Abstract: Presently, the exponential growth of unstructured data on the web and social networks has made it increasingly ... This paper presents a novel Deep Learning (DL)-based text summarization ...

Deep learning models go above and beyond traditional machine learning and can process data and recognize patterns much more efficiently.

Abstract: Deep learning-based high-resolution ... and factors such as narrow edges in farmland lead to lower extraction accuracy and inaccurate boundaries in high-resolution remote sensing. Therefore, ...

Objective The purpose of our work was to determine if a convolutional neural network (CNN) was able to identify and segment the intracranial aneurysm (IA) sac in a DSA and extract API radiomic ...

Deep learning works by using multilayered neural networks. To make sense of the ... This process, called feature extraction, takes time, and the success of the model depends on the programmer ...

In recent years, prominent research has focused on enhancing the accuracy of BCI decoding algorithms by effectively utilizing meaningful features extracted from electroencephalographic (EEG) signals.

The changes of brain network are ... branch parallel feature extraction. In addition, we construct a hypergraph on the captured features to explore the intrinsic features in the high-dimensional space ...

Deep neural networks are a type of deep learning, which is a type of machine learning. Deep neural networks are used in a variety of applications, including speech recognition, computer vision, and ...

Read the 20 winning stories. By The Learning Network This year’s Super Bowl was likely the biggest wagering event in U.S. sports betting history. Is legalized gambling hurting fans and sports?

Deep neural networks are a type of deep learning, which is a type of machine learning. Deep neural networks are used in a variety of applications, including speech recognition, computer vision, and ...