Another research line that Rus is excited about to make robots smarter was inspired by the development of large language ...

The big challenge in deep learning is that you need a lot of data to train the neural network. Fortunately, one of my ...

To address this issue, this article proposes a deep feature fusion network (DFFNet) for full-scale change detection in remote sensing images. DFFNet enhances the accuracy of boundary information in ...

At the core of AI’s value in cancer care is its performance in early detection and diagnostic support, where deep learning ...

From predictive analytics to autonomous control, AI is making renewable energy systems smarter, faster, and more efficient.

To address these limitations, this article proposes a novel two-stage graph generation framework called contrastive learning-based graph structure denoising network (CLGSDN). This framework formulates ...

A CHEPSTOW company has been featured on CNN for its innovative underwater project. The start-up, named DEEP, was showcased ... Lead diver Phil Short informed the network of the potential future ...

In contrast to the existing single-stage convolutional neural network (CNN) and bidirectional long short-term memory-based (BiLSTM) hybrid models, this paper presents DeepApneaNet, a novel end-to-end ...

New Mexico Secretary of State Maggie Toulouse Oliver, a Democrat, told CNN, “There’s been so much work done … to shore those up and create a national network. Now we’re looking at it being ...

However, the existing CS ISAR imaging methods based on deep learning (DL ... where the sampling network compresses the radar data by a convolutional neural network (CNN), and the reconstruction ...

The integration of deep learning in neuroimaging enhances diagnostic capabilities, offering new insights into neurological ...

Abstract: Developing deep learning models often involves working with ... 72 models with varying configurations – including different convolutional neural network architectures, initial learning rates ...