial expressions. Based on the features that are derived from an image, it assigns a facial expression to one of the classes of facial expressions. Convolutional Neural Network (CNN) is a classification technique that may also be used to identify patterns in an image. We used the CNN approach to identify facial expressions in our proposed study. To increase the precision of facial emotion recognition, the wavelet transform is used after CNN processing. Seven distinct facial expressions are included in the facial expression image datase

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Khelil, Jalel, Khaled Khelil, Messaoud Ramdani, and Nadir Boutasseta. "Discrete Wavelet Design for Bearing Fault Diagnosis Using Particle Swarm Optimization." Journal Européen des Systèmes Automatisés 53, no. 05 (2020): 705–13. http://dx.doi.org/10.18280/jesa.530513.

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Rolling bearings are widely used in a large variety of industrial applications. Therefore, it is necessary to provide an efficient fault detection and diagnosis mechanism to prevent component failure and poor performance during operation. This paper proposes a novel classification scheme based on the design of discrete wavelets best adapted to vibration signal analysis in order to identify and properly classify rolling bearing defects. Through polyphase representation of the wavelet filter bank, and using the particle swarm optimization (PSO) algorithm, the appropriate discrete wavelet associa

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Liu, Zhishuai, Guihua Yao, Qing Zhang, Junpu Zhang, and Xueying Zeng. "Wavelet Scattering Transform for ECG Beat Classification." Computational and Mathematical Methods in Medicine 2020 (October 9, 2020): 1–11. http://dx.doi.org/10.1155/2020/3215681.

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An electrocardiogram (ECG) records the electrical activity of the heart; it contains rich pathological information on cardiovascular diseases, such as arrhythmia. However, it is difficult to visually analyze ECG signals due to their complexity and nonlinearity. The wavelet scattering transform can generate translation-invariant and deformation-stable representations of ECG signals through cascades of wavelet convolutions with nonlinear modulus and averaging operators. We proposed a novel approach using wavelet scattering transform to automatically classify four categories of arrhythmia ECG hea

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An, Lixin, and Wei Li. "An integrated approach to fashion flat sketches classification." International Journal of Clothing Science and Technology 26, no. 5 (2014): 346–66. http://dx.doi.org/10.1108/ijcst-05-2013-0054.

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Purpose – The purpose of this paper is to study the problem of fashion flat sketches classification and proposed an integrated approach. It aims to propose a fast, reliable method to handle multi-class fashion flat sketches classification problems and lay the foundation for the garment style query in the next step. Design/methodology/approach – The proposed integrated approach adopts wavelet Fourier descriptor (WFD), linear discriminant analysis (LDA) and extreme learning machine (ELM). First, the discrete wavelet and Fourier transform are adopted to extract the shape features of fashion flat

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Bühlmann, Hans, and Eckhard Platen. "A Discrete Time Benchmark Approach for Insurance and Finance." ASTIN Bulletin 33, no. 02 (2003): 153–72. http://dx.doi.org/10.2143/ast.33.2.503688.

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This paper proposes a consistent approach to discrete time valuation in insurance and finance. This approach uses the growth optimal portfolio as reference unit or benchmark. When used as benchmark, it is shown that all benchmarked price processes are supermartingales. Benchmarked fair price processes are characterized as martingales. No measure transformation is needed for the fair pricing of insurance policies and derivatives. The standard actuarial pricing rule is obtained as a particular case of fair pricing when the contingent claim is independent from the growth optimal portfolio. 1991 M

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Sasireka, M., and A. Senthilkumar. "BEAT CLASSIFICATION USING HYBRID WAVELET TRANSFORM BASED FEATURES AND SUPERVISED LEARNING APPROACH." JOURNAL OF ADVANCES IN CHEMISTRY 13, no. 8 (2017): 6397–405. http://dx.doi.org/10.24297/jac.v13i8.5709.

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This paper describes an automatic heartbeat recognition based on QRS detection, feature extraction and classification. In this paper five different type of ECG beats of MIT BIH arrhythmia database are automatically classified. The proposed method involves QRS complex detection based on the differences and approximation derivation, inversion and threshold method. The computation of combined Discrete Wavelet Transform (DWT) and Dual Tree Complex Wavelet Transform (DTCWT) of hybrid features coefficients are obtained from the QRS segmented beat from ECG signal which are then used as a feature vect

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Altaie, Ahmed Sabri, Mohamed Abderrahim, and Afaneen Anwer Alkhazraji. "Transmission Line Fault Classification Based on the Combination of Scaled Wavelet Scalograms and CNNs Using a One-Side Sensor for Data Collection." Sensors 24, no. 7 (2024): 2124. http://dx.doi.org/10.3390/s24072124.

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This research focuses on leveraging wavelet transform for fault classification within electrical power transmission networks. This study meticulously examines the influence of various parameters, such as fault resistance, fault inception angle, fault location, and other essential components, on the accuracy of fault classification. We endeavor to explore the interplay between classification accuracy and the input data while assessing the efficacy of combining wavelet analysis with deep learning methodologies. The data, sourced from network recorders, including phase currents and voltages, unde

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Chen, Yuan, Shaonan Liang, Zhongyang Wang, et al. "Automatic Classification of Weld Defects From Ultrasonic Signals Using WPEE-KPCA Feature Extraction and an ABC-SVM Approach." Insight - Non-Destructive Testing and Condition Monitoring 65, no. 5 (2023): 262–69. http://dx.doi.org/10.1784/insi.2023.65.5.262.

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The classification of weld defects is very important for the safety assessment of welded structures and feature extraction of ultrasonic defect signals is vital for defect classification. A novel approach based on wavelet packet energy entropy (WPEE) and kernel principal component analysis (KPCA) feature extraction and an artificial bee colony optimisation support vector machine (ABC-SVM) classifier is proposed in this paper. Firstly, the WPEE method is adopted to extract ultrasonic signal features of weld defects and KPCA is used for feature selection. Secondly, an ABC-SVM classifier is emplo

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Patel, Shubha V., and S. L. Sunitha. "Analysis of Muscular Paralysis using EMG Signal with Wavelet Decomposition Approach." Asian Journal of Computer Science and Technology 11, no. 1 (2022): 5–16. http://dx.doi.org/10.51983/ajcst-2022.11.1.3241.

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Paralysis refers to temporary or permanent loss of voluntary muscle movement in a body part or region. The degree of muscle function loss determines the severity of paralysis. The muscle function is represented by electrical activity of the muscles. Electromyography is a technique concerned with the analysis of myoelectric signals. EMG allows the determination of muscular activity. EMG signal analysis is performed using the features extracted in time domain, frequency domain and time frequency domain. In this work, the EMG of Amyotrophic Lateral Sclerosis (ALS), Myopathy, and Normal conditions

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Thawonmas, Ruck, and Keita Iizuka. "Haar Wavelets for Online-Game Player Classification with Dynamic Time Warping." Journal of Advanced Computational Intelligence and Intelligent Informatics 12, no. 2 (2008): 150–55. http://dx.doi.org/10.20965/jaciii.2008.p0150.

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Online game players’ action sequences, while important to understand their behavior, usually contain noise and/or redundancy, making them unnecessarily long. To acquire briefer sequences representative of players’ features, we apply the Haar wavelet transform to action sequences and reconstruct them from selected wavelet coefficients. Results indicate that this approach is effective in classification when thek-nearest neighbor classifier is used to classify players based on dynamic time warping distances between reconstructed sequences.

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Goshvarpour, Atefeh, Ataollah Abbasi, Ateke Goshvarpour, and Sabalan Daneshvar. "A NOVEL SIGNAL-BASED FUSION APPROACH FOR ACCURATE MUSIC EMOTION RECOGNITION." Biomedical Engineering: Applications, Basis and Communications 28, no. 06 (2016): 1650040. http://dx.doi.org/10.4015/s101623721650040x.

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The objective of this study is to propose an accurate emotion recognition methodology. To this end, a novel fusion framework based on wavelet transform (WT), and matching pursuit (MP) algorithm was offered. Electrocardiogram (ECG) and galvanic skin response (GSR) of 11 healthy students were collected while subjects listened to emotional music clips. In both fusion techniques, Coiflet wavelet (Coif5 at level 14) was chosen as a wavelet family and MP dictionary, respectively. After employing the proposed fusion framework, some statistical measures were extracted. To describe emotions, three sche

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Gautam, Mayank Kumar, and V. K. Giri. "Feature Extraction and Classification of ECG Signal Using Neuro-Wavelet Approach." i-manager's Journal on Digital Signal Processing 3, no. 4 (2015): 20–26. http://dx.doi.org/10.26634/jdp.3.4.3708.

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