{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T15:48:49Z","timestamp":1778600929521,"version":"3.51.4"},"reference-count":39,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,5,23]],"date-time":"2023-05-23T00:00:00Z","timestamp":1684800000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Reliable detection of COVID-19 from cough recordings is evaluated using bag-of-words classifiers. The effect of using four distinct feature extraction procedures and four different encoding strategies is evaluated in terms of the Area Under Curve (AUC), accuracy, sensitivity, and F1-score. Additional studies include assessing the effect of both input and output fusion approaches and a comparative analysis against 2D solutions using Convolutional Neural Networks. Extensive experiments conducted on the COUGHVID and COVID-19 Sounds datasets indicate that sparse encoding yields the best performances, showing robustness against various combinations of feature type, encoding strategy, and codebook dimension parameters.<\/jats:p>","DOI":"10.3390\/s23114996","type":"journal-article","created":{"date-parts":[[2023,5,23]],"date-time":"2023-05-23T07:00:02Z","timestamp":1684825202000},"page":"4996","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["COVID-19 Detection from Cough Recordings Using Bag-of-Words Classifiers"],"prefix":"10.3390","volume":"23","author":[{"given":"Irina","family":"Pavel","sequence":"first","affiliation":[{"name":"Faculty of Electronics, Telecommunications and Information Technology, \u201cGheorghe Asachi\u201d Technical University of Iasi, Bd. 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