Now showing items 1-6 of 6
Proposing an ensemble-based model using data clustering and machine learning algorithms for effective predictions
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 ...
Design and development of advanced machine learning algorithms for lithium-ion battery state-of-charge estimation
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 ...
Predicting Bitcoin: a robust model for predicting Bitcoin price directions based on network influencers
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
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 ...
Machine learning classification techniques for non-intrusive load monitoring
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
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 ...