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dc.contributor.advisorHussain, Amir-
dc.contributor.advisorBron, James-
dc.contributor.advisorShinn, Andrew-
dc.contributor.authorAli, Rozniza-
dc.description.abstractThis thesis presents an investigation into Gyrodactylus species recognition, making use of machine learning classification and feature selection techniques, and explores image feature extraction to demonstrate proof of concept for an envisaged rapid, consistent and secure initial identification of pathogens by field workers and non-expert users. The design of the proposed cognitively inspired framework is able to provide confident discrimination recognition from its non-pathogenic congeners, which is sought in order to assist diagnostics during periods of a suspected outbreak. Accurate identification of pathogens is a key to their control in an aquaculture context and the monogenean worm genus Gyrodactylus provides an ideal test-bed for the selected techniques. In the proposed algorithm, the concept of classification using a single model is extended to include more than one model. In classifying multiple species of Gyrodactylus, experiments using 557 specimens of nine different species, two classifiers and three feature sets were performed. To combine these models, an ensemble based majority voting approach has been adopted. Experimental results with a database of Gyrodactylus species show the superior performance of the ensemble system. Comparison with single classification approaches indicates that the proposed framework produces a marked improvement in classification performance. The second contribution of this thesis is the exploration of image processing techniques. Active Shape Model (ASM) and Complex Network methods are applied to images of the attachment hooks of several species of Gyrodactylus to classify each species according to their true species type. ASM is used to provide landmark points to segment the contour of the image, while the Complex Network model is used to extract the information from the contour of an image. The current system aims to confidently classify species, which is notifiable pathogen of Atlantic salmon, to their true class with high degree of accuracy. Finally, some concluding remarks are made along with proposal for future work.en_GB
dc.publisherUniversity of Stirlingen_GB
dc.subjectmachine learningen_GB
dc.subjectfeature selectionen_GB
dc.subjectActive Shape Modelen_GB
dc.subjectensemble classificationen_GB
dc.subjectComplex Networken_GB
dc.subject.lcshMachine learningen_GB
dc.subject.lcshFishes Parasitesen_GB
dc.titleEnsemble classification and signal image processing for genus Gyrodactylus (Monogenea)en_GB
dc.typeThesis or Dissertationen_GB
dc.type.qualificationnameDoctor of Philosophyen_GB
dc.contributor.funderMinistry of Malaysia Education, Malaysiaen_GB
Appears in Collections:Computing Science and Mathematics eTheses

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