In the vast field of signal processing, machine learning is rapidly expanding its domain into all realms. As a constituent of this expansion, this thesis presents contributive work on advancements in machine learning algorithms by building on the shoulder of giants. The first chapter of this thesis contains enhancements to a CNN (convolutional neural network) for better classification of heartbeat arrhythmia. The network goes through a two stage development, the first being augmentations to the network and the second being the implementation of dropout. Chapter 2 involves the combination of CNN and LSTM (long short term memory) networks for the task of short-term energy use data regression. Exploiting the benefits of two of the most powerful neural networks, a unique, novel neural network is created to effectually predict future energy use. The final section concludes this work with directions for future works.
| Identifer | oai:union.ndltd.org:unt.edu/info:ark/67531/metadc1986472 |
| Date | 08 1900 |
| Creators | Kim, Hae Jin |
| Contributors | Bailey, Colleen, Guturu, Parthasarathy, Namuduri, Kamesh |
| Publisher | University of North Texas |
| Source Sets | University of North Texas |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
| Format | Text |
| Rights | Public, Kim, Hae Jin, Copyright, Copyright is held by the author, unless otherwise noted. All rights Reserved. |
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