With climate change posing an unprecedented global challenge, the demand for environmentally friendly solvents in green ...

In food drying applications, machine learning has demonstrated strong capability in predicting drying rates, moisture ...

When engineers build AI language models like GPT-5 from training data, at least two major processing features emerge: memorization (reciting exact text they’ve seen before, like famous quotes or ...

This repository contains the code and data for the experiments in the paper "Discovering network dynamics with neural symbolic regression", published in Nature Computational Science (2025). Abstract: ...

Precision architecture makes measurement the core of trustworthy AI, letting models reflect real battery behavior—not sensor artifacts. Artificial intelligence and machine learning are transforming ...

Abstract: This paper proposes a predictive regression model based on Convolutional Neural Networks(CNN) for predicting the magnetic field peaks of periodic magnetic systems under different periods ...

Breans Neural Network is a fully customizable feed‑forward neural network designed for real‑world tasks. Built from scratch in pure Java, it supports multiple activation functions, backpropagation, ...

Introduction: Since the rise of molecular high-throughput technologies, many diseases are now studied on multiple omics layers in parallel. Understanding the interplay between microRNAs (miRNA) and ...

Abstract: Symbolic regression provides an analytical method to derive explicit mathematical relationships from empirical data that elucidate underlying processes. The model produced aims to be ...

Neural processing unts (NPUs) are the latest chips you might find in smartphones and laptops — but what are they ard why are they so important? When you purchase through links on our site, we may earn ...