Abnormality Detection Approach using Deep Learning Models in Smart Home Environments

Author(s):  
A. S. Abdull Sukor ◽  
A. Zakaria ◽  
N. Abdul Rahim ◽  
L. M. Kamarudin ◽  
H. Nishizaki
2021 ◽  
Author(s):  
Albert Rego ◽  
Pedro Luis González Ramírez ◽  
Jose M. Jimenez ◽  
Jaime Lloret

AbstractInternet of Things (IoT) has introduced new applications and environments. Smart Home provides new ways of communication and service consumption. In addition, Artificial Intelligence (AI) and deep learning have improved different services and tasks by automatizing them. In this field, reinforcement learning (RL) provides an unsupervised way to learn from the environment. In this paper, a new intelligent system based on RL and deep learning is proposed for Smart Home environments to guarantee good levels of QoE, focused on multimedia services. This system is aimed to reduce the impact on user experience when the classifying system achieves a low accuracy. The experiments performed show that the deep learning model proposed achieves better accuracy than the KNN algorithm and that the RL system increases the QoE of the user up to 3.8 on a scale of 10.


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