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 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. This paper proposes a system that builds a prediction model to predict the remaining lifetime of smartphone battery using linear regression. The system first classifies users based on their usage patterns. A number of well-known data mining classification techniques are employed to perform the classification process including Naïve bias, multilayer perceptron, support vector machine, and Decision tree J48 classifiers. The proposed system consists of two main phases: data preprocessing and data processing. In the data preprocessing phase, a set of operations are applied on the used dataset including parsing, filtration, normalization, statistical processing, and clustering using K-means algorithm. In the data processing phase, the classification and prediction models are constructed and evaluated using the suitable performance metrics. The experimental results have shown the superiority of the J48 classifiers compared to other classifiers regarding the different performance metrics including True Positive Rate (TPR), False Positive Rate (FPR), Precision, Recall, F-Measure, and ROC Area. Also, the obtained experimental results show that the proposed prediction model has a promising performance with 0.0257 MAE and 0.0468 RMSE. |
