Gender Recognition using Smartphone Usage Pattern

Abstract

In today's world, mobile phones can quickly establish long-distance interconnections and perform important tasks. For this reason, the number of smartphone users is increasing rapidly. Smartphones are used not only as an aid, but also to identify and track users based on their behavior. In this survey, we identified the gender of this user based on smartphone usage pattern. The prefer paper work inspect 429 instances of male and female of this specific age range (18-more than 30 years) and collects them in a research that includes the participants and their gender in order to build a classify dataset. In order to improve approximation and experimentation, some different machine learning algorithms are applying. In those algorisms I can choose 5 algorithms which is perform better compare to other algorithms and 3 algorithms including RandomCommittee, IBK and Kstar are compared to other is perform best. Five distinct categories of classification algorithms are compared, classifiers based on trees, Bayes, and function-based classifiers. One of the available algorithms RandomCommittee, improves performance with an accuracy of approximately 83.50 %. Thanks to this survey, we are aware of the characteristics related to smartphones, and the use of smartphones is important for determining the gender of users. Knowing these features will help you think about security, biometrics, and privacy issues.