Bibek Bhattarai details Intel's AMX, highlighting its role in accelerating deep learning on CPUs. He explains how AMX ...

Researchers in China have created a dataset of various PV faults and normalized it to accommodate different array sizes and typologies. After testing the new approach in combination with the 1D-CNN ...

Performance analysis of artificial intelligence–driven convolutional neural network architectures for liver tumor segmentation.

Quantum convolutional neural networks (QCNNs) notably employ this strategy, as highlighted in recent studies [31 – 37]. Inspired by the CNN architecture, the QCNN is composed of a sequence of quantum ...

Convolutional neural network (CNN) is the state-of-the-art deep learning approach employed in various applications. Real-time CNN implementations in resource limited embedded systems are becoming ...

Taye, M.M. (2023) Theoretical Understanding of Convolutional Neural Network Concepts, Architectures, Applications, Future Directions. Computation, 11, Article 52.

There are many artificial neural network (ANN) architectures, each suited for specific tasks. This FAQ begins with a review of the components of the neurons that make up ANNs, looks at the basic ...

Fully convolutional networks (FCNs) are a type of neural network architecture commonly used in computer vision tasks such as image segmentation, object detection and image classification.