Name
Abstract | ||
|---|---|---|
Smart environments possess devices that collaborate to help the user non-intrusively. One possible aid smart environment offer is to anticipate user's tasks and perform them on his/her behalf or facilitate the action completion. In this paper, we propose a framework that predicts user's actions by learning his/her behavior when interacting with the smart environment. We prepare the datasets and train a predictor that is responsible to decide whether a target transducer value should be changed or not. Our solution achieves a significant improvement for all target transducers studied and most combinations of parameters yields better results than the base case. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/BRACIS.2015.32 | 2015 Brazilian Conference on Intelligent Systems (BRACIS) |
Keywords | Field | DocType |
Smart Environment,Ubiquitous Computing,Ambient Assisted Living,Intelligent Control,Machine Learning | Intelligent control,Transducer,Smart environment,State prediction,Computer science,Internet of Things,Human–computer interaction,Ubiquitous computing,Embedded system | Conference |
Citations | PageRank | References |
0 | 0.34 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marcelo Bassani de Freitas | 1 | 3 | 0.73 |
| George D. C. Cavalcanti | 2 | 451 | 52.60 |
| Robert Sabourin | 3 | 908 | 61.89 |