Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/149321
Title: Energy related activities recognition using smartphones
Authors: Tai, Jie Qin
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Tai, J. Q. (2021). Energy related activities recognition using smartphones. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/149321
Abstract: In recent years, the use of machine learning techniques in applications increased rapidly. More researchers are interested to develop machine techniques to bring comfortability and increase safety through the implementation of smart home and smart office. This report focused on Energy Related Activities Recognition using Smartphones. Machine learning techniques such as Neural Network (NN) and Convolutional Neural Network (CNN) are the main discussion topic of the report. By using different types of parameters such as the Adam and Stochastic Gradient Descent (SGD) optimizer, observations are made on how the accuracy of the model will be affected. Moreover, the learning rate is also one of the factors that can affect accuracy. Subsequently, the CNN was identified as the most suitable model. In summary, the accuracy of the model is high. However, the samples size data of this project was 1300. Future research can increase the sample data.
URI: https://hdl.handle.net/10356/149321
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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