Neel Somani has built a career that sits at the intersection of theory and practice. His work spans formal methods, mac ...

Around the world, millions of families have suffered forcible separation, through war, trafficking, natural disasters, or ...

In a study titled Recent Applications of Machine Learning Algorithms for Pesticide Analysis in Food Samples, published in the ...

Cost estimation, a pivotal component in project management and production planning, has increasingly harnessed the power of machine learning techniques to augment accuracy and efficiency. By ...

Understanding and preventing drug side effects holds a profound influence on drug development and utilization, profoundly impacting patients’ physical and mental well-being. Traditional artificial ...

Objective This study reviewed the current state of machine learning (ML) research for the prediction of sports-related injuries. It aimed to chart the various approaches used and assess their efficacy ...

Background Early graft failure within 90 postoperative days is the leading cause of mortality after heart transplantation. Existing risk scores, based on linear regression, often struggle to capture ...

A machine learning model incorporating functional assessments predicts one-year mortality in older patients with HF and improves risk stratification beyond established scores. Functional status at ...

A University of Idaho lab received $1.3 million from the Department of Defense to study early detection methods for post-traumatic stress disorder and military family stressors using machine learning ...

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