Earticle

초록

영어

Rapid and timely monitoring of traumatic inflammation is conducive to doctors’ diagnosis and treatment. It has been proved that electronic nose (E-nose) is an effective way to predict the bacterial class of wound infection by smelling the odor produced by the metabolites, and the classification accuracy of E-nose is influenced strongly by the classifier. To improve the performance of E-nose in predicting the bacterial class of wound infection, an enhanced SVM with a novel weighted Gaussian RBF kernel is proposed in this paper, and the way of setting parameters of this enhanced SVM is also given. Experimental results prove that the classification accuracy of SVM with the novel weighted Gaussian RBF kernel is 95.24%, which is better than other considered classifiers (PLS-DA, RBFNN, SVM with single Gaussian RBF kernel and SVM with traditional weighted Gaussian RBF kernel). All results make it clear that the enhanced SVM proposed in this paper is an ideal classifier when E-nose is used to detect the bacterial class of wound infection.

목차

Abstract
 1. Introduction
 2. Materials and Experiments
  2.1. Sampling Preparation
  2.2. E-nose for Bacteria Detection and Measurement
 3. SVM with a Novel Weighted Kernel Function
  3.1. Overview of SVM
  3.2. A Novel Weighted Kernel Function
 4. Results and Discussion
 5. Conclusions
 References

키워드

저자

• Pengfei Jia [ College of Electronic and Information Engineering, Southwest University, Chongqing,China ]
• Jia Yan [ College of Electronic and Information Engineering, Southwest University, Chongqing,China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605