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

A machine learning model trained on EEG data from patients recovering from strokes helps predict how new patients will regain ...

A novel multi-task XGBoost model shows robust overall performance in predicting antimicrobial resistance in common gram-negative pathogens.

A deep learning model using retinal images obtained during ROP screening may be used to predict diagnosis of BPD and PH.

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 ...

Scientists have created an AI model that forecasts moderate heat stress—a major precursor to coral bleaching—at sites along ...

The Department of Information Technology at North-Eastern Hill University has developed a Landslide Susceptibility Map using ...

Deep-learning model decodes the regulatory effects of DNA changes ...

Scientists have created an AI model that forecasts moderate heat stress — a major precursor to coral bleaching — at sites ...

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 ...

Scientists have created an AI model that forecasts moderate heat stress — a major precursor to coral bleaching — at sites ...

Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression with pseudo-inverse training implemented using JavaScript. Compared to other training techniques, such as ...