Name
Abstract | ||
|---|---|---|
This paper presents a methodology to obtain rules of conduction from a set of data captured from sensors placed at a train as well data of actions executed by drivers. These actions result in a history H. The knowledge discovered is put in practice in a driving simulator and the result of the simulated actions generates a history H'. The validation of the discovered knowledge is done in an objective manner, which is calculated as a degree of similarity between the records. This degree of similarity reflects the performance of knowledge discovery process, which in experiments was around 85%. This degree of similarity represents how next were H and H. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/CSCWD.2011.5960082 | Computer Supported Cooperative Work in Design |
Keywords | Field | DocType |
data mining,railway engineering,driving simulator,knowledge discovery,modal rail,Decision Systems,Intelligent Agent,Machine Learning | Data mining,Intelligent agent,Degree of similarity,Driving simulator,Railway engineering,Computer science,Decision system,Artificial intelligence,Knowledge extraction,Modal,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4577-0386-7 | 1 | 0.43 |
References | Authors | |
11 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| André Pinz Borges | 1 | 14 | 7.67 |
| Granatyr, J. | 2 | 14 | 1.67 |
| O. B. Dordal | 3 | 1 | 0.43 |
| Richardson Ribeiro | 4 | 43 | 11.12 |
| Bráulio Coelho Ávila | 5 | 22 | 10.63 |
| Fabrício Enembreck | 6 | 274 | 38.42 |
| Edson Emílio Scalabrin | 7 | 36 | 14.52 |