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

The 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, data pertaining methodology's performance equally as valuable creation. While utilized show result modeling process, it critical consider proper labeling data, technique acquisition, volume. Obtaining certain sectors, particularly medical fields, can be costly time consuming. Thus, augmenting via classical synthetic methods has recently gained popularity. Our study uses augmentation since newer, more efficient, produces desired effect. study's goal classify collection lung sounds into four groups using augmenting. standardizing wavelet scatter transformation each cycle sounds, splitting transformed test training, classifying training data. In stage, we ELM-AE, then ELM-W-AE, with six functions (Gaussian, Morlet, Mexican, Shannon, Meyer, Ggw) added. SVM EBT classifiers improved by 4% 3% ELM-W-AE compared original structure.