Name
Abstract | ||
|---|---|---|
In this paper we describe the use of machine learning algorithms (Naïve Bayesian, Neural Network, and Support Vector Machine) on data collected from strain gauge sensors to automatically classify vehicles into classes, ranging from small vehicles to combination trucks, along the lines of Federal Highway Administration vehicle classification guide. Knowing the types of vehicles can help reduce operating costs and improve the health monitoring of infrastructure and would help to make transportation safer and personalized; use of such non-image-based data permits user privacy. Our results indicate that the Support Vector Machine technique outperforms the rest with an accuracy of 94.8%. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/PERCOMW.2007.25 | PerCom Workshops |
Keywords | Field | DocType |
combination truck,support vector machine,strain gauge sensors,strain gauge sensor,neural network,health monitoring,automatic vehicle type classification,federal highway administration vehicle,non-image-based data,small vehicle,support vector machine technique,classification guide,machine learning,data privacy,strain gauge,neural nets,naive bayesian,learning artificial intelligence,strain gauges,support vector machines,neural networks,capacitive sensors,bayesian methods,data collection | Truck,Data mining,Naive Bayes classifier,Computer science,Support vector machine,SAFER,Ranging,Strain gauge,Artificial neural network,Information privacy | Conference |
ISBN | Citations | PageRank |
0-7695-2788-4 | 2 | 0.48 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peter Shin | 1 | 3 | 0.85 |
| Hector Jasso | 2 | 28 | 5.03 |
| Sameer Tilak | 3 | 727 | 64.40 |
| Neil Cotofana | 4 | 2 | 0.48 |
| Tony Fountain | 5 | 89 | 11.54 |
| Linjun Yan | 6 | 2 | 0.48 |
| Mike Fraser | 7 | 5 | 2.23 |
| Ahmed Elgamal | 8 | 34 | 3.92 |