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An experimental result of estimating an application volume by machine learning techniques
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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4329122/ https://www.ncbi.nlm.nih.gov/pubmed/25713755 http://dx.doi.org/10.1186/s40064-015-0791-3 |
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author | Hasegawa, Tatsuhito Koshino, Makoto Kimura, Haruhiko |
author_facet | Hasegawa, Tatsuhito Koshino, Makoto Kimura, Haruhiko |
author_sort | Hasegawa, Tatsuhito |
collection | PubMed |
description | 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s40064-015-0791-3) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4329122 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-43291222015-02-24 An experimental result of estimating an application volume by machine learning techniques Hasegawa, Tatsuhito Koshino, Makoto Kimura, Haruhiko Springerplus Research 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s40064-015-0791-3) contains supplementary material, which is available to authorized users. Springer International Publishing 2015-02-01 /pmc/articles/PMC4329122/ /pubmed/25713755 http://dx.doi.org/10.1186/s40064-015-0791-3 Text en © Hasegawa et al.; licensee Springer. 2015 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Hasegawa, Tatsuhito Koshino, Makoto Kimura, Haruhiko An experimental result of estimating an application volume by machine learning techniques |
title | An experimental result of estimating an application volume by machine learning techniques |
title_full | An experimental result of estimating an application volume by machine learning techniques |
title_fullStr | An experimental result of estimating an application volume by machine learning techniques |
title_full_unstemmed | An experimental result of estimating an application volume by machine learning techniques |
title_short | An experimental result of estimating an application volume by machine learning techniques |
title_sort | experimental result of estimating an application volume by machine learning techniques |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4329122/ https://www.ncbi.nlm.nih.gov/pubmed/25713755 http://dx.doi.org/10.1186/s40064-015-0791-3 |
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