Nature

Machine learning articles from across Nature Portfolio

Researchers developed NiCLIP, an AI model that decodes cognitive tasks, concepts, and domains from brain activation patterns. Trained on over 23,000 papers, NiCLIP excels at group-level decoding, ...

[Part 1] What is the AI foundation "Machine Learning"? Explaining the mechanism and 3 ...

Estimated reading time: approx. 3 minutes 30 secondsWhen researching AI, you often see the terms "machine learning" and "deep ...
HUB

Machine learning method could speed path to cleaner energy solutions

The process of testing new solar cell technologies has traditionally been slow and costly, requiring multiple steps. Led by a fifth-year PhD student, a Johns Hopkins team has developed a machine ...
Nature

Feature Selection Techniques in Machine Learning Systems articles from across Nature Portfolio

Feature selection (FS) is a critical step in hyperspectral image (HSI) classification, essential for reducing data dimensionality while preserving classification accuracy. However, FS for HSIs remains ...
BIOENGINEER

Deep learning outperforms classical methods for early weed detection

A comparative study of sugar beet and maize seedling imagery found that a custom CNN outperformed classical classifiers, ...

Label Design in Financial Machine Learning: Triple Barrier Method, Meta-Labeling, and ...

In quantitative trading research, a significant amount of time is spent on model architecture, feature engineering, and ...
Medical Xpress

Integrating machine learning with statistical methods enhances disease risk prediction models

Researchers from Peking University have conducted a comprehensive systematic review on the integration of machine learning into statistical methods for disease risk prediction models, shedding light ...
TechRepublic

20+ Machine Learning Methods in Groundbreaking Periodic Table From MIT, Google, Microsoft

Find out how this structured machine learning roadmap called I-Con could lead to breakthroughs in AI. A new “periodic table for machine learning” is reshaping how researchers explore AI, unlocking ...
American Physical Society

Advancing Physical Understanding with Interpretable Machine Learning

A new artificial neural-network architecture opens a window into the workings of a tool previously regarded as a black box. Since the Scientific Revolution, scientific progress has mostly been made by ...
Phys.org

Machine learning method improves accuracy of inverse protein folding for drug design

An AI approach developed by researchers from the University of Sheffield and AstraZeneca, could make it easier to design proteins needed for new treatments. Inverse protein folding is a critical ...
BIOENGINEER

Hybrid machine learning improves lost-circulation prediction in small oil-field dataset

A hybrid CatBoost pipeline using synthetic sampling and optimized class weights achieved the strongest reported ...
Lab Manager

Integrating AI and Machine Learning in Analytical Chemistry

The most immediate and impactful application of AI in analytical chemistry is in data analysis. Traditional methods of interpreting chromatograms or spectra are often time-consuming, prone to human ...