“Over the past decade, deep-learning-based representations have demonstrated remarkable performance in academia and industry. The learning capability of convolutional neural networks (CNNs) originates ...
INT8 provides better performance with comparable precision than floating point for AI inference. But when INT8 is unable to meet the desired performance with limited resources, INT4 optimization is ...
In the ever-evolving world of artificial intelligence, deep neural networks (DNNs) have revolutionized data processing, offering unparalleled accuracy across various ...
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 ...
MicroCloud Hologram Inc. (NASDAQ: HOLO), (HOLO or the Company), a technology service provider, has launched a groundbreaking ...
Learn about the most prominent types of modern neural networks such as feedforward, recurrent, convolutional, and transformer networks, and their use cases in modern AI. Neural networks are the ...
Scientists in Spain have used genetic algorithms to optimize a feedforward artificial neural network for the prediction of energy generation of PV systems. Genetic algorithms use “parents” and ...
Compared to traditional generative adversarial network models, the quantum generative adversarial network model researched by WiMi has shorter simulation time. This benefits from the parallel ...
Awurum, N.P. (2025) Next-Generation Cyber Defense: AI-Powered Predictive Analytics for National Security and Threat Resilience. Open Access Library Journal, 12, 1-17. doi: 10.4236/oalib.1114210 .