抜粋
In this paper, we performed human moving pattern recognition using communication quality: cellular download throughputs, Received Signal Strength Indicators (RSSIs) and cellular base station IDs. We apply three machine learning algorithms, such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Random Forest (RF) and evaluate recognition accuracy of human moving patterns. Results conclude that the communication quality can recognize moving patterns with high accuracy.
| 元の言語 | English |
|---|---|
| ホスト出版物のタイトル | 2017 IEEE 6th Global Conference on Consumer Electronics, GCCE 2017 |
| 出版者 | Institute of Electrical and Electronics Engineers Inc. |
| ページ | 1-2 |
| ページ数 | 2 |
| 巻 | 2017-January |
| ISBN（電子版） | 9781509040452 |
| DOI | |
| 出版物ステータス | Published - 2017 12 19 |
| イベント | 6th IEEE Global Conference on Consumer Electronics, GCCE 2017 - Nagoya, Japan 継続期間: 2017 10 24 → 2017 10 27 |
Other
| Other | 6th IEEE Global Conference on Consumer Electronics, GCCE 2017 |
|---|---|
| 国 | Japan |
| 市 | Nagoya |
| 期間 | 17/10/24 → 17/10/27 |
ASJC Scopus subject areas
- Media Technology
- Instrumentation
- Electrical and Electronic Engineering
フィンガープリント Accuracy evaluations of human moving pattern using communication quality based on machine learning' の研究トピックを掘り下げます。これらはともに一意のフィンガープリントを構成します。
これを引用
Kawakami, W., Kanai, K., Wei, B., & Katto, J. (2017). Accuracy evaluations of human moving pattern using communication quality based on machine learning. ： 2017 IEEE 6th Global Conference on Consumer Electronics, GCCE 2017 (巻 2017-January, pp. 1-2). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GCCE.2017.8229351