Deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are designed to partly emulate the functioning and structure of biological neural networks. As a ...

When enterprise adoption requires server farms full of energy-hungry GPUs just to run basic AI services, we face both an ...

AI and multimodal data are reshaping analytics. Success requires architectural flexibility: matching tools to tasks in a ...

Smaller, smarter, and radically efficient - bringing brain-inspired intelligence to battery-powered devices, unlocking new era of real-time, ultra-low power AI at edge ...

Convolutional Neural Networks ... classification, object detection, and image segmentation. The use of ConvNets in visual recognition is inarguably one of the biggest inventions of decade 2010s in ...

Open-source clones of ChatGPT can be fine-tuned at scale and with limited or no expertise, facilitating ‘private' language ...

A key application often envisioned for neuromorphic technology is to implement similarly brain-inspired neural networks, the main AI systems in use today. In addition, spiking neuromorphic devices ...

Esther Ugwueke is a biomedical expert and PhD student at the University of Nebraska Medical Center, United States, where she ...

Abstract: The Graph Neural Network (GNN) exhibits noteworthy performance in hyperspectral image classification ... theory based convolutional operation employed in the graph for HSIC. As a new network ...

Use of various machine learning algorithms for plant disease classification and the evolution of deep convolutional neural network (CNN) based architectures have further enhanced the plant disease ...