Tungsten's superior performance in extreme environments makes it a leading candidate for plasma-facing components (PFCs) in ...
A new method for identifying types of plastics, built on advanced spectral imaging and machine learning, could make recycling ...
In this tutorial, we explore how we use Daft as a high-performance, Python-native data engine to build an end-to-end analytical pipeline. We start by loading a real-world MNIST dataset, then ...
Abstract: Image recognition and classification, powered by machine learning, is a rapidly advancing field with broad applications across various industries. Machine learning techniques, especially ...
Traditional machine learning (TML) algorithms remain indispensable tools for the analysis of biomedical images, offering significant advantages in multimodal data integration, interpretability, ...
Today, the plastics industry stands at the threshold of a technological revolution, with artificial intelligence and machine learning poised to transform everything from material development to ...
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: Classifying metals is an essential task in all industries to make sure the materials used in the processes are safe and meet the required standards all while enhancing operational and cost ...
[MICCAI 2025] The official implementation of the paper "Exposing and Mitigating Calibration Biases and Demographic Unfairness in MLLM Few-Shot In-Context Learning for Medical Image Classification".
Some results have been hidden because they may be inaccessible to you
Show inaccessible results