It now appears that neural nets may be the next frontier in the advance of computing technology as a whole. But what are ...

Abstract: The brain signal classification is the basis for the implementation ... After that, a novel automatic dynamic convergence learning (ADCL) algorithm is proposed and proved for training neural ...

Abstract: Neural network has been increasingly used in remote sensing image classification in the last few decades. Nevertheless, an efficient method for high resolution remote sensing image ...

In this paper, it is proposed a methodology of automatic PD classification in hydro-generator stator windings using neural networks ... of the training algorithm. In order to mitigate the mentioned ...

This project implements the methodology described in the paper "Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification." The key insight is that not all regions of a ...

At ARVO 2025, in Salt Lake City, Utah, Patipol Tiyajamorn, talked about his poster on using graph neural networks to identify ...

A review by researchers at Tongji University and the University of Technology Sydney published in Frontiers of Computer Science, highlights the powerful role of graph neural networks (GNNs) in ...

EEG’s sensitivity to depression-related brain behavior. The team’s method, the Inner Composition Alignment algorithm, results ...

Confused about cost functions in neural networks? In this video, we break down what cost functions are, why they matter, and which types are best for different applications—from classification to ...

Net, a new deep learning method that accurately identifies 36 rice seed varieties using images. This advances previous ...