Please use one of the following formats to cite this article in your essay, paper or report: APA. Cuffari, Benedette. (2025, April 07). Using Deep Learning for Brain Imaging Data Analysis.

In a recent review published in the Scientific Reports, a group of authors used a graph-convolutional neural network (NN) to predict and analyze the thermal decomposition products of e-liquid ...

Deep learning, especially through convolutional neural networks (CNN) such as the U-Net 3D model, has revolutionized fault identification from seismic data, representing a significant leap over ...

The utilization of a 3D Convolutional Neural Network (3D-CNN) model improves classification performance and provides insights into the multi-domain feature information of EEG signals. Furthermore, our ...

Low latency inference has many applications in edge machine learning. In this paper, we present a run-time configurable convolutional neural network (CNN) inference ASIC design for low-latency edge ...

CGAN can also be applied in manufacturing processes such as lithography to effectively model three-dimensional (3D) ... Supervised discriminative ML algorithms such as regression and convolutional ...

Due to the high level of precision and remarkable capabilities to solve the intricate problems in industry and academia, convolutional neural networks (CNNs) are presented. Speech emotion recognition ...

Convolutional Neural Networks for MNIST Data Using PyTorch. Dr. James McCaffrey of Microsoft Research details the "Hello World" of image classification: a convolutional neural network (CNN) applied to ...

A Convolutional Neural Network (ConvNet/CNN) is a Deep Learning system that can take an image ... input data, and a feature map, among other things. Let’s pretend the input is a color image, which is ...