الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
Nowadays, Smartphones are playing important role in human life as considered the
primary communication tool. Additionally, the users of smartphones can perform
variety tasks such as watching videos, playing games, listening to music, browsing
the internet, etc. However, smartphone are battery based devices; therefore they
have a limited amount of energy. The battery lifetime prediction can help the user
optimizing the smartphone usage in such a way that can prolong the duration of the
battery charge.
In this thesis, we propose a data mining based system that is able to classify
the users of mobile devices based on their usage patterns, into one of three classes,
namely High, Medium, and Normal. Also, the proposed system can estimate the
remaining battery lifetime of a mobile device. The proposed system includes two
main phases: data preprocessing and data processing. In the data preprocessing
phase, a set of operations are applied on the used dataset to make it ready for the
next phase. These operations include parsing, handling missing data (by two
methods: deleting missing values and compensating missing values by average
imputation), normalization, statistical operations, and clustering using k-means
clustering algorithm. In the data processing phase, both classification and
prediction models are using a number of well-known data mining techniques.
IX
Abstract
In the proposed system, four classification models have been used Naïve
Bayes, multilayer perceptron, support vector machine, and J48 algorithms,
respectively. The algorithms are applied on both datasets that are resulted from
handling missing data methods. In addition, four prediction models have been used
Naïve Bayes, multilayer perceptron, support vector regression, and linear
regression. The algorithms are applied on both datasets resulted from handling
missing data methods.