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Watermelon Ripeness Detection via Extreme Learning Machine with Kernel Principal Component Analysis Based on Acoustic Signals.
- Source :
-
International Journal of Pattern Recognition & Artificial Intelligence . Jul2019, Vol. 33 Issue 8, pN.PAG-N.PAG. 17p. - Publication Year :
- 2019
-
Abstract
- Many investigations have proved that the acoustics method is intuitive and effective for determining watermelon ripeness. The objective of this work is to drive a new robust acoustics classification scheme KPCA-ELM, which is based on the kernel principal component analysis (KPCA) and extreme learning machine (ELM). Acoustic signals are sampled by a microphone from unripe, ripe and over-ripe watermelon samples, which are randomly divided into two sample sets for training and testing. A set of basic signals is first obtained via KPCA of the training sample. Thus, any given signal can be represented as a linear combination of basis signals, and the coefficients of linear combination are extracted as the features of a signal. Corresponding to the unripe, ripe and over-ripe watermelons, a three-class ELM identification model is constructed based on the training data. The scheme presented in this paper is tested with the testing sample and an accuracy of 92% is achieved. To further evaluate the scheme performance, a comparison of ELM and SVM is conducted in terms of the classification results. The results reveal that the proposed scheme can classify faster than SVM, while ELM is better than SVM in accuracy. [ABSTRACT FROM AUTHOR]
- Subjects :
- *PRINCIPAL components analysis
*WATERMELONS
*MACHINE learning
*SIGNAL sampling
Subjects
Details
- Language :
- English
- ISSN :
- 02180014
- Volume :
- 33
- Issue :
- 8
- Database :
- Academic Search Index
- Journal :
- International Journal of Pattern Recognition & Artificial Intelligence
- Publication Type :
- Academic Journal
- Accession number :
- 137147194
- Full Text :
- https://doi.org/10.1142/S0218001419510029