{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T10:37:54Z","timestamp":1778755074722,"version":"3.51.4"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031533105","type":"print"},{"value":"9783031533112","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-53311-2_26","type":"book-chapter","created":{"date-parts":[[2024,1,27]],"date-time":"2024-01-27T21:37:36Z","timestamp":1706391456000},"page":"355-368","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["C3-PO: A Convolutional Neural Network for\u00a0COVID Onset Prediction from\u00a0Cough Sounds"],"prefix":"10.1007","author":[{"given":"Xiangyu","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0225-6969","authenticated-orcid":false,"given":"Md Ayshik Rahman","family":"Khan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2565-5321","authenticated-orcid":false,"given":"Md Rakibul","family":"Hasan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tom","family":"Gedeon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1892-831X","authenticated-orcid":false,"given":"Md Zakir","family":"Hossain","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,1,28]]},"reference":[{"issue":"3","key":"26_CR1","doi-asserted-by":"publisher","first-page":"1220","DOI":"10.1109\/TSC.2021.3061402","volume":"15","author":"J Andreu-Perez","year":"2021","unstructured":"Andreu-Perez, J., et al.: A generic deep learning based cough analysis system from clinically validated samples for point-of-need Covid-19 test and severity levels. IEEE Trans. Serv. Comput. 15(3), 1220\u20131232 (2021). https:\/\/doi.org\/10.1109\/TSC.2021.3061402","journal-title":"IEEE Trans. Serv. Comput."},{"key":"26_CR2","doi-asserted-by":"publisher","unstructured":"Ashby, A.E., Meister, J.A., Soldar, G., Nguyen, K.A.: A novel cough audio segmentation framework for covid-19 detection. In: Proceedings of the Symposium on Open Data and Knowledge for a Post-Pandemic Era ODAK22, UK, pp. 1\u20138 (2022). https:\/\/doi.org\/10.14236\/ewic\/ODAK22.1","DOI":"10.14236\/ewic\/ODAK22.1"},{"issue":"1","key":"26_CR3","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1038\/s41597-023-02266-0","volume":"10","author":"D Bhattacharya","year":"2023","unstructured":"Bhattacharya, D., et al.: Coswara: a respiratory sounds and symptoms dataset for remote screening of SARS-COV-2 infection. Sci. Data 10(1), 397 (2023)","journal-title":"Sci. Data"},{"issue":"5","key":"26_CR4","doi-asserted-by":"publisher","first-page":"1684","DOI":"10.3390\/app10051684","volume":"10","author":"H Duan","year":"2020","unstructured":"Duan, H., Wei, Y., Liu, P., Yin, H.: A novel ensemble framework based on k-means and resampling for imbalanced data. Appl. Sci. 10(5), 1684 (2020)","journal-title":"Appl. Sci."},{"issue":"7","key":"26_CR5","doi-asserted-by":"publisher","first-page":"1527","DOI":"10.3390\/diagnostics12071527","volume":"12","author":"A Furtado","year":"2022","unstructured":"Furtado, A., da Purifica\u00e7\u00e3o, C.A.C., Badar\u00f3, R., Nascimento, E.G.S.: A light deep learning algorithm for CT diagnosis of COVID-19 Pneumonia. Diagnostics 12(7), 1527 (2022). https:\/\/doi.org\/10.3390\/diagnostics12071527","journal-title":"Diagnostics"},{"key":"26_CR6","unstructured":"Geertsen, A., Chmelyuk, V.: Dataset of recordings of induced cough (Dec 2020). https:\/\/github.com\/covid19-cough\/dataset"},{"issue":"12","key":"26_CR7","doi-asserted-by":"publisher","first-page":"817","DOI":"10.1049\/tje2.12082","volume":"2021","author":"MR Hasan","year":"2021","unstructured":"Hasan, M.R., Hasan, M.M., Hossain, M.Z.: How many mel-frequency cepstral coefficients to be utilized in speech recognition? a study with the Bengali language. J. Eng. 2021(12), 817\u2013827 (2021). https:\/\/doi.org\/10.1049\/tje2.12082","journal-title":"J. Eng."},{"key":"26_CR8","doi-asserted-by":"publisher","unstructured":"Hossain, M.Z., Uddin, M.B., Yang, Y., Ahmed, K.A.: CovidEnvelope: an automated fast approach to diagnose Covid-19 from cough signals. In: 2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), pp. 1\u20136. IEEE, Brisbane, Australia (Dec 2021). https:\/\/doi.org\/10.1109\/CSDE53843.2021.9718501","DOI":"10.1109\/CSDE53843.2021.9718501"},{"key":"26_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2020.100378","volume":"20","author":"A Imran","year":"2020","unstructured":"Imran, A., et al.: AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app. Inform. Med. Unlocked 20, 100378 (2020). https:\/\/doi.org\/10.1016\/j.imu.2020.100378","journal-title":"Inform. Med. Unlocked"},{"issue":"3","key":"26_CR10","doi-asserted-by":"publisher","first-page":"867","DOI":"10.1016\/j.bbe.2021.05.013","volume":"41","author":"SH Kassania","year":"2021","unstructured":"Kassania, S.H., Kassanib, P.H., Wesolowskic, M.J., Schneidera, K.A., Detersa, R.: Automatic detection of coronavirus disease (Covid-19) in x-ray and CT images: a machine learning based approach. Biocybernet. Biomed. Eng. 41(3), 867\u2013879 (2021)","journal-title":"Biocybernet. Biomed. Eng."},{"issue":"18","key":"26_CR11","doi-asserted-by":"publisher","first-page":"3329","DOI":"10.1140\/epjs\/s11734-022-00432-w","volume":"231","author":"L Kranthi Kumar","year":"2022","unstructured":"Kranthi Kumar, L., Alphonse, P.: Covid-19 disease diagnosis with light-weight CNN using modified MFCC and enhanced GFCC from human respiratory sounds. Europ. Phys. J. Special Topics 231(18), 3329\u20133346 (2022)","journal-title":"Europ. Phys. J. Special Topics"},{"key":"26_CR12","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1109\/OJEMB.2020.3026928","volume":"1","author":"J Laguarta","year":"2020","unstructured":"Laguarta, J., Hueto, F., Subirana, B.: COVID-19 artificial intelligence diagnosis using only cough recordings. IEEE Open J. Eng. Med. Biol. 1, 275\u2013281 (2020). https:\/\/doi.org\/10.1109\/OJEMB.2020.3026928","journal-title":"IEEE Open J. Eng. Med. Biol."},{"issue":"7","key":"26_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-022-05127-6","volume":"24","author":"X Liu","year":"2023","unstructured":"Liu, X., Hasan, M.R., Ahmed, K.A., Hossain, M.Z.: Machine learning to analyse omic-data for COVID-19 diagnosis and prognosis. BMC Bioinform. 24(7), 1\u201320 (2023). https:\/\/doi.org\/10.1186\/s12859-022-05127-6","journal-title":"BMC Bioinform."},{"key":"26_CR14","doi-asserted-by":"crossref","unstructured":"Mahanta, S.K., Kaushik, D., Jain, S., Van Truong, H., Guha, K.: COVID-19 diagnosis from cough acoustics using convnets and data augmentation (May 2022). arXiv:2110.06123","DOI":"10.1109\/ICACFCT53978.2021.9837350"},{"issue":"1","key":"26_CR15","doi-asserted-by":"publisher","first-page":"15404","DOI":"10.1038\/s41598-021-95042-2","volume":"11","author":"EA Mohammed","year":"2021","unstructured":"Mohammed, E.A., Keyhani, M., Sanati-Nezhad, A., Hejazi, S.H., Far, B.H.: An ensemble learning approach to digital corona virus preliminary screening from cough sounds. Sci. Rep. 11(1), 15404 (2021)","journal-title":"Sci. Rep."},{"key":"26_CR16","doi-asserted-by":"crossref","unstructured":"Muguli, A., et al.: Dicova challenge: dataset, task, and baseline system for covid-19 diagnosis using acoustics. arXiv preprint arXiv:2103.09148 (2021). 10.48550\/arXiv. 2103.09148","DOI":"10.21437\/Interspeech.2021-74"},{"issue":"1","key":"26_CR17","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1038\/s41597-021-00937-4","volume":"8","author":"L Orlandic","year":"2021","unstructured":"Orlandic, L., Teijeiro, T., Atienza, D.: The coughvid crowdsourcing dataset, a corpus for the study of large-scale cough analysis algorithms. Sci. Data 8(1), 156 (2021). https:\/\/doi.org\/10.1038\/s41597-021-00937-4","journal-title":"Sci. Data"},{"issue":"9","key":"26_CR18","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0162128","volume":"11","author":"RXA Pramono","year":"2016","unstructured":"Pramono, R.X.A., Imtiaz, S.A., Rodriguez-Villegas, E.: A cough-based algorithm for automatic diagnosis of pertussis. PLoS ONE 11(9), e0162128 (2016). https:\/\/doi.org\/10.1371\/journal.pone.0162128","journal-title":"PLoS ONE"},{"key":"26_CR19","unstructured":"Richards, R.: Evidence on the accuracy of the number of reported covid-19 infections and deaths in lower-middle income countries. K4D Helpdesk Report 856 (2020). https:\/\/opendocs.ids.ac.uk\/opendocs\/handle\/20.500.12413\/15576"},{"key":"26_CR20","unstructured":"Schuller, B.W., Coppock, H., Gaskell, A.: Detecting covid-19 from breathing and coughing sounds using deep neural networks. arXiv preprint arXiv:2012.14553 (2020)"},{"key":"26_CR21","doi-asserted-by":"publisher","unstructured":"Sharma, N., et al.: Coswara - a database of breathing, cough, and voice sounds for Covid-19 diagnosis. In: Interspeech 2020, pp. 4811\u20134815 (Oct 2020). https:\/\/doi.org\/10.21437\/Interspeech.2020\u20132768","DOI":"10.21437\/Interspeech."},{"key":"26_CR22","doi-asserted-by":"publisher","unstructured":"Sharma, N.K., Chetupalli, S.R., Bhattacharya, D., Dutta, D., Mote, P., Ganapathy, S.: The second dicova challenge: dataset and performance analysis for diagnosis of covid-19 using acoustics. In: ICASSP 2022\u20132022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 556\u2013560 (2022). https:\/\/doi.org\/10.1109\/ICASSP43922.2022.9747188","DOI":"10.1109\/ICASSP43922.2022.9747188"},{"issue":"1","key":"26_CR23","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1080\/03637751.2017.1342043","volume":"85","author":"KB Sheehan","year":"2018","unstructured":"Sheehan, K.B.: Crowdsourcing research: data collection with amazon\u2019s mechanical turk. Commun. Monogr. 85(1), 140\u2013156 (2018). https:\/\/doi.org\/10.1080\/03637751.2017.1342043","journal-title":"Commun. Monogr."},{"issue":"5","key":"26_CR24","doi-asserted-by":"publisher","first-page":"700","DOI":"10.1016\/j.jtho.2020.02.010","volume":"15","author":"S Tian","year":"2020","unstructured":"Tian, S., Hu, W., Niu, L., Liu, H., Xu, H., Xiao, S.Y.: Pulmonary pathology of early-phase 2019 novel coronavirus (covid-19) pneumonia in two patients with Lung Cancer. J. Thorac. Oncol. 15(5), 700\u2013704 (2020). https:\/\/doi.org\/10.1016\/j.jtho.2020.02.010","journal-title":"J. Thorac. Oncol."},{"key":"26_CR25","unstructured":"World Health Organization: WHO coronavirus (COVID-19) dashboard (2023). https:\/\/covid19.who.int\/"}],"container-title":["Lecture Notes in Computer Science","MultiMedia Modeling"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-53311-2_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T15:29:21Z","timestamp":1710257361000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-53311-2_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031533105","9783031533112"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-53311-2_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"28 January 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MMM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Multimedia Modeling","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Amsterdam","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"The Netherlands","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 January 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 February 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mmm2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"ConfTool Pro","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"297","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"112","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"38% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.2","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}