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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
- 3Granular Computing
- 1Construction Industry
- 1Factor Surveys
- 1Feature Selection
- 1Fuzzy Inference Systems
- 1Fuzzy Modeling
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Developing and Optimizing Context-Specific and Universal Construction Labour Productivity Models
DownloadFall 2015
Construction labour productivity (CLP) significantly influences the profitability of construction companies; however, CLP exhibits the highest variability among project resources and is a major source of project risk. The construction industry is thus constantly searching for ways to improve...
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Fall 2012
In this thesis, we introduce a concept of feature reduction, in which the reduction is guided by a criterion of structure retention. In other words, the features forming the reduced space are selected in such a way that the original structure present in the highly dimensional space is retained in...
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Fall 2014
In this thesis, we propose a design process to construct granular neural networks with granular inputs and numeric network parameters. The proposed granular network is formed on the basis of a numeric neural network whose inputs are augmented using probabilistic information granules. The design...