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dc.contributor.authorStiles, Eric Todd
dc.date.accessioned2014-03-03T23:16:10Z
dc.date.available2014-03-03T23:16:10Z
dc.date.issued2005-05
dc.identifier.otherstiles_eric_t_200505_ms
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/stiles_eric_t_200505_ms
dc.identifier.urihttp://hdl.handle.net/10724/22519
dc.description.abstractExpert System Tools for veterinary students can improve treatments the animals receive and help to train new students. The usability of such a tool is important for acceptance by the target community and for the productivity of users. This thesis involves the development of a visualization-based tool, BoneDesktop, to help with the evaluation of external skeletal fixation proposals. BoneDesktop is also surveyed by Veterinarian Residents, Interns, and Students for evaluation to further enhance its usability in meeting the needs of its target community. BoneDesktop offers its review based on machine learning techniques. In this thesis we compare several variations of two common machine learning techniques, K-Nearest Neighbor and Decision Trees, in their application of veterinary domain data. BoneDesktop then serves as a platform for building future, advanced user friendly features.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjecthuman-computer interaction
dc.subjectexternal bone fixation
dc.subjectdecision trees.
dc.titleBoneDesktop
dc.title.alternativea visualization based tool supporting the evaluation of external skeletal fixation proposals
dc.typeThesis
dc.description.degreeMS
dc.description.departmentComputer Science
dc.description.majorComputer Science
dc.description.advisorKhaled Rasheed
dc.description.committeeKhaled Rasheed
dc.description.committeeEileen Kraemer
dc.description.committeeDennis Aron


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