AHA/ASA Journals

Contrastive Machine Learning to Quantify Hypertensive Multiorgan Damage and Identify New Disease Phenotypes: A Multinational Multimodal Study

BACKGROUND: Hypertension induces structural and functional damage in multiple organs. Evidence of subclinical damage ...
C&EN

Chemists ran 50,688 reactions to make a huge open dataset

The dataset, which the researchers have made available on the Open Reaction Database, is nearly five times as large as the ...
SciELO

Pɸ-mi-pa loaded PVA/HA electrospun nanofibers from a statistically designed experiment using machine learning algorithms

Bacteriophages, which infect bacterial cells, have a unique ability to reduce bacterial colonization, particularly in antibiotic-resistant biofilm infections. This study aims to fabricate optimized ...
GitHub

MSTA: Multi Strategy Trading Algorithm

Main guidelines: Why will the algo work? Let us not forget how hard it is to predict asset returns. Here we will try to gain an edge over the market by cleverly combining different signals coming from ...
IEEE

Diagnosis of Demagnetization Faults in PMSGs Based on Performance Evaluation of Machine Learning Algorithms Through Flux and Current Signals

Abstract: This study proposed an effective machine learning (ML)-based fault diagnosis method for demagnetization faults, including “healthy, 30% unipolar demagnetization, 50% multimagnet ...
Scientific Research Publishing

A Novel Efficient and Effective Preprocessing Algorithm for Text Classification ()

1 College of Information Science and Technology, Jinan University, Guangzhou, China. 2 University of Birmingham Joint Institute, Jinan University, Guangzhou, China. Text classification is an essential ...
ascopubs.org

A Comparison of Logistic Regression Against Machine Learning Algorithms for Gastric Cancer Risk Prediction Within Real-World Clinical Data Streams

We trained models using logistic regression (LR) and four commonly used ML algorithms to predict NCGC from age-/sex-matched controls in two EHR systems: Stanford University and the University of ...
IEEE

A Performance Comparison of Machine Learning Algorithms for Load Forecasting in Smart Grid

Abstract: With the rapid increase in the world’s population, the global electricity demand has increased drastically. Therefore, it is required to adopt efficient energy management mechanisms. Since ...