TY - JOUR
AU1 - Nanda, M A
AU2 - Seminar, K B
AU3 - Nandika, D
AU4 - Maddu, A
AB - Over the last decade, it has been broadly reported that wooden buildings have been massively degraded due to termite attacks. The termite detection system based on the acoustic signal has been proposed to overcome such termite attacks. In this study, we investigate the implementation of the support vector machine (SVM) at the termite detection system. In this work, the pine wood as the medium for termite infestation was divided into two groups, i.e., the wood infested by termites Coptotermes curvignathus (‘infested’) and the normal wood (‘uninfested’). The acoustic signal from each group was analyzed to produce the acoustic features, i.e., energy (E) and entropy (H). Subsequently, the acoustic features were included to construct the SVM Classifier. According to the numerical results, the SVM classifier achieved an accuracy of 93.21 ± 2.58%.
TI - Time domain features in combination with a support vector machine classifier for constructing the termite detection system
JF - IOP Conference Series: Earth and Environmental Science
DO - 10.1088/1755-1315/157/1/012037
DA - 2018-05-01
UR - https://www.deepdyve.com/lp/iop-publishing/time-domain-features-in-combination-with-a-support-vector-machine-u1oPIAAgUb
SP - 012037
VL - 157
IS - 1
DP - DeepDyve
ER -