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Machine learning classification techniques for non-intrusive load monitoring
(2016-10-01)
Non-intrusive load monitoring is the concept of determining the operational loads using
single-point sensing. The features contained within the electrical load’s signal are used to identify
a unique signature which is ...
Machine learning classifiers for critical cardiac conditions
(2016-04-01)
Cardiac diseases are one of the leading causes of death in Canada. Current methods of diagnosing cardiac conditions require a manual and visual analysis of ECG and heart rate (RR interval) data. In this thesis, novel ...
Predicting Bitcoin: a robust model for predicting Bitcoin price directions based on network influencers
(2016-10-01)
The ability to predict financial markets has tremendous potential to limit exposure
to risk and provide better assurances of annualized gains. In this thesis, a
model for predicting the future daily price of Bitcoin is ...
Time-efficient offloading and execution of machine learning tasks between embedded systems and fog nodes
(2019-12-01)
As embedded systems become more prominent in society, it is important that the technologies that run on them must be used efficiently. One such technology is the Neural Network (NN). NN's, combined with the Internet of ...
Design and development of advanced machine learning algorithms for lithium-ion battery state-of-charge estimation
(2019-11-01)
Batteries have been becoming more and more popular because of their long life and lightweight. Accurate estimation of the SOC help in making plans in an application to conserve and further enhance battery life. State of ...
An anomaly detection model utilizing attributes of low powered networks, IEEE 802.15.4e/TSCH and machine learning methods
(2019-12-01)
The rapid growth in sensors, low-power integrated circuits, and wireless communication standards has enabled a new generation of applications based on ultra-low powered wireless sensor networks. These are employed in many ...
Using machine learning methods to aid scientists in laboratory environments
(2019-12-01)
As machine learning gains popularity as a scientific instrument, we look to create methods to implement it as a laboratory tool for researchers. In the first of two projects, we discuss creating a real-time interference ...
Perpetually playing physics
(2019-08-01)
Here we discuss ideas of reinforcement learning and the importance of various aspects of it. We show how reinforcement learning methods based on genetic algorithms can be used to reproduce thermodynamic cycles without prior ...
Supporting student success with machine learning and visual analytics
(2019-08-01)
Post secondary institutions have a wealth of student data at their disposal. This data has recently been used to explore a problem that has been prevalent in the education domain for decades. Student retention is a complex ...
Proposing an ensemble-based model using data clustering and machine learning algorithms for effective predictions
(2019-08-01)
One of the most important tasks in machine learning is prediction. Data scientists use various regression methods to find the most appropriate and accurate model applicable for each type of datasets. This study proposes a ...