A Comparison Study of Kernel Functions in the Support Vector Machine and Its Application for Termite Detection
AbstractTermites 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
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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.
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(1):5.Chicago/Turabian Style
Achirul Nanda, Muhammad; Boro Seminar, Kudang; Nandika, Dodi; Maddu, Akhiruddin. 2018. "A Comparison Study of Kernel Functions in the Support Vector Machine and Its Application for Termite Detection." Information 9, no. 1: 5.
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