{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T13:06:25Z","timestamp":1753880785782,"version":"3.41.2"},"reference-count":16,"publisher":"World Scientific Pub Co Pte Ltd","issue":"04","funder":[{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2023,3,30]]},"abstract":"<jats:p> Due to the increasing severity of the COVID-19 pandemic, timely screening and diagnosis of infections are essential. Since cough is a common symptom of COVID-19, an AI-assisted cough classification scheme is designed in this paper to diagnose COVID-19 infection. To reduce the labeling efforts by human experts, a semi-supervised learning with voting scheme using a triple-classifier model is proposed for the COVID-19 cough classification. This work aims to improve the accuracy of the classification. Initially, the data pre-processing scheme is executed by performing data cleaning, resampling, and data enhancement so as to improve the audio quality before training. The pre-training scheme is then performed by using a few numbers of COVID-19 cough data with labeling. Then we modify a well-known self-supervised learning model, SimCLR, to a semi-supervised learning-based SimCLR-like model, which uses three different loss functions to fine-tune three training models for cough classification. Finally, a voting scheme is performed based on the classification results of the three cough classifiers so as to enhance the accuracy of the cough classification for COVID-19. The experiment results illustrate that the proposed scheme can achieve 85% accuracy, which outperforms the existing semi-supervised learning-based classification schemes. <\/jats:p>","DOI":"10.1142\/s0218001423520043","type":"journal-article","created":{"date-parts":[[2023,2,10]],"date-time":"2023-02-10T01:39:39Z","timestamp":1675993179000},"source":"Crossref","is-referenced-by-count":0,"title":["A Semi-Supervised Learning Using Tri-Classifier Model with Voting for COVID-19 Cough Classification"],"prefix":"10.1142","volume":"37","author":[{"given":"Yuh-Shyan","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Taipei University, Sanxia District, New Taipei 23741, Taiwan, Republic of China"}]},{"given":"Kuang-Hung","family":"Cheng","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Taipei University, Sanxia District, New Taipei 23741, Taiwan, Republic of China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0386-2231","authenticated-orcid":false,"given":"Chih-Shun","family":"Hsu","sequence":"additional","affiliation":[{"name":"Department of Information Management, Shih Hsin University, Taipei 116, Taiwan, Republic of China"}]},{"given":"Tzu-Hung","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Taipei University, Sanxia District, New Taipei 23741, Taiwan, Republic of China"}]}],"member":"219","published-online":{"date-parts":[[2023,3,25]]},"reference":[{"key":"S0218001423520043BIB001","first-page":"1","volume-title":"Proc. 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