{"id":"https://openalex.org/W4411072860","doi":"https://doi.org/10.32604/cmc.2025.066373","title":"Enhancing Respiratory Sound Classification Based on Open-Set Semi-Supervised Learning","display_name":"Enhancing Respiratory Sound Classification Based on Open-Set Semi-Supervised Learning","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4411072860","doi":"https://doi.org/10.32604/cmc.2025.066373"},"language":"en","primary_location":{"id":"doi:10.32604/cmc.2025.066373","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.066373","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials & Continua","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.32604/cmc.2025.066373","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5109692699","display_name":"Won-Yang Cho","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Won-Yang Cho","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100712410","display_name":"Sang-Jun Lee","orcid":"https://orcid.org/0009-0009-1580-7732"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sangjun Lee","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5109692699"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.2725652,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"84","issue":"2","first_page":"2847","last_page":"2863"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12419","display_name":"Phonocardiography and Auscultation Techniques","score":0.9944999814033508,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T12419","display_name":"Phonocardiography and Auscultation Techniques","score":0.9944999814033508,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11309","display_name":"Music and Audio Processing","score":0.9804999828338623,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9391999840736389,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/sound","display_name":"Sound (geography)","score":0.5445410013198853},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5359021425247192},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.5277555584907532},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5237579345703125},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4164675176143646},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.357006698846817},{"id":"https://openalex.org/keywords/acoustics","display_name":"Acoustics","score":0.1899634301662445},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.07119560241699219}],"concepts":[{"id":"https://openalex.org/C203718221","wikidata":"https://www.wikidata.org/wiki/Q491713","display_name":"Sound (geography)","level":2,"score":0.5445410013198853},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5359021425247192},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.5277555584907532},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5237579345703125},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4164675176143646},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.357006698846817},{"id":"https://openalex.org/C24890656","wikidata":"https://www.wikidata.org/wiki/Q82811","display_name":"Acoustics","level":1,"score":0.1899634301662445},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.07119560241699219},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.32604/cmc.2025.066373","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.066373","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials & Continua","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.32604/cmc.2025.066373","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.066373","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials & Continua","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.49000000953674316,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1513801117","https://openalex.org/W2619772334","https://openalex.org/W2968272386","https://openalex.org/W2984353870","https://openalex.org/W3022944588","https://openalex.org/W3038482565","https://openalex.org/W3128301555","https://openalex.org/W3128681511","https://openalex.org/W3174451793","https://openalex.org/W3214429548","https://openalex.org/W4288437714","https://openalex.org/W4317802980","https://openalex.org/W4318161904","https://openalex.org/W4376875175","https://openalex.org/W4390837863","https://openalex.org/W4399168388","https://openalex.org/W4403421186","https://openalex.org/W4405905105"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"The":[0,236],"classification":[1,216,232,248],"of":[2,51,147,219,224,238],"respiratory":[3,11,23,123,246],"sounds":[4,24,124],"is":[5,15,119,136,177,190],"crucial":[6],"in":[7,79,88,125,222,250],"diagnosing":[8],"and":[9,59,140,158,164,195,200],"monitoring":[10],"diseases.":[12],"However,":[13,66],"auscultation":[14],"highly":[16],"subjective,":[17],"making":[18],"it":[19],"challenging":[20],"to":[21,33,62,86,121,143,192,204,243],"analyze":[22],"accurately.":[25],"Although":[26],"deep":[27],"learning":[28,47,100,189,208],"has":[29],"been":[30],"increasingly":[31],"applied":[32],"this":[34,64,94,113,181,239],"task,":[35],"most":[36],"existing":[37],"approaches":[38,68],"have":[39,56,69],"primarily":[40],"relied":[41],"on":[42],"supervised":[43,46],"learning.":[44],"Since":[45],"requires":[48],"large":[49],"amounts":[50],"labeled":[52,90,139,194],"data,":[53],"recent":[54],"studies":[55],"explored":[57],"self-supervised":[58],"semi-supervised":[60,99,188,207],"methods":[61],"overcome":[63],"limitation.":[65],"these":[67],"largely":[70],"assumed":[71],"a":[72,115],"closed-set":[73,206],"setting,":[74,101],"where":[75,102],"the":[76,80,89,103,130,133,151,185,210],"classes":[77],"present":[78],"unlabeled":[81,104,141,156,196,228],"data":[82,105,142,157,197,225,229],"are":[83,162,170,241],"considered":[84],"identical":[85],"those":[87],"data.":[91],"In":[92,129,150,184],"contrast,":[93],"study":[95,240],"explores":[96],"an":[97,126,167,214],"open-set":[98,127],"may":[106],"contain":[107],"additional,":[108],"unknown":[109,182,201],"classes.":[110,149,202],"To":[111],"address":[112],"challenge,":[114],"distance-based":[116],"prototype":[117,134,145,176],"network":[118,135],"employed":[120,191],"classify":[122,193],"setting.":[128],"first":[131],"stage,":[132,153,187],"trained":[137],"using":[138],"derive":[144],"representations":[146],"known":[148,159,199],"second":[152],"distances":[154],"between":[155],"class":[160],"prototypes":[161],"computed,":[163],"samples":[165],"exceeding":[166],"adaptive":[168],"threshold":[169],"identified":[171],"as":[172],"unknown.":[173],"A":[174],"new":[175],"then":[178],"calculated":[179],"for":[180],"class.":[183],"final":[186],"into":[198],"Compared":[203],"conventional":[205],"approaches,":[209],"proposed":[211],"method":[212],"achieved":[213],"average":[215],"accuracy":[217],"improvement":[218],"2%\u20135%.":[220],"Additionally,":[221],"cases":[223],"scarcity,":[226],"utilizing":[227],"further":[230],"improved":[231],"performance":[233,249],"by":[234],"6%\u20138%.":[235],"findings":[237],"expected":[242],"significantly":[244],"enhance":[245],"sound":[247],"practical":[251],"clinical":[252],"settings.":[253]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}