Recent advances in deep learning have significantly transformed mineral classification methodologies, supplanting labour‐intensive manual approaches with automated, high-precision systems. By ...

The rapid development of deep learning in recent years is largely due to the rapid increase in the scale of data. The availability of large amounts of data is revolutionary for model training by the ...

Integrating deep learning in optical microscopy enhances image analysis, overcoming traditional limitations and improving classification and segmentation tasks.

Researchers have leveraged deep learning techniques to enhance the image quality of a metalens camera. The new approach uses artificial intelligence to turn low-quality images into high-quality ones, ...

An international research team has developed a novel PV fault detection method based on deep learning of aerial images. The proposed methodology utilizes the convolutional neural network (CNN) ...