Relevant bibliographies by topics / Wavelet approach JEL classification

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Academic literature on the topic 'Wavelet approach JEL classification'

Author: Grafiati
Published: 5 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Wavelet approach JEL classification"

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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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Dissertations / Theses on the topic "Wavelet approach JEL classification"

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"A wavelet packet approach to transient signal classification." Massachusetts Institute of Technology, Laboratory for Information and Decision Systems], 1993. http://hdl.handle.net/1721.1/3329.

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Rachel E. Learned, Alan S. Willsky.<br>Caption title.<br>Includes bibliographical references (p. 34).<br>Supported by the Charles Stark Draper Laboratory under a research fellowship. Supported by the Draper Laboratory IR&D Program. DL-H-467133 Supported by the Air Force Office of Scientific Research. AFSOR-92-J-0002 Supported by the Army Research Office. DAAL03-92-G-0115
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Huang, Jian-Jie, and 黃建傑. "Classification of Epileptic EEG Signals using Wavelet Analysis and Nonlinear Approach." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/56787880772239470526.

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碩士<br>國立中正大學<br>電機工程所<br>98<br>This thesis presents a method, which first exploits the discrete wavelet transform (DWT) to decompose EEG signals into the five frequency sub-bands such as , , , and . Non-linear features, including time delay (TL), embedding dimensions (ED), correlation dimension (CD), largest Lyapunov exponent (LLE), approximate entropy (ApEn), Hurst exponent (HE), fractal dimension (FD), and wavelet entropy (WE), were extracted from the original EEG signals and the five frequency sub-bands. Three feature selection methods are employed in the study, and the one that shows the
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Gharbali, Ali Abdollahi. "Sleep Stage Classification: A Deep Learning Approach." Doctoral thesis, 2018. http://hdl.handle.net/10362/56821.

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Sleep occupies significant part of human life. The diagnoses of sleep related disorders are of great importance. To record specific physical and electrical activities of the brain and body, a multi-parameter test, called polysomnography (PSG), is normally used. The visual process of sleep stage classification is time consuming, subjective and costly. To improve the accuracy and efficiency of the sleep stage classification, automatic classification algorithms were developed. In this research work, we focused on pre-processing (filtering boundaries and de-noising algorithms) and classification
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Chiu, Chi-Ya, and 邱琪雅. "The classification and reconstruction of 12-lead electrocardiogram from single-lead ECG based on wavelet approach." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/8fx8qe.

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碩士<br>國立中央大學<br>生物醫學工程研究所<br>106<br>The 12-lead electrocardiogram (ECG), which provides special projection of electrical activity of the heart in different orientation, is one of the standard procedures for clinical diagnosis of heart disease, especially the deadly myocardial infarction. However, the standard 12-lead ECG device is bulky and with lots of wires and therefore cannot be widely used outside the hospital and clinics. So far, there have been several miniaturized products for single-lead ECG recorders, allowing patients to measure ECG signals anytime. Once the measured signals seemed
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Baek, Seung Hyun. "Kernel-Based Data Mining Approach with Variable Selection for Nonlinear High-Dimensional Data." 2010. http://trace.tennessee.edu/utk_graddiss/676.

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In statistical data mining research, datasets often have nonlinearity and high-dimensionality. It has become difficult to analyze such datasets in a comprehensive manner using traditional statistical methodologies. Kernel-based data mining is one of the most effective statistical methodologies to investigate a variety of problems in areas including pattern recognition, machine learning, bioinformatics, chemometrics, and statistics. In particular, statistically-sophisticated procedures that emphasize the reliability of results and computational efficiency are required for the analysis of high-
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Book chapters on the topic "Wavelet approach JEL classification"

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Cohen, Achraf, Chaimaa Messaoudi, and Hassan Badir. "A New Wavelet-Based Approach for Mass Spectrometry Data Classification." In New Frontiers of Biostatistics and Bioinformatics. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99389-8_8.

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Birajadar, Parmeshwar, Meet Haria, and Vikram Gadre. "Scattering Wavelet Network-Based Iris Classification: An Approach to De-duplication." In ICT with Intelligent Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3571-8_64.

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Mukherjee, Gunjan, Arpitam Chatterjee, and Bipan Tudu. "A Computer Vision Approach Towards Maturity Stage Classification of Tomatoes Using Second Order Wavelet Features." In Proceedings of International Conference on Data Science and Applications. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5120-5_10.

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Karaca, Yeliz. "Wavelet-based Multifractal Spectrum Estimation in Hepatitis Virus Classification Models by Using Artificial Neural Network Approach." In Global Virology III: Virology in the 21st Century. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29022-1_4.

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Taran, Sachin, Ravi, Smith K. Khare, Varun Bajaj, and G. R. Sinha. "Classification of Alertness and Drowsiness States Using the Complex Wavelet Transform-Based Approach for EEG Records." In Analysis of Medical Modalities for Improved Diagnosis in Modern Healthcare. CRC Press, 2021. http://dx.doi.org/10.1201/9781003146810-1.

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Simon, Philomina, and V. Uma. "A Machine Learning Approach for Steel Surface Textural Defect Classification Based on Wavelet Scattering Features and PCA." In Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021). Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96302-6_28.

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Canbilen, Ayşe Elif, and Murat Ceylan. "A Novel Approach for the Classification of Liver MR Images Using Complex Orthogonal Ripplet-II and Wavelet-Based Transforms." In Lecture Notes in Computational Vision and Biomechanics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65981-7_2.

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Jareena Begum, D., and S. P. Chokkalingam. "MRI-Based Brain Tumour Detection and Classification Using Random Forest Algorithm." In Smart Innovation, Systems and Technologies. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-8355-7_7.

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Abstract A brain tumour develops when cells in the brain multiply abnormally and out of control. The possibility of fatality makes this growth dangerous. The brain regulates everything from memory to vision to emotions. Recognizing these tumours is a challenging and complex task due to their location, size, and shape. There have been a number of successful attempts to enhance detection. However, the current level of precision is insufficient. This study details a method with a high likelihood of success in identifying brain cancers. The approach is implemented in Python LAB by use of an image
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Panda, Mrutyunjaya, Aboul Ella Hassanien, and Ajith Abraham. "Hybrid Data Mining Approach for Image Segmentation Based Classification." In Biometrics. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0983-7.ch064.

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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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Zahra, Noore, Hussah N. Al-Eisa, Kahkashan Tabassum, Sahar A. EI-Rahman, and Mona Jamjoom. "Wavelet Approach for the Classification of Autism Spectrum Disorder." In Computational Science and Its Applications. Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9780429288739-13.

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Conference papers on the topic "Wavelet approach JEL classification"

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Lalawat, Rajveer Singh, Varun Bajaj, and Prabin Kumar Padhy. "Sleep Stage Classification Using EEG Signals: A Empirical Wavelet Transform-Based Decomposition Approach." In 2024 18th International Conference on Control, Automation, Robotics and Vision (ICARCV). IEEE, 2024. https://doi.org/10.1109/icarcv63323.2024.10821607.

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Debnath, Rajesh, and Arvind Kumar Jain. "Classification of Complex Power Quality Disturbances Using Wavelet Scalogram and Computer Vision Approach." In 2024 23rd National Power Systems Conference (NPSC). IEEE, 2024. https://doi.org/10.1109/npsc61626.2024.10986777.

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Chettouh, Rokia, Abdelfatih Bengana, and Ismail Boukli Hacene. "A Diagnostic Classification Framework for Alzheimer's Disease: An Integrated Approach with ESIHE, SVM, and Wavelet Features." In 2025 International Symposium on iNnovative Informatics of Biskra (ISNIB). IEEE, 2025. https://doi.org/10.1109/isnib64820.2025.10983022.

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El Gohary, Youssef H., Ahmed H. Osman, and Mostafa Shaaban. "Intelligent Fault Classification and Localization in Islanded DC Microgrids: A Discrete Wavelet Transform and Neural Network Approach." In 2024 7th International Conference on Electric Power and Energy Conversion Systems (EPECS). IEEE, 2024. https://doi.org/10.1109/epecs62845.2024.10805504.

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Lertwanitrot, Praikanok, Atthapol Ngaopitakkul, and Santipont Ananwattanaporn. "Comparative Study for Discrete Wavelet Transform Between Single and Double Detection Approach to Fault Classification on Transmission Line." In 2024 16th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI). IEEE, 2024. http://dx.doi.org/10.1109/iiai-aai63651.2024.00095.

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Perpenti, Mattia, Federico Mento, Sajjad Afrakhteh, Giuliana Barcellona, Tiziano Perrone, and Libertario Demi. "A Novel Empirical Wavelet Transform Approach for Classification of Radiofrequency Lung Ultrasound Signals Applied to Diagnosis of Lung Diseases." In 2024 IEEE Ultrasonics, Ferroelectrics, and Frequency Control Joint Symposium (UFFC-JS). IEEE, 2024. https://doi.org/10.1109/uffc-js60046.2024.10793884.

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Gariso, Ruben, Jo�o P. L. Coutinho, Tiago J. Rato, and Marco S. Reis. "Linear and non-linear convolutional approaches and XAI for spectral data: classification of waste lubricant oils." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.103935.

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Waste lubricant oil (WLO) is a hazardous residual that requires proper management, being WLO regeneration the preferred approach. However, regeneration is only viable if the WLO does not coagulate in the equipment. Otherwise, the process needs to be shut down for cleaning and maintenance. To mitigate this risk, a laboratory test is currently used to assess the WLO coagulation potential before it enters the process. This laboratory test is, however, time-consuming, presents several safety risks, and is subjective. To expedite decision-making, process analytics technology (PAT) and machine learn
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Alimohammadi, Hamzeh, Shengnan Chen, Hassan Karimi, and Hamid Rahmanifard. "Wavelet Modelling Approach for Reservoir Model Classification." In SPE Europec. Society of Petroleum Engineers, 2020. http://dx.doi.org/10.2118/200555-ms.

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Appati, Justice Kwame, Beatrice Armah, Ebenezer Owusu, and Michael Agbo Tettey Soli. "Fundus Image Classification: A Wavelet Feature Descriptor Approach." In 2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC). IEEE, 2022. http://dx.doi.org/10.1109/assic55218.2022.10088415.

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Torabi, M., S. Razavian, R. Vaziri, and B. Vosoughi-Vahdat. "A Wavelet-packet-based approach for breast cancer classification." In 2011 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2011. http://dx.doi.org/10.1109/iembs.2011.6091263.

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Reports on the topic "Wavelet approach JEL classification"

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Learned, Rachel E., and Alan S. Willsky. A Wavelet Packet Approach to Transient Signal Classification. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada459970.

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Chepeliev, Maksym. The GTAP 10A Data Base with Agricultural Production Targeting Based on the Food and Agricultural Organization (FAO) Data. GTAP Research Memoranda, 2020. http://dx.doi.org/10.21642/gtap.rm35.

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This document describes a new source of inputs, based on FAO data, that allows us to estimate agricultural output targets on 133 regions of the GTAP 10A Data Base. This approach allows to overcome several limitations present under the current agricultural production targeting (APT) processing. First, a significant expansion in the regional coverage is achieved, as the number of regions undergoing APT more than doubles. Second, the detailed commodity classification of the FAO dataset allows for a more accurate mapping to the GTAP Data Base sectors. Third, a better commodity coverage in the FAO
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