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dc.contributor.authorAddison, George Tsekpetse
dc.date.accessioned2020-08-25T18:57:04Z
dc.date.available2020-08-25T18:57:04Z
dc.identifier.urihttp://hdl.handle.net/10464/14884
dc.description.abstractObject classification and processing have become a coordinated piece of modern industrial manufacturing systems, generally utilized in a manual or computerized inspection process. Vagueness is a common issue related to object classification and analysis such as the ambiguity in input data, the overlapping boundaries among the classes or regions, and the indefiniteness in defining or extracting features and relations among them. The main purpose of this thesis is to construct, define, and implement an abstract algebraic framework for L-fuzzy relations to represent the uncertainties involved at every stage of the object classification. This is done to handle the proposed vagueness that is found in the process of object classification such as retaining information as much as possible from the original data for making decisions at the highest level making the ultimate output or result of the associated system with least uncertainty.en_US
dc.language.isoengen_US
dc.publisherBrock Universityen_US
dc.subjectformal concept analysisen_US
dc.subjectfuzzy concept analysisen_US
dc.subjectmachine learningen_US
dc.subjectartificial intelligenceen_US
dc.subjectobject classificationen_US
dc.titleObject Classification using L-Fuzzy Concept Analysisen_US
dc.typeElectronic Thesis or Dissertationen
dc.degree.nameM.Sc. Computer Scienceen_US
dc.degree.levelMastersen_US
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.degree.disciplineFaculty of Mathematics and Scienceen_US
refterms.dateFOA2021-08-14T01:42:08Z


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