Abstract: Image Retrieval (IR) has become one of the main problems ... The proposed classifier has been achieved using CNN and the data extracted from the CIFAR-10 dataset by the inception model, ...

Label Encoding: The target labels are converted to categorical format for multi-class classification. We designed a Sequential CNN model using the following ... a CNN to classify fashion images. Key ...

Spiking neural networks (SNNs), on the other hand, use different methods of encoding and processing data, making their behavior more ... with convolutional neural networks (CNNs) in solving image ...

In this article, we will implement the multiclass image classification using ... one hot encoding print((x_train.shape,y_train.shape)) print((x_val.shape,y_val.shape)) print((x_test.shape,y_test.shape ...

The demo begins by loading a 5,000-item subset of the 50,000-item CIFAR-10 training data, and a 1,000-item subset of the test data. The demo program creates a convolutional neural network (CNN ...

The multi-granular land use for multi-source remotely sensed image categories is now a principal task in remote sensing data augmentation and data selection. Understanding image representations are ...

The class statistics for fault classification using the testing data set using time-stacked images ... from the PMU monitoring time-series–encoded images with the CNN framework. Different time series ...

Automatic feature extraction from a large set of complex ground motion data and ... While the classification accuracy of the CNN trained with 2,250 ground motion images slightly decreases from 79.5% ...