FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4224317442> ?p ?o ?g. }

• W4224317442 endingPage "e958" @default.
• W4224317442 startingPage "e958" @default.
• W4224317442 abstract "For COVID-19, the need for robust, inexpensive, and accessible screening becomes critical. Even though symptoms present differently, cough is still taken as one of the primary symptoms in severe and non-severe infections alike. For mass screening in resource-constrained regions, artificial intelligence (AI)-guided tools have progressively contributed to detect/screen COVID-19 infections using cough sounds. Therefore, in this article, we review state-of-the-art works in both years 2020 and 2021 by considering AI-guided tools to analyze cough sound for COVID-19 screening primarily based on machine learning algorithms. In our study, we used PubMed central repository and Web of Science with key words: (Cough OR Cough Sounds OR Speech) AND (Machine learning OR Deep learning OR Artificial intelligence) AND (COVID-19 OR Coronavirus). For better meta-analysis, we screened for appropriate dataset (size and source), algorithmic factors (both shallow learning and deep learning models) and corresponding performance scores. Further, in order not to miss up-to-date experimental research-based articles, we also included articles outside of PubMed and Web of Science, but pre-print articles were strictly avoided as they are not peer-reviewed." @default.
• W4224317442 created "2022-04-26" @default.
• W4224317442 creator A5038741954 @default.
• W4224317442 creator A5062022577 @default.
• W4224317442 creator A5087790566 @default.
• W4224317442 creator A5091542573 @default.
• W4224317442 date "2022-04-25" @default.
• W4224317442 modified "2023-10-09" @default.
• W4224317442 title "A systematic review on cough sound analysis for Covid-19 diagnosis and screening: is my cough sound COVID-19?" @default.
• W4224317442 cites W1979572435 @default.
• W4224317442 cites W2011678552 @default.
• W4224317442 cites W2134833483 @default.
• W4224317442 cites W2936853908 @default.
• W4224317442 cites W2944946763 @default.
• W4224317442 cites W2979298088 @default.
• W4224317442 cites W3008090866 @default.
• W4224317442 cites W3008827533 @default.
• W4224317442 cites W3010819203 @default.
• W4224317442 cites W3014741004 @default.
• W4224317442 cites W3031621189 @default.
• W4224317442 cites W3031807710 @default.
• W4224317442 cites W3034089691 @default.
• W4224317442 cites W3035378948 @default.
• W4224317442 cites W3035937959 @default.
• W4224317442 cites W3043786499 @default.
• W4224317442 cites W3082664757 @default.
• W4224317442 cites W3082914976 @default.
• W4224317442 cites W3088011590 @default.
• W4224317442 cites W3089168043 @default.
• W4224317442 cites W3091468319 @default.
• W4224317442 cites W3091758192 @default.
• W4224317442 cites W3092128991 @default.
• W4224317442 cites W3094317729 @default.
• W4224317442 cites W3094979312 @default.
• W4224317442 cites W3096215591 @default.
• W4224317442 cites W3097079238 @default.
• W4224317442 cites W3098831650 @default.
• W4224317442 cites W3104903508 @default.
• W4224317442 cites W3105837102 @default.
• W4224317442 cites W3108250559 @default.
• W4224317442 cites W3109783949 @default.
• W4224317442 cites W3111155820 @default.
• W4224317442 cites W3111388919 @default.
• W4224317442 cites W3111852349 @default.
• W4224317442 cites W3112786478 @default.
• W4224317442 cites W3118347226 @default.
• W4224317442 cites W3118723804 @default.
• W4224317442 cites W3127573699 @default.
• W4224317442 cites W3128508004 @default.
• W4224317442 cites W3131917172 @default.
• W4224317442 cites W3132012119 @default.
• W4224317442 cites W3152531055 @default.
• W4224317442 cites W3157670534 @default.
• W4224317442 cites W3158480671 @default.
• W4224317442 cites W3158767600 @default.
• W4224317442 cites W3162335554 @default.
• W4224317442 cites W3164753307 @default.
• W4224317442 cites W3166597180 @default.
• W4224317442 cites W3169741781 @default.
• W4224317442 cites W3175127495 @default.
• W4224317442 cites W3180409537 @default.
• W4224317442 cites W3182764154 @default.
• W4224317442 cites W3183605158 @default.
• W4224317442 cites W3191710696 @default.
• W4224317442 cites W3191726097 @default.
• W4224317442 cites W3205790296 @default.
• W4224317442 cites W3214356413 @default.
• W4224317442 doi "https://doi.org/10.7717/peerj-cs.958" @default.
• W4224317442 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35634112" @default.
• W4224317442 hasPublicationYear "2022" @default.
• W4224317442 type Work @default.
• W4224317442 citedByCount "13" @default.
• W4224317442 countsByYear W42243174422022 @default.
• W4224317442 countsByYear W42243174422023 @default.
• W4224317442 crossrefType "journal-article" @default.
• W4224317442 hasAuthorship W4224317442A5038741954 @default.
• W4224317442 hasAuthorship W4224317442A5062022577 @default.
• W4224317442 hasAuthorship W4224317442A5087790566 @default.
• W4224317442 hasAuthorship W4224317442A5091542573 @default.
• W4224317442 hasBestOaLocation W42243174421 @default.
• W4224317442 hasConcept C108583219 @default.
• W4224317442 hasConcept C119857082 @default.
• W4224317442 hasConcept C121332964 @default.
• W4224317442 hasConcept C142724271 @default.
• W4224317442 hasConcept C154945302 @default.
• W4224317442 hasConcept C177713679 @default.
• W4224317442 hasConcept C189708586 @default.
• W4224317442 hasConcept C24890656 @default.
• W4224317442 hasConcept C2779134260 @default.
• W4224317442 hasConcept C2779473830 @default.
• W4224317442 hasConcept C2984030306 @default.
• W4224317442 hasConcept C3008058167 @default.
• W4224317442 hasConcept C3020774429 @default.
• W4224317442 hasConcept C41008148 @default.
• W4224317442 hasConcept C524204448 @default.
• W4224317442 hasConcept C55493867 @default.
• W4224317442 hasConcept C71924100 @default.
• W4224317442 hasConcept C86803240 @default.

• next