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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
- 3Graph Neural Networks
- 1Blockchain
- 1Deep Reinforcement Learning
- 1Derivatization
- 1Electron Ionization
- 1Explainability
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Application of Machine Learning Towards Compound Identification through Gas Chromatography Retention Index (RI) and Electron Ionization Mass Spectrometry (EI-MS) Predictions
DownloadSpring 2024
With over 100 million synthetic chemicals and over 1 million biologically-derived compounds known to humans, chemists face significant challenges trying to identify or characterize them. In addition to this large collection of known compounds, analytical chemists, natural product chemists,...
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Spring 2024
Since the advent of distributed ledger technologies, they have provided diverse opportunities in a wide range of application domains. With the transition towards a more decentralized and dynamic system, the significance of blockchain-enabled smart contracts has grown in prominence. Despite their...
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Fall 2022
The rise of Deep Learning (DL) and its assistance in learning complex feature representations significantly impacted Reinforcement Learning (RL). Deep Reinforcement Learning (DRL) made it possible to apply RL to complex real-world problems and even achieve human-level performance. One of these...