Modified cuckoo search ased neural networks for forest types classification
Chatterjee S.1; Dey N.2; Sen S.1; Ashour A.S.5; Fong S.J.3; Shi F.4
Source PublicationFrontiers in Artificial Intelligence and Applications
AbstractPixel classification in land scape images is a challenging process especially in forest images due to the similar spectral features of pixels situated close to each other. Previously, meta-heuristic coupled artificial neural network (ANN) models have been used to classify the two-different species, namely Japanese Cedar, Japanese Cypress and one mixed forest class. Previous attempts have shown reasonable improvement in the classification process using genetic algorithm (GA) supported neural network over other traditional approaches. Consequently, in the current work, a modified Cuckoo Search (CS) supported Neural Network (NN-MCS) classifier is proposed. The lévy flight associated with cuckoo search has been modified using McCulloch's method of generating stable random numbers. The proposed approach is compared with GA-NN using single objective function and CS-NN (ANN trained with CS) classifiers in terms of confusion matrix based performance metrics. The results depicted the dominance of the suggested NN-MCS model compared to the CS-NN model to a greater extent.
KeywordArtificial neural network Cuckoo search Forest type McCulloch's method
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Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionUniversity of Macau
Affiliation1.University of Calcutta
2.Techno India College of Technology
3.Universidade de Macau
4.Wenzhou Medical University
5.University of Tanta
Recommended Citation
GB/T 7714
Chatterjee S.,Dey N.,Sen S.,et al. Modified cuckoo search ased neural networks for forest types classification[C],2017:490-498.
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