This decommissioned ERA site remains active temporarily to support our final migration steps to https://ualberta.scholaris.ca, ERA's new home. All new collections and items, including Spring 2025 theses, are at that site. For assistance, please contact erahelp@ualberta.ca.
Search
Skip to Search Results- 184Machine Learning
- 26Artificial Intelligence
- 23Reinforcement Learning
- 21Deep Learning
- 11Natural Language Processing
- 10Computer Vision
- 165Graduate and Postdoctoral Studies (GPS), Faculty of
- 165Graduate and Postdoctoral Studies (GPS), Faculty of/Theses and Dissertations
- 8Computing Science, Department of
- 7Computing Science, Department of/Technical Reports (Computing Science)
- 2Chemical and Materials Engineering, Department of
- 2Biological Sciences, Department of
- 165Thesis
- 8Report
- 5Article (Published)
- 2Conference/Workshop Poster
- 2Research Material
- 1Article (Draft / Submitted)
-
Fall 2024
This thesis studies a virtual power plant (VPP) that trades the bidirectional charging flexibility of privately owned plug-in electric vehicles (EVs) in a real-time electricity market to maximize its profit. The main contribution of this thesis is the development of scalable and efficient...
-
Enhancing the Architecture of Context-Aware Driver Assistance Systems by Incorporating Insights from Naturalistic Driving Data
DownloadSpring 2019
Driving assistance systems (DASs) have received a great deal of attention in the past decades as an active and effective collision countermeasure. DASs potential benefits will be attained by enhancing the systems’ awareness regarding the dynamic driving context including the change in the driver...
-
Estimating Instantaneous Fuel Consumption of Vehicles By Using Machine Learning And Real-Time On-Board Diagnostics (OBD) Data
Download2022-06-01
Ansari, Amir, Abediasl, Hamidreza, Patel, Parth Rakeshkumar, Hosseini, Vahid, Koch, Charles Robert, Shahbakhti, Mahdi
Estimation of instantaneous fuel consumption of fleet vehicles to identify the causes of high fuel consumption and determine the optimum vehicle type for different applications and driving cycles is essential for the design of an intelligent fleet management system. Developing a practical and...
-
Spring 2012
Recent advances in high-throughput technologies, such as genome-wide SNP analysis and microar- ray gene expression profiling, have led to a multitude of ranked lists, where the features (SNPs, genes) are sorted based on their individual correlation with a phenotype. Multiple reviews have shown...
-
2003
Greiner, Russ, Poulin, B., Lu, Paul, Anvik, J., Lu, Z., Macdonell, Cam, Wishart, David, Eisner, Roman, Szafron, Duane
Technical report TR03-09. Naive Bayes classifiers, a popular tool for predicting the labels of query instances, are typically learned from a training set. However, since many training sets contain noisy data, a classifier user may be reluctant to blindly trust a predicted label. We present a...
-
Exploring biomarkers to predict pig disease resilience traits under a natural disease challenge
DownloadSpring 2024
The intensification and consolidation of modern pig production is exposed to higher risks of endemic or pandemic infections. The complexity of the polymicrobial challenge and increasing concerns on antibiotics resistance make it pivotal to find an efficient way of controlling infections besides...
-
Exploring Machine Learning Techniques for Predicting Open Stope Stability in Underground Mining: Evaluating Accuracy and Applicability
DownloadFall 2024
Underground mining operations are inherently dangerous due to a variety of factors present in a mining environment. Firstly, the confined spaces and limited ventilation, the use of heavy machinery, explosives, and drilling equipment poses significant risks to the safety of workers. Moreover,...
-
Fall 2022
Overfitting is a phenomenon when a machine learning system learns the patterns in training data so well that it starts to inauspiciously affect the model performance on unseen data. In practice, machine learning systems that overfit are not deployable rather systems that generalize well and do...
-
Spring 2023
As we listen to spoken language, the brain performs multiple levels of computation, from understanding individual words to comprehending the arc of a story. Recently, computational models have been developed that also process text on multiple levels. These models, called multi-timescale long...