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.

Deep learning for the automated detection of epileptic seizures has received much attention during recent years. In this work, one dimensional convolutional neural network (1D-CNN) and two dimensional ...

An enhanced 1-D convolutional neural network (1D-CNN)-based fault diagnosis method is proposed. The method begins with the acquisition of fault data, including three-phase voltages on the dc side and ...

To try and save myself a few hundred dollars, I grabbed a pair of the top-rated CRZ Yoga running shorts from Amazon to see how they fared against a Lululemon pair.

It enables the generation of MSD as the first output digit and usage in consecutive computations in digit serial manner leading to different benefits like low latency, high throughput, variable ...

DirecTV cut "Stream" from its name, but its streaming offerings remain. Its basic plan starts at $87 a month, while YouTube TV, Sling, Fubo and Hulu Plus Live TV's plans all cost more now ...

Data fusion techniques combined with a 1-D convolutional neural network (1D-CNN) model were employed to classify the damage evolution process.

Convolutional Neural Networks (CNNs) have been applied effectively to classify high to medium-resolution imageries, especially for local scale land cover mapping, and attained high accuracy by ...

For this reason, this study developed a computationally inexpensive one-dimensional convolutional neural network (1DCNN) algorithm for cyber-attack classification. The proposed study achieved an ...