EurekAlert!

Smart monitoring for a greener future: New AI-driven model predicts lithium-ion battery health with unprecedented accuracy

An accurate assessment of the state of health (SOH) is the cornerstone for guaranteeing the long-term stable operation of ...
The American Journal of Managed Care

Machine Learning May Enable Earlier Detection of CKD Risk Factors

Both approaches identified hemoglobin as one of the most significant predictors of CKD risk. Additional top-ranked features included blood urea, sodium levels, red blood cell count, potassium, and ...
Technology Networks

Machine Learning Applications in Proteomics

Machine learning enhances proteomics by optimizing peptide identification, structure prediction, and biomarker discovery.

Clinically informed AI outperforms foundation models in spinal cord disease prediction

Cervical spondylotic myelopathy (CSM) refers to spinal cord compression from arthritis in the neck and is the leading cause of spinal cord dysfunction in older adults. CSM is a chronic, progressive ...
IEEE

Machine Learning Algorithms for Nondestructive Sensing of Moisture Content in Grain and Seed

Abstract: Machine learning (ML) models were used to determine the moisture content (MC) for multiple grains and seeds after training on a large dataset obtained through several decades of research.
Open Heart

Enhanced cardiovascular disease risk prediction using integrated machine learning models: a study from the UK Biobank cohort

Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk identification to improve prevention and management strategies. Traditional risk ...
TMCnet

Data Normalization vs. Standardization: What's the Difference and When to Use Each

Data Normalization vs. Standardization is one of the most foundational yet often misunderstood topics in machine learning and ...
IEEE

SVM Outperforms Conventional Models for Rainfall Forecasting: A Machine Learning Perspective

Abstract: Accurate predictions of rainfall are the most essential in water resources management, crop planning and disaster avoidance. The complex and nonlinear trends in the meteorological data tend ...