A team of astronomers led by Michael Janssen (Radboud University, The Netherlands) has trained a neural network with millions ...

Limited memory machines are able to learn and improve over time using data, typically by using artificial neural networks or another programming model. Deep learning, which is a subtype of machine ...

However, the power of transfer learning for medical image denoising tasks has not been fully explored. In this work, we proposed a transfer learning residual convolutional neural network (TLR-CNN) to ...

Here, we show that extrinsic background can significantly affect a classification model using Raman images, yielding misleadingly high accuracies in the distinction of benign and malignant samples of ...

A study reveals machine learning algorithms can predict compressive strength in concrete with waste glass powder, enhancing ...

The use of Artificial Intelligence (AI) to automate cancer detection might allow us to evaluate more cases in less time. In this research, AI-based deep learning models are proposed to classify the ...

Master data science in 2025. Complete guide to machine learning, big data analytics, Python programming, statistical modeling ...

Causal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that elude a purely ...

This will create a folder where all intermediate results are stored so that you can find out where there are problems with your images, if any If you are familiar with Docker and don't feel like ...

Ferguson's expertise lies in using machine learning models to detect anomalies in cybersecurity systems. For this project, he developed machine learning techniques to focus on detecting ...