Saving Energy on Smartphones by Categorizing Users One major issue of current Smart Phones powersaving strategies is the One Size Fits All philosophy which does not take into consideration the factors that could distinguish different Smartphone users. For example the nature of the workspace of the user (IndoorOutdoor), the age, the gender, the userxe2x80x99s applications categories of interestsxe2x80xa6 etc. A very wellknown example of a current One Size Fits All philosophy is Samsung Ultra Power Saving Mode which forces the user to end up using a black and white screen Smartphone even if the user was a 60 years old book reader or a 17 years old heavy online gamer for the sake of 14 days battery life. This paper proposes a new philosophy that adds another dimension to the current detective and preventive powersaving strategies. The proposed addition is based on classifying Smartphones users into groups based on different factors in order to improve power saving for each group. The relation between the group of users and power consumption is very valuable and can be used by marketing groups of operatorsproducers of mobile phones as well as by designers of the networks for dimensioning of radio interfaces in mobilecellular networks. In this paper, Google Play Classification is used because Google Play store is the official application store for Android applications. It has 85 billion downloads in 2018. Initial empirical experimental results showed that new policies, more specific than the current onesizefitsall that include u0027user categoryu0027 as a new factor, can now be proposed.

User Classification in Electronic Devices Using Machine Learning Methods User classification is a major concern for electronic device providers, because accurate and efficient classification can cut operating cost of company significantly. To deal with this problem, manufacturers try to classify customer into several categories and recognize the characteristic of users, then adopt different promotion strategies to improve sales revenue. This study aims to build models to divide users into several categories, and identify critical and controllable features which dramatically affects classification results. We test proposed models with real data coming from an electronic device producer. Results shows that random forest model performs best. Our main contributions are: 1) we focus on user classification of electronic devices; although many existing studies have discussed similar problem, few of them focus on applications in electronic devices; 2) we consider imbalance sample, and datasets are from real company. This work will be helpful for electronic device producers to improve operation and enhance marketing competitiveness.