Name
Abstract | ||
|---|---|---|
With more sensors embedded and functions added, mobile phones tend to be more critical to daily life. Researchers have been using the sensor data to recognize human activity these days; meanwhile, the mobile application usage prediction is also gradually brought into the spotlight. In this paper, we leveraged a state-of-the-art technique, which is LSTM, to model the mobile application usage data, also introduced a data fusion technique that eventually accomplished an over 90% of prediction accuracy. To validate the generality of our proposed solution, we applied the model on a public dataset. Our proposed solution treated the mobile application usage as a time series problem which is novel in the related field; it has the advantages of low resource consumption, short training time, as well as a generality. With the growth of users' reliance on mobile phones, mobile application usage prediction will be more useful in the future. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICMLA.2019.00054 | 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) |
Keywords | Field | DocType |
LSTM,HAR,Deep learning,Mobile application usage,Prediction | Resource consumption,Computer science,Sensor fusion,Artificial intelligence,Deep learning,Usage data,Machine learning,Generality | Conference |
ISBN | Citations | PageRank |
978-1-7281-4551-8 | 0 | 0.34 |
References | Authors | |
14 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jingyi Shen | 1 | 1 | 1.02 |
| M. Omair Shafiq | 2 | 139 | 18.59 |