NDLI: Battery discharge rate prediction model for mobile phone using data mining

Content Provider	IEEE Xplore Digital Library
Author	Nusawat, P. Adulkasem, S. Chantrapornchai, C.
Copyright Year	2014
Description	Author affiliation: Dept. of Comput., Silpakorn Univ., Nakorn Pathom, Thailand (Nusawat, P.; Adulkasem, S.) \|\| Dept. of Comput. Eng., Kasetart Univ., Bangkok, Thailand (Chantrapornchai, C.)
Abstract	This paper proposes an framework to create the prediction model for energy consumption of mobile phone battery using data mining, based on three usage patterns of the phone: the standby state, video playing, and web browsing. In this model, the battery discharge rate are analyzed and used for constructing the model. To predict the power used, the perception neural network and support vector machine are employed. The measurement of prediction efficiency is done by the mean absolute error (MAE) and Root mean squared error (RMSE) of the model. According to the developed model, it is found that the Support Vector Machine with the kernel function Linear based on the polynomial equation for predicting the energy consumption of mobile phone battery effective in prediction accuracy.
Sponsorship	IEEE Thailand Sect.
Starting Page	69
Ending Page	74
File Size	1006941
Page Count	6
File Format	PDF
e-ISBN	9781479914241
DOI	10.1109/KST.2014.6775396
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-30
Publisher Place	Thailand
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bluetooth Prediction Model Energy Consumption Data Mining Polynomials Batteries Browsers Mathematical model Mobile Battery Usage Kernel
Content Type	Text
Resource Type	Article

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