FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3205559994> ?p ?o ?g. }

• W3205559994 endingPage "6750" @default.
• W3205559994 startingPage "6750" @default.
• W3205559994 abstract "The recent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as coronavirus disease (COVID)-19, has appeared as a global pandemic with a high mortality rate. The main complication of COVID-19 is rapid respirational deterioration, which may cause life-threatening pneumonia conditions. Global healthcare systems are currently facing a scarcity of resources to assist critical patients simultaneously. Indeed, non-critical patients are mostly advised to self-isolate or quarantine themselves at home. However, there are limited healthcare services available during self-isolation at home. According to research, nearly 20-30% of COVID patients require hospitalization, while almost 5-12% of patients may require intensive care due to severe health conditions. This pandemic requires global healthcare systems that are intelligent, secure, and reliable. Tremendous efforts have been made already to develop non-contact sensing technologies for the diagnosis of COVID-19. The most significant early indication of COVID-19 is rapid and abnormal breathing. In this research work, RF-based technology is used to collect real-time breathing abnormalities data. Subsequently, based on this data, a large dataset of simulated breathing abnormalities is generated using the curve fitting technique for developing a machine learning (ML) classification model. The advantages of generating simulated breathing abnormalities data are two-fold; it will help counter the daunting and time-consuming task of real-time data collection and improve the ML model accuracy. Several ML algorithms are exploited to classify eight breathing abnormalities: eupnea, bradypnea, tachypnea, Biot, sighing, Kussmaul, Cheyne-Stokes, and central sleep apnea (CSA). The performance of ML algorithms is evaluated based on accuracy, prediction speed, and training time for real-time breathing data and simulated breathing data. The results show that the proposed platform for real-time data classifies breathing patterns with a maximum accuracy of 97.5%, whereas by introducing simulated breathing data, the accuracy increases up to 99.3%. This work has a notable medical impact, as the introduced method mitigates the challenge of data collection to build a realistic model of a large dataset during the pandemic." @default.
• W3205559994 created "2021-10-25" @default.
• W3205559994 creator A5000112659 @default.
• W3205559994 creator A5005868223 @default.
• W3205559994 creator A5009826262 @default.
• W3205559994 creator A5022863238 @default.
• W3205559994 creator A5023084872 @default.
• W3205559994 creator A5049530484 @default.
• W3205559994 creator A5074586222 @default.
• W3205559994 creator A5076516617 @default.
• W3205559994 date "2021-10-12" @default.
• W3205559994 modified "2023-10-03" @default.
• W3205559994 title "Improving Machine Learning Classification Accuracy for Breathing Abnormalities by Enhancing Dataset" @default.
• W3205559994 cites W2016095118 @default.
• W3205559994 cites W2113638573 @default.
• W3205559994 cites W2133920396 @default.
• W3205559994 cites W2183092875 @default.
• W3205559994 cites W2794514299 @default.
• W3205559994 cites W2918630919 @default.
• W3205559994 cites W2955745546 @default.
• W3205559994 cites W2968705375 @default.
• W3205559994 cites W2981892815 @default.
• W3205559994 cites W3001118548 @default.
• W3205559994 cites W3007940623 @default.
• W3205559994 cites W3008947817 @default.
• W3205559994 cites W3009885589 @default.
• W3205559994 cites W3021429961 @default.
• W3205559994 cites W3036966675 @default.
• W3205559994 cites W3091434185 @default.
• W3205559994 cites W3092128991 @default.
• W3205559994 cites W3158202104 @default.
• W3205559994 cites W3160004655 @default.
• W3205559994 cites W3168094202 @default.
• W3205559994 doi "https://doi.org/10.3390/s21206750" @default.
• W3205559994 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8538545" @default.
• W3205559994 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34695963" @default.
• W3205559994 hasPublicationYear "2021" @default.
• W3205559994 type Work @default.
• W3205559994 sameAs 3205559994 @default.
• W3205559994 citedByCount "11" @default.
• W3205559994 countsByYear W32055599942022 @default.
• W3205559994 countsByYear W32055599942023 @default.
• W3205559994 crossrefType "journal-article" @default.
• W3205559994 hasAuthorship W3205559994A5000112659 @default.
• W3205559994 hasAuthorship W3205559994A5005868223 @default.
• W3205559994 hasAuthorship W3205559994A5009826262 @default.
• W3205559994 hasAuthorship W3205559994A5022863238 @default.
• W3205559994 hasAuthorship W3205559994A5023084872 @default.
• W3205559994 hasAuthorship W3205559994A5049530484 @default.
• W3205559994 hasAuthorship W3205559994A5074586222 @default.
• W3205559994 hasAuthorship W3205559994A5076516617 @default.
• W3205559994 hasBestOaLocation W32055599941 @default.
• W3205559994 hasConcept C126322002 @default.
• W3205559994 hasConcept C154945302 @default.
• W3205559994 hasConcept C160735492 @default.
• W3205559994 hasConcept C162324750 @default.
• W3205559994 hasConcept C177713679 @default.
• W3205559994 hasConcept C2779134260 @default.
• W3205559994 hasConcept C3008058167 @default.
• W3205559994 hasConcept C39300077 @default.
• W3205559994 hasConcept C41008148 @default.
• W3205559994 hasConcept C42219234 @default.
• W3205559994 hasConcept C50522688 @default.
• W3205559994 hasConcept C524204448 @default.
• W3205559994 hasConcept C71924100 @default.
• W3205559994 hasConcept C89623803 @default.
• W3205559994 hasConceptScore W3205559994C126322002 @default.
• W3205559994 hasConceptScore W3205559994C154945302 @default.
• W3205559994 hasConceptScore W3205559994C160735492 @default.
• W3205559994 hasConceptScore W3205559994C162324750 @default.
• W3205559994 hasConceptScore W3205559994C177713679 @default.
• W3205559994 hasConceptScore W3205559994C2779134260 @default.
• W3205559994 hasConceptScore W3205559994C3008058167 @default.
• W3205559994 hasConceptScore W3205559994C39300077 @default.
• W3205559994 hasConceptScore W3205559994C41008148 @default.
• W3205559994 hasConceptScore W3205559994C42219234 @default.
• W3205559994 hasConceptScore W3205559994C50522688 @default.
• W3205559994 hasConceptScore W3205559994C524204448 @default.
• W3205559994 hasConceptScore W3205559994C71924100 @default.
• W3205559994 hasConceptScore W3205559994C89623803 @default.
• W3205559994 hasFunder F4320334627 @default.
• W3205559994 hasIssue "20" @default.
• W3205559994 hasLocation W32055599941 @default.
• W3205559994 hasLocation W32055599942 @default.
• W3205559994 hasLocation W32055599943 @default.
• W3205559994 hasLocation W32055599944 @default.
• W3205559994 hasLocation W32055599945 @default.
• W3205559994 hasLocation W32055599946 @default.
• W3205559994 hasOpenAccess W3205559994 @default.
• W3205559994 hasPrimaryLocation W32055599941 @default.
• W3205559994 hasRelatedWork W2748952813 @default.
• W3205559994 hasRelatedWork W2899084033 @default.
• W3205559994 hasRelatedWork W3033635008 @default.
• W3205559994 hasRelatedWork W3048554897 @default.
• W3205559994 hasRelatedWork W3106370311 @default.
• W3205559994 hasRelatedWork W3119540162 @default.
• W3205559994 hasRelatedWork W3152606407 @default.
• W3205559994 hasRelatedWork W3152916563 @default.

• next