TY  - CONF
ID  - SisLab2584
UR  - https://link.springer.com/chapter/10.1007/978-981-10-4154-9_4
A1  - Lu, Dang Nhac
A1  - Ngo, Thi Thu Trang
A1  - Nguyen, Duc Nhan
A1  - Nguyen, Thi Hau
A1  - Nguyen, Ha Nam
Y1  - 2017/03/18/
N2  - In this paper, we proposed an efficient method with flexible framework for vehicle status awareness using smartphone sensors, so called Mobile Online Vehicle Status Awareness System (MOVSAS). The system deployed while users to put their smartphones in any position and at any direction. In our proposed framework, principal component analysis (PCA) algorithm is used to selected suitable features from set of combining features on time-base, power-based and frequency-based domain, which extracted from accelerometer sensor data. The classification model using Random Forest (RF), Naïve Bayes (NB), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM) algorithms to deploy for awareness issues of vehicle status. The refining model is proposed using Artificial Neural Network (ANN) algorithm aim to improved accuracy prediction vehicle status results before. Training data sets, which are collected and the dynamic feedback also helping improved accuracy of system. A number of experiments are shown that the high accuracy of MOVSAS with vehicle kinds as bicycle, motorbike and car.
TI  - A Novel Mobile Online Vehicle Status Awareness Method Using Smartphone Sensors
AV  - none
T2  - 2017 International Conference on Information Science and Applications
ER  -