Land-Use Detection Using Residual Convolutional Neural Network

Paul, Sneha

doi:10.1109/icasert.2019.8934607

Cited by 4 publications

References 13 publications

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Brain cancer classification based on multistage ensemble generative adversarial network and convolutional neural network

Melekoodappattu,

Kandambeth Puthiyapurayil,

Vylala

et al. 2023

Cell Biochemistry & Function

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An advanced approach that capitalizes on the synergies between multimodal feature fusion and the dual‐path network is presented in this manuscript. Our proposed methodology harnesses a combination of potent techniques, merging the benefits of nonlinear mapping and expansive perception. The foundation of our methodology lies in leveraging well‐established pretrained models, namely EfficientNet‐B7, ResNet‐152, and a meticulously crafted custom convolutional neural network (CNN), to effectively extract salient features from the data. These models are combined in a two‐stage ensemble approach. We employ maximum variance unfolding (MVU) to select the most relevant attributes from the extracted features. In this study, we propose a hybrid approach that integrates a generative adversarial network and Neural Autoregressive Distribution Estimation (NADE‐K) with a CNN. The resulting two‐stage ensemble hybrid CNN model achieves an accuracy of 99.63%. The implementation of the two‐stage ensemble hybrid CNN with MVU demonstrates significant improvements in brain tumor classification.

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Brain cancer classification based on multistage ensemble generative adversarial network and convolutional neural network

Melekoodappattu,

Kandambeth Puthiyapurayil,

Vylala

et al. 2023

Cell Biochemistry & Function

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DualMLP: a two-stream fusion model for 3D point cloud classification

Paul,

Patterson,

Bouguila

2023

Vis Comput

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Land Use and Land Cover Classification Using CNN, SVM, and Channel Squeeze & Spatial Excitation Block

Dewangkoro

Arymurthy

2021

IOP Conf. Ser.: Earth Environ. Sci.

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One of the materials essential for human life that must manage properly is the land. Land use and land cover (LULC) classification can help us how to manage land. The satellite can record images that can use as the data for LULC classification. This research aims to perform LULC classification using Convolutional Neural Network (CNN) on EuroSAT remote sensing image dataset taken from the Sentinel-2 satellite. CNN has become a well-known method to deal with image feature extraction. We used several CNN for feature extraction, such as VGG19, ResNet50, and InceptionV3. Then, we recalibrated the feature of CNN using Channel Squeeze & Spatial Excitation (sSE) block. We also used Support Vector Machine (SVM) and Twin SVM (TWSVM) as the classifier. VGG19 with sSE block and TWSVM achieved the highest experimental results with 94.57% accuracy, 94.40% precision, 94.40% recall, and 94.39% F1-score.

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Land-Use Detection Using Residual Convolutional Neural Network

Cited by 4 publications

References 13 publications

Brain cancer classification based on multistage ensemble generative adversarial network and convolutional neural network

Brain cancer classification based on multistage ensemble generative adversarial network and convolutional neural network

DualMLP: a two-stream fusion model for 3D point cloud classification

Land Use and Land Cover Classification Using CNN, SVM, and Channel Squeeze & Spatial Excitation Block

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