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Proceedings ArticleDOI

Non-dominated sorting genetic algorithm — II supported neural network in classifying forest types

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TLDR
Experimental results have indicated that the proposed NN-NSGA-II model is superior to the GA-NN model to a greater extent.
Abstract
Pixel classification in land scape images has been found to be challenging. The problem becomes more challenging in forest images due to the similar spectral features of pixels situated close to each other. Geographically weighted variables have been employed to classify the two different species namely Cryptomeria japonica (Japanese Cedar or Sugi) and Chamaecyparisobtusa (Japanese Cypress or Hinoki) and one mixed forest class. Previous attempts have shown reasonable improvement in this task using Genetic Algorithm supported Neural Network over other traditional approaches. Motivated by this, a NSGA — II supported Neural Network (NN-NSGA — II) classifier is proposed. The proposed model has been compared with GA-NN (ANN trained with Genetic Algorithm with a single objective function) classifiers in terms of confusion matrix based performance metrics such as accuracy, precision, recall and F-Measure. Experimental results have indicated that the proposed NN-NSGA-II model is superior to the GA-NN model to a greater extent.

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Proceedings ArticleDOI

Detection of skin disease using metaheuristic supported artificial neural networks

TL;DR: Experimental results clearly show the superiority of the proposed NN-NSGA-II model with different features, which has been evaluated using various performances measuring metrics such as accuracy, precision, recall and F-measure.
Proceedings ArticleDOI

Image based skin disease detection using hybrid neural network coupled bag-of-features

TL;DR: Experimental results indicated towards the superiority of the proposed bag-of-features enabled NN-NSGA-II model in terms of testing phase confusion matrix based performance measuring metrics.
Journal ArticleDOI

Soil moisture quantity prediction using optimized neural supported model for sustainable agricultural applications

TL;DR: A modified Flower Pollination Algorithm has been employed to train Artificial Neural Network to predict soil moisture quantity and the proposed method is compared with well known PSO supported ANN and Cuckoo Search supported ANN along with MLP-FFN classifier.
Proceedings ArticleDOI

Biomedical image enhancement based on modified Cuckoo Search and morphology

TL;DR: This work describes an method for biomedical image enhancement using modified Cuckoo Search Algorithm with some Morphological Operation and a new technique has been proposed to enhance biomedical images using modified cuckoo search algorithm and morphological operation.
Proceedings ArticleDOI

Gradient approximation in retinal blood vessel segmentation

TL;DR: A gradient-based blood vessel segmentation technique is proposed to assist retinal image analysis and to extract the retinal vessels and it outperformed the corresponding values obtained by the other standard edge detectors, namely Sobel, Prewitt, Canny, and Robert's.
References
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Journal ArticleDOI

An assessment of the effectiveness of decision tree methods for land cover classification

TL;DR: The results indicate that the performance of the univariate DT is acceptably good in comparison with that of other classifiers, except with high-dimensional data, and the use of attribute selection methods does not appear to be justified in terms of accuracy increases.
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