Mobile Applications Usage Prediction for Enhanced User Experience - Ibrahim, Rebwar Bakhtyar; Wakil , Karzan; Kareem, Hemin Muheddin (The Scientific Journal of Cihan University– Sulaimaniya, 2022)
Smartphones are no doubt, one of the most essential accessories as these phones have taken over almost every solution of our everyday operations. Achieving their goals comes with a cost. The variety of Apps and increase in mobile Apps are consistently trying to make smartphones obsolete. The user experience is affected by the mass of Apps and the performance and efficiency are affected by the occupation of memory. Especially in Android OS, the inbuilt low memory killer, which is responsible for freeing memory, is obsolete. For it does not consider user needs information and frequently kills Applications that are going to be launched. In this paper, we propose an Approach of machine learning to develop a system that predicts the upcoming Apps and prelaunch them as well tweak the android User Interface with bringing predict Apps forward for usability and better user experience. We use Scikit-learn; a machine learning library for Python and implement a Decision tree classifier; a machine-learning algorithm to trained models on the previous App usage data provided by google from a smartphone. Later on, through experimentation, the results demonstrate the accuracy of the model and its feasibility to be implemented as a real-world system into an App usage manager for Android operating system.