The Lancet

When to and when not to use machine learning in risk prediction models

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered considerable interest among researchers. The debate around the use of machine ...
The Southern Maryland Chronicle on MSN

How are QA teams using machine learning to predict test failures in real time?

QA teams now use machine learning to analyze past test data and code changes to predict which tests will fail before they run. The technology examines patterns from previous test runs, code commits, ...

Fentanyl or phony? Machine learning algorithm learns to pick out opioid signatures

New forms of fentanyl are created every day. For law enforcement, that poses a challenge: How do you identify a chemical you've never seen before? Researchers at Lawrence Livermore National Laboratory ...
FinanceFeeds

Crypto Machine Learning Algorithms Explained for Beginners

Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions ...
Devdiscourse

Data-driven AI approach narrows diagnostic gap between dengue and chikungunya

Dengue and chikungunya, the two mosquito-borne diseases that frequently circulate at the same time, share the same Aedes ...
Medical Dialogues

AI tools with skull X-rays can help detect meningiomas: Nature study

A new study published in the journal of Scientific Reports proposed a potential diagnostic tool by combining deep learning ...
GitHub

RFX-Fuse: Breiman and Cutler's Random Forests as a Forest Unified Learning and Similarity Engine - Extended with Native Explainable Similarity

RFX-Fuse (Random Forests X [X=compression] — Forest Unified Learning and Similarity Engine) delivers Breiman and Cutler's complete vision for Random Forests as a Forests Unified Machine Learning and ...
Frontiers

A Machine Learning and Remote Sensing Approach for Accurate Forest Sub-compartment Level Vegetation Cover Change Monitoring

1 Beijing Forestry University, Beijing, China 2 Ecological Environment Monitoring Center, Sichuan Forestry and Grassland Survey and Planning Institute, CHENGDU, China The final, formatted version of ...
IEEE

Enhancing Random Forest Using Genetic Algorithm for Lifelong Machine Learning

Abstract: Learning over time for machine learning (ML) models is emerging as a new field, often called continual learning or lifelong Machine learning (LML). Today, deep learning and neural networks ...
Oak Ridge National Lab

A machine learning approach to quantify degradation of nuclear fuels and the effects of fission products

Nuclear fuel performance is critically dependent on understanding the evolution of fuel properties under operational conditions, a complex challenge driven by chemical changes and substantial ...
Frontiers

Development and validation of an interpretable machine learning model for predicting cognitive impairment in patients with sepsis

1 Department of Critical Care Medicine, Shenzhen Baoan Shiyan People’s Hospital, Shenzhen, China 2 Department of Gynecology, Langxin Community Health Service Center, Shenzhen Baoan Shiyan People’s ...