Abstract
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%.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IOP Conference Series: Earth and Environmental Science
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
