title: An experimental result of estimating an application volume by machine learning techniques.
abstract: In this study, we improved the usability of smartphones by automating a user's operations. We developed an intelligent system using machine learning techniques that periodically detects a user's context on a smartphone. We selected the Android operating system because it has the largest market share and highest flexibility of its development environment. In this paper, we describe an application that automatically adjusts application volume. Adjusting the volume can be easily forgotten because users need to push the volume buttons to alter the volume depending on the given situation. Therefore, we developed an application that automatically adjusts the volume based on learned user settings. Application volume can be set differently from ringtone volume on Android devices, and these volume settings are associated with each specific application including games. Our application records a user's location, the volume setting, the foreground application name and other such attributes as learning data, thereby estimating whether the volume should be adjusted using machine learning techniques via Weka.
authors.affiliation: Graduate School of Natural Science and Technology, Kanazawa University, Kakumamachi, Kanazawa Japan.
authors.forename: Tatsuhito
authors.initials: T
authors.lastname: Hasegawa
authors.affiliation: Department of Electronics and Information Engineering, Ishikawa National College of Technology, Kitachujo, Tsubatamachi Japan.
authors.forename: Makoto
authors.initials: M
authors.lastname: Koshino
authors.affiliation: Graduate School of Natural Science and Technology, Kanazawa University, Kakumamachi, Kanazawa Japan.
authors.forename: Haruhiko
authors.initials: H
authors.lastname: Kimura
keywords: Context aware; Lifelog; Machine learning; Smartphone