Deep learning architectures have brought about new heights in computer vision, with the most common approach being the Convolutional Neural Network (CNN). Through CNN, tasks previously deemed ...

Methods: This study aimed to develop and evaluate a lightweight two-dimensional convolutional neural network (2D CNN) for the automated classification of MPS maturation stages using axial CBCT slices.

This study utilized 1D and 2D convolutional neural networks (CNN) to assess OA of the knee using vibroarthrographic (VAG) signals recorded by an inertial measurement unit sensor. VAG signals were ...

Convolutional neural network (CNN) was widely applied to the data-driven-based fault diagnosis. However, it often needs to artificially transform the signal into a 2-D image with the help of ...

In order to handle the above problems, this article proposes a domain-adversarial wide-kernel convolutional neural network (DAWDCNN) for noisy domain adaptive diesel engine misfire diagnosis. First, ...

Remote sensing is of great importance for analyzing and studying various phenomena occurrence and development on Earth. Today is possible to extract features specific to various fields of application ...