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The state data held by sensors are utilised to provide users services by applying artificial intelligence (AI) and data mining techniques such as fuzzy logic, clustering algorithms, reinforcement learning, and association rules. IoT main objective is to allow users to live comfortably and leisurely in comfort of their own home as well as personal space. IoT enables connection between various real-time objects such as sensors and smartphones. Where an entity describes human and the state describes human attributes such as location, action and other attributes. A state describes the condition of an entity. To predict the human action in a smart home, the state of the human is crucial. A human action prediction in the smart home connected with the internet of things (IoT) is under high research interest recently.
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The basic idea behind prediction is given a sequence of human–device interaction states, how the next human action can be predicted. Such needs will not be achieved without an appropriate device interaction record for every daily action in the home environment. The smart home system is a system that is needed to adjust to the human's lifestyle, predict their future actions, and reduce the human's device interaction. Referring to smart homes, the best definition of smart home technology is the incorporation of technology and services through a home network for a better life. The expressions of smart homes, intelligent homes, as well as home automation, have been utilised for more than a decade to present the concept of smart and network devices in the house. Smart home systems enhance traditional home safety and security techniques by utilising observing and access control.
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A user can control home devices remotely, which enables the user to perform tasks before arriving home or after leaving home. They enhance humans comfort by utilising context awareness and predefined restrictions based on the situation of the home environment. Smart home presents a better quality of life by introducing automated control for devices and services. Nowadays, most smart home systems are used to enhance the quality of life by turning the home environment to a more relaxing and comfortable place to live in. Smart homes are proliferating they are used to provide comfort, high quality of living and security for humans. Their results showed that the proposed prediction system achieved an accuracy of 79% for all weekdays and 81% excluding weekend days. As a result, the best weight distribution selected in this study is (70, 30) for time and location ontology graphs respectively. Both graphs are tested through multiple weight values with the results of FP-growth. For the evaluation of the proposed system, two ontology graphs are introduced with FP-growth to achieve the best results. Their proposed system simulates the human prediction actions by adding common sense data by utilizing the advantages of the ontology graph and the FP-growth to find a better solution in predicting home user actions for automated systems. In this study, the authors propose a new hybrid prediction system based on the frequent pattern (FP)-growth and ontology graphs for home automation systems. The idea of smart homes is not a recent concept it has been in high interest by both academia and industry to make smart homes a more convenient technology for human's comfort. Moreover, the evolution of the internet of things has introduced new insights into smart platforms and devices that leads to the new vision of ‘smart homes’. Nowadays, with the rapid increase of Internet users, the Internet services dominate a primary part of our lifestyle.
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CAAI Transactions on Intelligence Technology.