Researchers have developed an integrated framework for estimating battery state of health, or SOH, by combining incremental ...

What was the rationale behind applying machine learning (ML) models to improve identification probability in the absence of ...

A new study published in Genome Research presents an interpretable artificial intelligence framework that improves both the accuracy and transparency of genomic prediction, a key challenge in fields ...

Published in Microplastics, the study titled “Canonical Spectral Transformation for Raman Spectra Enables High Accuracy AI Identification of Marine Microplastics” introduces a novel data processing ...

The increasing global demand for sustainable energy and carbon materials, alongside pressing environmental concerns, ...

Read more about Deep learning and AI unlock new era of solar energy forecasting and performance on Devdiscourse ...

An international reserch team developed two deep learning-based IDS models to enhance cybersecurity in SCADA systems. The ...

This study develops a high-accuracy machine-learning framework to predict and optimize metal-doped MnO2 cathodes for aqueous ...

To improve data center efficiency, multiple storage devices are often pooled together over a network so many applications can share them. But even with pooling, significant device capacity remains ...

Machine learning researchers using Ollama will enjoy a speed boost to LLM processing, as the open-source tool now uses MLX on Apple Silicon to fully take advantage of unified memory.

There is no doubt that the semiconductor industry is in an era of rapid and profound transformation, driven by an increasing ...

The multiple condition (MC)-retention model is an uncertainty-aware graph-based neural network that predicts liquid chromatography (LC) retention times across multiple column chem ...