FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2117478064> ?p ?o ?g. }

• W2117478064 abstract "Abstract Background Root canal treatment is a debridement process which disrupts and removes entire microorganisms from the root canal system. Identification of microorganisms may help clinicians decide on treatment alternatives such as using different irrigants, intracanal medicaments and antibiotics. However, the difficulty in cultivation and the complexity in isolation of predominant anaerobic microorganisms make clinicians resort to empirical medical treatments. For this reason, identification of microorganisms is not a routinely used procedure in root canal treatment. In this study, we aimed at classifying 7 different standard microorganism strains which are frequently seen in root canal infections, using odor data collected using an electronic nose instrument. Method Our microorganism odor data set consisted of 5 repeated samples from 7 different classes at 4 concentration levels. For each concentration, 35 samples were classified using 3 different discriminant analysis methods. In order to determine an optimal setting for using electronic-nose in such an application, we have tried 3 different approaches in evaluating sensor responses. Moreover, we have used 3 different sensor baseline values in normalizing sensor responses. Since the number of sensors is relatively large compared to sample size, we have also investigated the influence of two different dimension reduction methods on classification performance. Results We have found that quadratic type dicriminant analysis outperforms other varieties of this method. We have also observed that classification performance decreases as the concentration decreases. Among different baseline values used for pre-processing the sensor responses, the model where the minimum values of sensor readings in the sample were accepted as the baseline yields better classification performance. Corresponding to this optimal choice of baseline value, we have noted that among different sensor response model and feature reduction method combinations, the difference model with standard deviation based dimension reduction or normalized fractional difference model with principal component analysis based dimension reduction results in the best overall performance across different concentrations. Conclusion Our results reveal that the electronic nose technology is a promising and convenient alternative for classifying microorganisms that cause root canal infections. With our comprehensive approach, we have also determined optimal settings to obtain higher classification performance using this technology and discriminant analysis." @default.
• W2117478064 created "2016-06-24" @default.
• W2117478064 creator A5003645803 @default.
• W2117478064 creator A5016591251 @default.
• W2117478064 creator A5026193393 @default.
• W2117478064 creator A5026694770 @default.
• W2117478064 creator A5035166086 @default.
• W2117478064 creator A5041794762 @default.
• W2117478064 creator A5080618671 @default.
• W2117478064 date "2010-11-22" @default.
• W2117478064 modified "2023-10-16" @default.
• W2117478064 title "Classification of root canal microorganisms using electronic-nose and discriminant analysis" @default.
• W2117478064 cites W1983025481 @default.
• W2117478064 cites W1996898660 @default.
• W2117478064 cites W1999033186 @default.
• W2117478064 cites W2014952463 @default.
• W2117478064 cites W2022070868 @default.
• W2117478064 cites W2032287970 @default.
• W2117478064 cites W2033799002 @default.
• W2117478064 cites W2048911146 @default.
• W2117478064 cites W2062030121 @default.
• W2117478064 cites W2074364736 @default.
• W2117478064 cites W2099114511 @default.
• W2117478064 cites W2103246502 @default.
• W2117478064 cites W2128560526 @default.
• W2117478064 cites W2132549764 @default.
• W2117478064 cites W2140759481 @default.
• W2117478064 cites W2146012751 @default.
• W2117478064 cites W2146178402 @default.
• W2117478064 cites W2152054556 @default.
• W2117478064 cites W2152986933 @default.
• W2117478064 doi "https://doi.org/10.1186/1475-925x-9-77" @default.
• W2117478064 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3224911" @default.
• W2117478064 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/21092166" @default.
• W2117478064 hasPublicationYear "2010" @default.
• W2117478064 type Work @default.
• W2117478064 sameAs 2117478064 @default.
• W2117478064 citedByCount "4" @default.
• W2117478064 countsByYear W21174780642014 @default.
• W2117478064 countsByYear W21174780642018 @default.
• W2117478064 countsByYear W21174780642021 @default.
• W2117478064 countsByYear W21174780642022 @default.
• W2117478064 crossrefType "journal-article" @default.
• W2117478064 hasAuthorship W2117478064A5003645803 @default.
• W2117478064 hasAuthorship W2117478064A5016591251 @default.
• W2117478064 hasAuthorship W2117478064A5026193393 @default.
• W2117478064 hasAuthorship W2117478064A5026694770 @default.
• W2117478064 hasAuthorship W2117478064A5035166086 @default.
• W2117478064 hasAuthorship W2117478064A5041794762 @default.
• W2117478064 hasAuthorship W2117478064A5080618671 @default.
• W2117478064 hasBestOaLocation W21174780641 @default.
• W2117478064 hasConcept C153180895 @default.
• W2117478064 hasConcept C154945302 @default.
• W2117478064 hasConcept C169760540 @default.
• W2117478064 hasConcept C175605896 @default.
• W2117478064 hasConcept C199343813 @default.
• W2117478064 hasConcept C23895516 @default.
• W2117478064 hasConcept C2778916471 @default.
• W2117478064 hasConcept C2779747767 @default.
• W2117478064 hasConcept C33923547 @default.
• W2117478064 hasConcept C41008148 @default.
• W2117478064 hasConcept C523546767 @default.
• W2117478064 hasConcept C54355233 @default.
• W2117478064 hasConcept C69738355 @default.
• W2117478064 hasConcept C71924100 @default.
• W2117478064 hasConcept C86803240 @default.
• W2117478064 hasConceptScore W2117478064C153180895 @default.
• W2117478064 hasConceptScore W2117478064C154945302 @default.
• W2117478064 hasConceptScore W2117478064C169760540 @default.
• W2117478064 hasConceptScore W2117478064C175605896 @default.
• W2117478064 hasConceptScore W2117478064C199343813 @default.
• W2117478064 hasConceptScore W2117478064C23895516 @default.
• W2117478064 hasConceptScore W2117478064C2778916471 @default.
• W2117478064 hasConceptScore W2117478064C2779747767 @default.
• W2117478064 hasConceptScore W2117478064C33923547 @default.
• W2117478064 hasConceptScore W2117478064C41008148 @default.
• W2117478064 hasConceptScore W2117478064C523546767 @default.
• W2117478064 hasConceptScore W2117478064C54355233 @default.
• W2117478064 hasConceptScore W2117478064C69738355 @default.
• W2117478064 hasConceptScore W2117478064C71924100 @default.
• W2117478064 hasConceptScore W2117478064C86803240 @default.
• W2117478064 hasIssue "1" @default.
• W2117478064 hasLocation W21174780641 @default.
• W2117478064 hasLocation W21174780642 @default.
• W2117478064 hasLocation W21174780643 @default.
• W2117478064 hasLocation W21174780644 @default.
• W2117478064 hasLocation W21174780645 @default.
• W2117478064 hasOpenAccess W2117478064 @default.
• W2117478064 hasPrimaryLocation W21174780641 @default.
• W2117478064 hasRelatedWork W2067546129 @default.
• W2117478064 hasRelatedWork W2094946501 @default.
• W2117478064 hasRelatedWork W2146076056 @default.
• W2117478064 hasRelatedWork W2353567328 @default.
• W2117478064 hasRelatedWork W2380927352 @default.
• W2117478064 hasRelatedWork W2530636277 @default.
• W2117478064 hasRelatedWork W2824192316 @default.
• W2117478064 hasRelatedWork W2889088970 @default.
• W2117478064 hasRelatedWork W3022680479 @default.
• W2117478064 hasRelatedWork W3157560838 @default.
• W2117478064 hasVolume "9" @default.

• next