Researchers have demonstrated, for the first time, that transfer learning can significantly enhance material Z-class identification in muon tomography, even in scenarios with limited or completely ...

Beijing, Feb. 06, 2026 (GLOBE NEWSWIRE) -- WiMi Releases Hybrid Quantum-Classical Neural Network (H-QNN) Technology for Efficient MNIST Binary Image Classification ...

Organizations have a wealth of unstructured data that most AI models can’t yet read. Preparing and contextualizing this data is essential for moving from AI experiments to measurable results. In ...

Abstract: Convolutional Neural Networks (CNNs) dominate medical image classification, yet their “black box” nature limits understanding of their decision-making process. This study applies ...

Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. By mid-October, the Democrats’ chances ...

CNN in deep learning is a special type of neural network that can understand images and visual information. It works just like human vision: first it detects edges, lines and then recognizes faces and ...

To better understand which social media platforms Americans use, Pew Research Center surveyed 5,022 U.S. adults from Feb. 5 to June 18, 2025. SSRS conducted this National Public Opinion Reference ...

Recently, there has been a lot of hullabaloo about the idea that large reasoning models (LRM) are unable to think. This is mostly due to a research article published by Apple, "The Illusion of ...

Develop an AI-based image classification system using CNN and transfer learning. The project includes data preprocessing, model training, fine-tuning, evaluation with precision, recall, and F1-score, ...

Abstract: Convolutional Neural Networks (CNNs) are extensively utilized for image classification due to their ability to exploit data correlations effectively. However, traditional CNNs encounter ...