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- W3152929442 abstract "Cough detection is a promising technology in detecting every pathology severity in respiratory diseases. The development of an automated cough detection system will serve as the best tracking tool in early diagnosis. An accurate, reliable lab-free, long term, patient-centric, and remote cough severity monitoring will be a game-changer in healthcare infrastructure since many approaches have been developed over the past few decades in telemedicine and remote caring. In this paper, we present a methodology for analyzing the cough audio signals for the occurrence of asthma, bronchitis, and whooping cough using Improved Convolutional Neural Networks (CNN). Herein, the preprocessing using Butterworth High Pass Filter and feature extraction using MFCC is carried out. Furthermore, using the training data sets of cough features, the classification of cough sounds using Improved CNN is accomplished. Using the Thing Speak IoT-cloud site, the corresponding labels are sent to clinician practitioners and caretakers via Wi-Fi communication technologies. Based on the proposed model, a dataset of 20 cough sounds is tested using simulation tools, and the labels are ascertained. Experimental results show that the proposed model outperforms the existing cough detection models in terms of F1-score, sensitivity, specificity, precision, and accuracy." @default.
- W3152929442 created "2021-04-26" @default.
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- W3152929442 date "2021-01-01" @default.
- W3152929442 modified "2023-10-16" @default.
- W3152929442 title "Enhanced exploration of chronic cough using Improved Convolutional Neural Networks and remote monitoring harnessing Internet of Things (IoT)" @default.
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- W3152929442 doi "https://doi.org/10.1016/j.matpr.2021.03.641" @default.
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