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Analysis and Prediction Algorithms on the State of User's Action Using the Hidden Markov Model in a Ubiquitous Home Network System  

Shin, Dong-Kyoo (세종대학교 컴퓨터공학과)
Shin, Dong-Il (세종대학교 컴퓨터공학과)
Hwang, Gu-Youn (세종대학교 대학원)
Choi, Jin-Wook (세종대학교 대학원)
Publication Information
Journal of Internet Computing and Services / v.12, no.2, 2011 , pp. 9-17 More about this Journal
Abstract
This paper proposes an algorithm that predicts the state of user's next actions, exploiting the HMM (Hidden Markov Model) on user profile data stored in the ubiquitous home network. The HMM, recognizes patterns of sequential data, adequately represents the temporal property implicated in the data, and is a typical model that can infer information from the sequential data. The proposed algorithm uses the number of the user's action performed, the location and duration of the actions saved by "Activity Recognition System" as training data. An objective formulation for the user's interest in his action is proposed by giving weight on his action, and change on the state of his next action is predicted by obtaining the change on the weight according to the flow of time using the HMM. The proposed algorithm, helps constructing realistic ubiquitous home networks.
Keywords
Ubiquitous Home Network; Hidden Markov Model; Prediction of User's Activity; Data Mining;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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