FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2100479327> ?p ?o ?g. }

• W2100479327 endingPage "50" @default.
• W2100479327 startingPage "43" @default.
• W2100479327 abstract "This paper is concerned with soft computing techniques for categorizing laryngeal disorders based on information extracted from an image of patient's vocal folds, a voice signal, and questionnaire data.Multiple feature sets are exploited to characterize images and voice signals. To characterize colour, texture, and geometry of biological structures seen in colour images of vocal folds, eight feature sets are used. Twelve feature sets are used to obtain a comprehensive characterization of a voice signal (the sustained phonation of the vowel sound /a/). Answers to 14 questions constitute the questionnaire feature set. A committee of support vector machines is designed for categorizing the image, voice, and query data represented by the multiple feature sets into the healthy, nodular and diffuse classes. Five alternatives to aggregate separate SVMs into a committee are explored. Feature selection and classifier design are combined into the same learning process based on genetic search.Data of all the three modalities were available from 240 patients. Among those, 151 patients belong to the nodular class, 64 to the diffuse class and 25 to the healthy class. When using a single feature set to characterize each modality, the test set data classification accuracy of 75.0%, 72.1%, and 85.0% was obtained for the image, voice and questionnaire data, respectively. The use of multiple feature sets allowed to increase the accuracy to 89.5% and 87.7% for the image and voice data, respectively. The test set data classification accuracy of over 98.0% was obtained from a committee exploiting multiple feature sets from all the three modalities. The highest classification accuracy was achieved when using the SVM-based aggregation with hyper parameters of the SVM determined by genetic search. Bearing in mind the difficulty of the task, the obtained classification accuracy is rather encouraging.Combination of both multiple feature sets characterizing a single modality and the three modalities allowed to substantially improve the classification accuracy if compared to the highest accuracy obtained from a single feature set and a single modality. In spite of the unbalanced data sets used, the error rates obtained for the three classes were rather similar." @default.
• W2100479327 created "2016-06-24" @default.
• W2100479327 creator A5038852557 @default.
• W2100479327 creator A5042022573 @default.
• W2100479327 creator A5060660673 @default.
• W2100479327 creator A5081932212 @default.
• W2100479327 creator A5084225188 @default.
• W2100479327 creator A5087630721 @default.
• W2100479327 date "2010-05-01" @default.
• W2100479327 modified "2023-10-13" @default.
• W2100479327 title "Combining image, voice, and the patient’s questionnaire data to categorize laryngeal disorders" @default.
• W2100479327 cites W1540007258 @default.
• W2100479327 cites W1968535060 @default.
• W2100479327 cites W1976407234 @default.
• W2100479327 cites W1984456879 @default.
• W2100479327 cites W1986080048 @default.
• W2100479327 cites W1992295967 @default.
• W2100479327 cites W2001573500 @default.
• W2100479327 cites W2002080627 @default.
• W2100479327 cites W2003304826 @default.
• W2100479327 cites W2006252838 @default.
• W2100479327 cites W2006924244 @default.
• W2100479327 cites W2009250875 @default.
• W2100479327 cites W2021548319 @default.
• W2100479327 cites W2024566889 @default.
• W2100479327 cites W2026535637 @default.
• W2100479327 cites W2031676154 @default.
• W2100479327 cites W2044465660 @default.
• W2100479327 cites W2046684938 @default.
• W2100479327 cites W2047389778 @default.
• W2100479327 cites W204967190 @default.
• W2100479327 cites W2055178087 @default.
• W2100479327 cites W2060542593 @default.
• W2100479327 cites W2061500280 @default.
• W2100479327 cites W2079959587 @default.
• W2100479327 cites W2080788379 @default.
• W2100479327 cites W2086234013 @default.
• W2100479327 cites W2087676024 @default.
• W2100479327 cites W2100342792 @default.
• W2100479327 cites W2107263735 @default.
• W2100479327 cites W2121771908 @default.
• W2100479327 cites W2121900453 @default.
• W2100479327 cites W2130167399 @default.
• W2100479327 cites W2134180898 @default.
• W2100479327 cites W2138041026 @default.
• W2100479327 cites W2143153057 @default.
• W2100479327 cites W2146086273 @default.
• W2100479327 cites W2148244507 @default.
• W2100479327 cites W2163224754 @default.
• W2100479327 cites W2170509092 @default.
• W2100479327 cites W2171630550 @default.
• W2100479327 cites W2973515423 @default.
• W2100479327 doi "https://doi.org/10.1016/j.artmed.2010.02.002" @default.
• W2100479327 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/20338736" @default.
• W2100479327 hasPublicationYear "2010" @default.
• W2100479327 type Work @default.
• W2100479327 sameAs 2100479327 @default.
• W2100479327 citedByCount "18" @default.
• W2100479327 countsByYear W21004793272013 @default.
• W2100479327 countsByYear W21004793272015 @default.
• W2100479327 countsByYear W21004793272017 @default.
• W2100479327 countsByYear W21004793272019 @default.
• W2100479327 countsByYear W21004793272022 @default.
• W2100479327 countsByYear W21004793272023 @default.
• W2100479327 crossrefType "journal-article" @default.
• W2100479327 hasAuthorship W2100479327A5038852557 @default.
• W2100479327 hasAuthorship W2100479327A5042022573 @default.
• W2100479327 hasAuthorship W2100479327A5060660673 @default.
• W2100479327 hasAuthorship W2100479327A5081932212 @default.
• W2100479327 hasAuthorship W2100479327A5084225188 @default.
• W2100479327 hasAuthorship W2100479327A5087630721 @default.
• W2100479327 hasConcept C12267149 @default.
• W2100479327 hasConcept C138885662 @default.
• W2100479327 hasConcept C148483581 @default.
• W2100479327 hasConcept C153180895 @default.
• W2100479327 hasConcept C154945302 @default.
• W2100479327 hasConcept C173988693 @default.
• W2100479327 hasConcept C177264268 @default.
• W2100479327 hasConcept C199360897 @default.
• W2100479327 hasConcept C2776401178 @default.
• W2100479327 hasConcept C28490314 @default.
• W2100479327 hasConcept C41008148 @default.
• W2100479327 hasConcept C41895202 @default.
• W2100479327 hasConcept C52622490 @default.
• W2100479327 hasConcept C548259974 @default.
• W2100479327 hasConcept C58489278 @default.
• W2100479327 hasConcept C71924100 @default.
• W2100479327 hasConcept C83665646 @default.
• W2100479327 hasConcept C94124525 @default.
• W2100479327 hasConcept C95623464 @default.
• W2100479327 hasConceptScore W2100479327C12267149 @default.
• W2100479327 hasConceptScore W2100479327C138885662 @default.
• W2100479327 hasConceptScore W2100479327C148483581 @default.
• W2100479327 hasConceptScore W2100479327C153180895 @default.
• W2100479327 hasConceptScore W2100479327C154945302 @default.
• W2100479327 hasConceptScore W2100479327C173988693 @default.
• W2100479327 hasConceptScore W2100479327C177264268 @default.
• W2100479327 hasConceptScore W2100479327C199360897 @default.

• next