A machine learning approach to predict the activity of smart home inhabitant
Ambient intelligence; Automation; Decision trees; Domestic appliances; Forecasting; Turing machines; Hardware prototype; Input-output; Machine learning approaches; Noise filters; Prediction systems; Smart homes; Machine learning
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IOS Press BV
2023
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| author | Marufuzzaman M. Tumbraegel T. Rahman L.F. Sidek L.M. |
| author2 | 57205234835 |
| author_facet | 57205234835 Marufuzzaman M. Tumbraegel T. Rahman L.F. Sidek L.M. |
| author_sort | Marufuzzaman M. |
| building | UNITEN Library |
| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Ambient intelligence; Automation; Decision trees; Domestic appliances; Forecasting; Turing machines; Hardware prototype; Input-output; Machine learning approaches; Noise filters; Prediction systems; Smart homes; Machine learning |
| format | Article |
| id | my.uniten.dspace-26513 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2023 |
| publisher | IOS Press BV |
| record_format | dspace |
| spelling | my.uniten.dspace-265132023-05-29T17:11:22Z A machine learning approach to predict the activity of smart home inhabitant Marufuzzaman M. Tumbraegel T. Rahman L.F. Sidek L.M. 57205234835 57226404664 36984229900 35070506500 Ambient intelligence; Automation; Decision trees; Domestic appliances; Forecasting; Turing machines; Hardware prototype; Input-output; Machine learning approaches; Noise filters; Prediction systems; Smart homes; Machine learning A smart home inhabitant performs a unique pattern or sequence of tasks repeatedly. Thus, a machine learning approach will be required to build an intelligent network of home appliances, and the algorithm should respond quickly to execute the decision. This study proposes a decision tree-based machine learning approach for predicting the activities using different appliances such as state, locations and time. A noise filter is employed to remove unwanted data and generate task sequences, and dual state properties of a home appliance are utilized to extract episodes from the sequence. An incremental decision tree approach was taken to reduce execution time. The algorithm was tested using a well-known smart home dataset from MavLab. The experimental results showed that the algorithm successfully extracted 689 predictions and their location at 90% accuracy, and the total execution time was 94 s, which is less than that of existing methods. A hardware prototype was designed using Raspberry Pi 2 B to validate the proposed prediction system. The general-purpose input-output (GPIO) interfaces of Raspberry Pi 2 B were used to communicate with the prototype testbed and showed that the algorithm successfully predicted the next activities. � 2021-IOS Press. All rights reserved. Final 2023-05-29T09:11:22Z 2023-05-29T09:11:22Z 2021 Article 10.3233/AIS-210604 2-s2.0-85111421511 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111421511&doi=10.3233%2fAIS-210604&partnerID=40&md5=c0e0a2a95ed2e23662e92a6e67e880f0 https://irepository.uniten.edu.my/handle/123456789/26513 13 4 271 283 IOS Press BV Scopus |
| spellingShingle | Marufuzzaman M. Tumbraegel T. Rahman L.F. Sidek L.M. A machine learning approach to predict the activity of smart home inhabitant |
| title | A machine learning approach to predict the activity of smart home inhabitant |
| title_full | A machine learning approach to predict the activity of smart home inhabitant |
| title_fullStr | A machine learning approach to predict the activity of smart home inhabitant |
| title_full_unstemmed | A machine learning approach to predict the activity of smart home inhabitant |
| title_short | A machine learning approach to predict the activity of smart home inhabitant |
| title_sort | machine learning approach to predict the activity of smart home inhabitant |
| url_provider | http://dspace.uniten.edu.my/ |