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Information 2018, 9(1), 5; https://doi.org/10.3390/info9010005

A Comparison Study of Kernel Functions in the Support Vector Machine and Its Application for Termite Detection

1
Faculty of Agricultural Technology, Bogor Agricultural University, Bogor 16680, West Java, Indonesia
2
Faculty of Forestry, Bogor Agricultural University, Bogor 16680, West Java, Indonesia
3
Faculty of Mathematics and Natural Sciences, Bogor Agricultural University, Bogor 16680, West Java, Indonesia
*
Author to whom correspondence should be addressed.
Received: 10 December 2017 / Revised: 27 December 2017 / Accepted: 28 December 2017 / Published: 2 January 2018
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Abstract

Termites are the most destructive pests and their attacks significantly impact the quality of wooden buildings. Due to their cryptic behavior, it is rarely apparent from visual observation that a termite infestation is active and that wood damage is occurring. Based on the phenomenon of acoustic signals generated by termites when attacking wood, we proposed a practical framework to detect termites nondestructively, i.e., by using the acoustic signals extraction. This method has the pros to maintain the quality of wood products and prevent higher termite attacks. In this work, we inserted 220 subterranean termites into a pine wood for feeding activity and monitored its acoustic signal. The two acoustic features (i.e., energy and entropy) derived from the time domain were used for this study’s analysis. Furthermore, the support vector machine (SVM) algorithm with different kernel functions (i.e., linear, radial basis function, sigmoid and polynomial) were employed to recognize the termites’ acoustic signal. In addition, the area under a receiver operating characteristic curve (AUC) was also adopted to analyze and improve the performance results. Based on the numerical analysis, the SVM with polynomial kernel function achieves the best classification accuracy of 0.9188. View Full-Text
Keywords: acoustic signal; kernel function; support vector machine; termite detection acoustic signal; kernel function; support vector machine; termite detection
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Achirul Nanda, M.; Boro Seminar, K.; Nandika, D.; Maddu, A. A Comparison Study of Kernel Functions in the Support Vector Machine and Its Application for Termite Detection. Information 2018, 9, 5.

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