Machine learning to predict how fast biodegradable plastics break down in nature

Testing how quickly a biodegradable plastic actually breaks down in the environment can take months, sometimes years, of lab ...
RMD Open

Development and assessment of an assisted diagnosis model using machine learning for identifying adult-onset Still’s disease in fever of unknown origin: a retrospective …

Background Adult-onset Still’s disease (AOSD) is a systemic autoinflammatory disorder lacking a gold-standard diagnostic ...
British Journal of Ophthalmology

Commentary on ‘identifying patients with poor visual outcomes after primary rhegmatogenous retinal detachment surgery using machine learning’

The paper by Sajiki et al 1 gives us a fascinating glimpse of the potential benefits of applying machine learning to large ...
IEEE

Predicting Port Congestion at Busan Port Using Machine Learning Algorithms and Temporal Variables

Ports serve as essential infrastructure for logistics and trade, playing a crucial role in the global economic system. In South Korea, approximately 99.7% of domestic import and export cargo is ...
Phys.org

Machine learning automates material analysis and design using X-ray spectroscopy data

Understanding the properties of different materials is an important step in material design. X-ray absorption spectroscopy (XAS) is an important technique for this, as it reveals detailed insights ...
ahajournals.org

Abstract 4365647: Prediction of Mortality and Morbidity After Acute Myocardial Infarction Using Medication-Enriched Real-World Clinical Data and Machine Learning

Background: Despite advances in acute myocardial infarction (AMI) management, long-term risks such as cardiovascular mortality, recurrent AMI, and heart failure remain substantial. Traditional risk ...
Frontiers

Local-NPDR: a novel variable importance method for explainable machine learning and false discovery diagnosis for ocean worlds biosignatures

Explainable machine learning (ML) is important for biosignature prediction on future astrobiology missions to minimize the risk of false positives due to geochemical biotic mimicry and false negatives ...
Frontiers

Evaluating machine learning models for stroke prediction based on clinical variables

Stroke remains one of the leading causes of global mortality and long-term disability, driving the urgent need for accurate and early risk prediction tools. Traditional models such as the Framingham ...
SciELO

Comparison of supervised machine learning and variable selection methods for body weight prediction of growth pigs using image processing data

This research aimed to compare statistical methods (random forest, RIDGE, LASSO, and elastic net regression) for the prediction of body weight in purebred and crossbred pigs reared in Brazil. This ...
IEEE

A Geographically Random Machine Learning Model for GOME-2 Global Seamless Sun-Induced Chlorophyll Fluorescence Downscaling Products With High Spatiotemporal Resolution

Abstract: Several downscaled datasets of sun-induced chlorophyll fluorescence (SIF) enhance the quality of raw SIF satellite retrievals and offer a better perspective for monitoring terrestrial ...