Name
Title | ||
|---|---|---|
A Pso-Svm Method For Parameters And Sensor Array Optimization In Wound Infection Detection Based On Electronic Nose |
Abstract | ||
|---|---|---|
In this paper a new method based on the support vector machine (SVM) combined with particle swarm optimization (PSO) is proposed to analyze signals of wound infection detection based on electronic nose (enose). Owing to the strong impact of sensor array optimization and SVM parameters selection on the classification accuracy of SVM, PSO is used to realize a synchronization optimization of sensor array and SVM model parameters. The results show that PSO-SVM method combined with sensor array optimization greatly improves the classification accuracy of mice wound infection compared with radical basis function (RBF) network and genetic algorithms (GA) with/without sensor array optimization. Meanwhile, the proposed sensor array optimization method which weights sensor signals by importance factors also obtain better classification accuracy than that of weighting sensor signals by 0 and 1. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.4304/jcp.7.11.2663-2670 | JOURNAL OF COMPUTERS |
Keywords | Field | DocType |
Electronic nose, Wound infection, Support vector machine, Particle swarm optimization, Sensor array optimization, Parameters optimization | Electronic nose,Particle swarm optimization,Synchronization,Weighting,Pattern recognition,Computer science,Support vector machine,Sensor array,Artificial intelligence,Basis function,Machine learning,Genetic algorithm | Journal |
Volume | Issue | ISSN |
7 | 11 | 1796-203X |
Citations | PageRank | References |
2 | 0.42 | 5 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jia Yan | 1 | 2 | 0.42 |
| Fengchun Tian | 2 | 27 | 10.47 |
| Jingwei Feng | 3 | 2 | 0.76 |
| Jia Pengfei | 4 | 5 | 1.49 |
| Qinghua He | 5 | 19 | 4.19 |
| Yue Shen | 6 | 7 | 1.04 |