Convolutional neural networks (CNNs) are highly effective deep learning architectures for remote sensing (RS) image classification. However, the interpretability of CNN architecture remains ...

The architecture of our self-driving car model is structured around a deep convolutional neural network (CNN), renowned for its effectiveness in interpreting visual data. The design incorporates ...

The R-CNN deep learning model R-CNN architecture. The Region-based Convolutional Neural Network (R-CNN) was proposed by AI researchers at the University of California, Berkley, in 2014.

A Convolutional Neural Network (CNN) model for Parkinson's Disease detection is a deep learning architecture designed to analyze medical data, typically in the form of images, to identify and diagnose ...

This work proposes a novel deep learning-based model for prediction of epileptic seizures using multichannel EEG signals. Multichannel images are first constructed by applying short-time Fourier ...

Deep neural networks have a huge advantage: They replace “feature engineering”—a difficult and arduous part of the classic machine learning cycle—with an end-to-end process that ...