dc.contributor.author | Ampurire, Ronald | |
dc.contributor.author | Kisakye, Julius | |
dc.contributor.author | Kamusiime, Moreen | |
dc.contributor.author | Guma, Nduhura Nelson | |
dc.date.accessioned | 2023-11-27T12:41:57Z | |
dc.date.available | 2023-11-27T12:41:57Z | |
dc.date.issued | 2023-06-30 | |
dc.identifier.citation | Ampurire, R et al. (2023). Diet track application (Unpublished undergraduate dissertation). Makerere University | en_US |
dc.identifier.uri | http://hdl.handle.net/20.500.12281/17333 | |
dc.description | A project report submitted to the School of Computing and Informatics Technology for the study leading to a project in partial fulfillment of the requirements for the award of the degree of Bachelor of Science in Computer Science of Makerere University | en_US |
dc.description.abstract | The project aims to give Ugandans the ability to track their diets and make more informed decisions about their nutrition, addressing the problem of diet-related diseases in Uganda, which are on the rise due to the people’s transition towards an unhealthy diet. We focused on creating a dataset of Ugandan food images, training a machine learning model to identify foods in meals, computing approximate food nutrients, and designing and developing an application to run and validate the model.
The final application was tested and validated against system requirements. It will help people identify the nutrients in their food, and will also create a database of Ugandan dietary trends that can be useful for health research in the country. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Makerere University | en_US |
dc.subject | Artificial intelligence | en_US |
dc.subject | Software construction | en_US |
dc.subject | Diet tracking | en_US |
dc.title | Diet track application | en_US |
dc.type | Thesis | en_US |