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Characterizing water-metal interfaces and machine learning potential energy surfaces
(2017-08-01)
In this thesis, we first discuss the fundamentals of ab initio electronic structure theory and density functional theory (DFT). We also discuss statistics related to computing thermodynamic averages of molecular dynamics ...
Predicting mutation score using source code and test suite metrics
(2012-09-01)
Mutation testing has traditionally been used to evaluate the effectiveness of test suites
and provide con dence in the testing process. Mutation testing involves the creation of
many versions of a program each with a single ...
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 ...
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 ...
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 ...
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 ...
Understanding and predicting method-level source code changes using commit history data
(2016-10-01)
Software development and software maintenance require a large amount of source
code changes to be made to a software repositories. Any change to a repository can
introduce new resource needs which will cost more time and ...
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 ...