A Lung Sound Classification System Based on Data Augmenting Using ELM-Wavelet-AE

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dc.contributor.authorARI, Berna
dc.contributor.authorAlçin, Ömer Faruk
dc.contributor.authorSengur, Abdulkadir
dc.date.accessioned2025-10-24T18:04:17Z
dc.date.available2025-10-24T18:04:17Z
dc.date.issued2022
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractThe method is of great importance in systems that include machine learning and classification steps. As a result, academics are constantly working to improve the process. However, the data pertaining to the methodology's performance is equally as valuable as the methodology's creation. While the data is utilized to show the result of the modeling process, it is critical to consider the proper labeling of the data, the technique of acquisition, and the volume. Obtaining data in certain sectors, particularly medical fields, can be costly and time consuming. Thus, data augmenting via classical and synthetic methods has recently gained popularity. Our study uses synthetic data augmentation since it is newer, more efficient, and produces the desired effect. Our study's goal is to classify a data collection of lung sounds into four groups using data augmenting. Obtaining and standardizing the wavelet scatter transformation of each cycle of lung sounds, splitting the transformed data into test and training, augmenting and classifying the training data. In the augmenting stage, we utilized ELM-AE, then ELM-W-AE, with six wavelet functions (Gaussian, Morlet, Mexican, Shannon, Meyer, Ggw) added. The SVM and EBT classifiers improved performance by 4% and 3% in ELM-W-AE compared to the original structure.
dc.identifier.doi10.55525/tjst.1063039
dc.identifier.endpage88
dc.identifier.issn1308-9099
dc.identifier.issue1
dc.identifier.startpage79
dc.identifier.trdizinid509931
dc.identifier.urihttps://doi.org/10.55525/tjst.1063039
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/509931
dc.identifier.urihttps://hdl.handle.net/20.500.12899/2770
dc.identifier.volume17
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.relation.ispartofTurkish Journal of Science & Technology
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzTR-Dizin_20251023
dc.subjectSolunum Sistemi
dc.subjectWavelet scatter
dc.subjectLung sound
dc.subjectELM-Auto Encoder
dc.subjectdata augmentation
dc.titleA Lung Sound Classification System Based on Data Augmenting Using ELM-Wavelet-AE
dc.typeArticle

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