Biological neural networks are immensely complex systems underlying all aspects of cognition and behavior. Despite significant advances in neuroscience, a ...
A deep learning framework enhances medical image recognition by optimizing RNN architectures with LSTM, GRU, multimodal fusion, and CNN integration. It improves dynamic lesion detection, temporal ...
Machine learning models called convolutional neural networks (CNNs) power technologies like image recognition and language ...
Photons are fast, stable, and easy to manipulate on chips, making photonic systems a promising platform for QCNNs. However, ...
In the development of modern animal husbandry, the feed industry serves as a crucial material foundation, and extrusion puffing technology has become ...
The advent of artificial intelligence might be just the latest stage in a guiding biological process that has produced ever more complex, mutually dependent organisms over the history of life.
Abstract: Recently, convolutional neural networks (CNNs) and Transformers have achieved considerable success in hyperspectral image (HSI) classification tasks. However, existing methods not only lack ...
As you begin your hybrid quantum approach, here are the advantages, use cases and limitations to keep in mind.
Mingi Kang ’26 received a Fall Research Award from Bowdoin this semester to support his project exploring how two distinct ...
WiMi innovatively combines the robust feature extraction capabilities of QCNN with the dual-discriminator architecture to construct a hybrid quantum-classical generative adversarial framework. The ...
Furthermore, we adopted a novel approach using the one-hot encoding method that transforms the 16 different genotypes into ...
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