How do neural networks work? It's a question that can confuse novices and experts alike. A team from MIT's Computer Science ...

The transformative aspect of merging quantum computing with classical neural networks has been phenomenal in this fast-paced ...

After fine-tuning, the VGG16 convolutional neural network outperformed other deep learning models in 2 of 3 key ...

The purpose of this study was to develop a method of classifying cancers to specific diagnostic categories based on their gene expression signatures using artificial neural networks (ANNs).

Radiologists who evaluate CT scans have diagnostic challenges due to the complexity of underlying anatomy and the potential ...

Concerns about AI's energy use have a lot of people looking into ways to cut down on its power requirements. Many of these focus on hardware and software approaches that are pretty straightforward ...

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

Could this Canadian innovation redefine the way AI models are trained and deployed? Well, based on recent achievements of the ...

Recently, a research team led by Prof. GAO Xiaoming from the Hefei Institutes of Physical Science of the Chinese Academy of Sciences, improved residual neural network to accurately classify and ...

One notable missing feature in most ANN models is top-down feedback, i.e. projections from higher-order layers to lower-order layers in the network. Top-down feedback is ubiquitous in the brain, and ...

A research paper by scientists at Shanghai Jiao Tong University presented a novel channel-wise cumulative spike train image-driven model (cwCST-CNN ...