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A study of combination of autoencoders and boosted Big-Bang crunch theory architectures for Land-Use classification using remotely sensed imagery

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Abstract The research introduced a new method for land-use classification by merging deep convolutional neural networks with a modified variant of a metaheuristic optimization technique. The methodology involved utilizing the VGG-19 model for feature extraction. dimensionality reduction. and a stacked autoencoder optimized with a boosted version of the Big Bang Crunch Theory. https://www.ealisboa.com/special-pick-CRB-Concept-Style-Spinning-Guides-Model-YG-best-find/
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