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A machine learning approach to predict the activity of smart home inhabitant

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dc.citedby8
dc.contributor.authorMarufuzzaman M.en_US
dc.contributor.authorTumbraegel T.en_US
dc.contributor.authorRahman L.F.en_US
dc.contributor.authorSidek L.M.en_US
dc.contributor.authorid57205234835en_US
dc.contributor.authorid57226404664en_US
dc.contributor.authorid36984229900en_US
dc.contributor.authorid35070506500en_US
dc.date.accessioned2023-05-29T09:11:22Z
dc.date.available2023-05-29T09:11:22Z
dc.date.issued2021
dc.descriptionAmbient intelligence; Automation; Decision trees; Domestic appliances; Forecasting; Turing machines; Hardware prototype; Input-output; Machine learning approaches; Noise filters; Prediction systems; Smart homes; Machine learningen_US
dc.description.abstractA 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.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.3233/AIS-210604
dc.identifier.epage283
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85111421511
dc.identifier.spage271
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85111421511&doi=10.3233%2fAIS-210604&partnerID=40&md5=c0e0a2a95ed2e23662e92a6e67e880f0
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26513
dc.identifier.volume13
dc.publisherIOS Press BVen_US
dc.sourceScopus
dc.sourcetitleJournal of Ambient Intelligence and Smart Environments
dc.titleA machine learning approach to predict the activity of smart home inhabitanten_US
dc.typeArticleen_US
dspace.entity.typePublication
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