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dc.contributor.authorPatel, Keyaben Mukeshbhai
dc.date.accessioned2024-12-20T01:28:01Z
dc.date.available2024-12-20T01:28:01Z
dc.date.issued2024en_US
dc.identifier.urihttp://hdl.handle.net/20.500.11937/96625
dc.description.abstract

This thesis presents two novel frameworks for selecting Machine Learning as a Service (MLaaS) providers using incomplete Quality of Service (QoS) information and contextual data in IoT environments. The proposed MLaaS Selection Framework (MSF) enhances service selection with bias detection and explainability mechanisms, while the IoT-based framework dynamically maps user contexts to MLaaS services. Together, these frameworks improve service efficiency, accuracy, and responsiveness, enabling informed MLaaS selection based on user preferences and contextual changes.

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dc.publisherCurtin Universityen_US
dc.titleMachine Learning as a Service (MLaaS) Selection for IoT Environmentsen_US
dc.typeThesisen_US
dcterms.educationLevelMPhilen_US
curtin.departmentSchool of Electrical Engineering, Computing and Mathematical Sciencesen_US
curtin.accessStatusOpen accessen_US
curtin.facultyScience and Engineeringen_US
curtin.contributor.orcidPatel, Keyaben Mukeshbhai [0000-0003-3987-9828]en_US


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