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Theses and Dissertations
This collection contains theses and dissertations of graduate students of the University of Alberta. The collection contains a very large number of theses electronically available that were granted from 1947 to 2009, 90% of theses granted from 2009-2014, and 100% of theses granted from April 2014 to the present (as long as the theses are not under temporary embargo by agreement with the Faculty of Graduate and Postdoctoral Studies). IMPORTANT NOTE: To conduct a comprehensive search of all UofA theses granted and in University of Alberta Libraries collections, search the library catalogue at www.library.ualberta.ca - you may search by Author, Title, Keyword, or search by Department.
To retrieve all theses and dissertations associated with a specific department from the library catalogue, choose 'Advanced' and keyword search "university of alberta dept of english" OR "university of alberta department of english" (for example). Past graduates who wish to have their thesis or dissertation added to this collection can contact us at erahelp@ualberta.ca.
Items in this Collection
- 5Image Classification
- 3Deep Learning
- 2Computer Vision
- 2Machine Learning
- 1Artificial Intelligence
- 1Bark Beetle Attack Stage Classification
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Spring 2022
Data augmentation is a strong tool for enhancing the performance of deep learning models using different techniques to increase both the quantity and diversity of training data. Cutout was previously proposed, in the context of image classification, as a simple regularization technique that...
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Advancing Forest Health Monitoring: Harnessing the Power of Deep Learning Computer Vision for Remote Sensing Applications
DownloadFall 2023
Forests provide immense economic, ecological, and societal values, making forest health monitoring (FHM) a crucial task for guiding conservation and management of these essential ecosystems. Drones have seen increased popularity in this domain due to their ability to collect high-resolution,...
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Fall 2021
Malware classification is a critical task in cybersecurity. It offers insights on the threats posed to victim devices from different malware and aids in the designing of precautionary measures. In real world applications, due to the vast amount of malware present in the networks, real-time...
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Fall 2024
The success of deep learning is partly due to the sheer size of modern models. However, such large models strain the capabilities of mobile or resourceconstrained devices. Ergo, reducing the resource demands of AI models is essential before AI can be deployed on such devices. One promising...
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Spring 2016
Image classification is an important problem in machine learning. Deep neural networks, particularly deep convolutional networks, have recently contributed great improvements to end-to-end learning quality for this problem. Such networks significantly reduce the need for human designed features...