Machine Learning as a Service (MLaaS) Selection for IoT Environments
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 expla...
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| Format: | Thesis |
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Curtin University
2024
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| Online Access: | http://hdl.handle.net/20.500.11937/96625 |
| _version_ | 1848766182485655552 |
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| author | Patel, Keyaben Mukeshbhai |
| author_facet | Patel, Keyaben Mukeshbhai |
| author_sort | Patel, Keyaben Mukeshbhai |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T11:47:05Z |
| format | Thesis |
| id | curtin-20.500.11937-96625 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:47:05Z |
| publishDate | 2024 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-966252024-12-20T01:28:01Z Machine Learning as a Service (MLaaS) Selection for IoT Environments Patel, Keyaben Mukeshbhai 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. 2024 Thesis http://hdl.handle.net/20.500.11937/96625 Curtin University fulltext |
| spellingShingle | Patel, Keyaben Mukeshbhai Machine Learning as a Service (MLaaS) Selection for IoT Environments |
| title | Machine Learning as a Service (MLaaS) Selection for IoT Environments |
| title_full | Machine Learning as a Service (MLaaS) Selection for IoT Environments |
| title_fullStr | Machine Learning as a Service (MLaaS) Selection for IoT Environments |
| title_full_unstemmed | Machine Learning as a Service (MLaaS) Selection for IoT Environments |
| title_short | Machine Learning as a Service (MLaaS) Selection for IoT Environments |
| title_sort | machine learning as a service (mlaas) selection for iot environments |
| url | http://hdl.handle.net/20.500.11937/96625 |