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

Microgrids play a growing role in modern power systems, supporting renewable integration, local resilience, and decentralized energy management. Yet as renewable penetration rises, maintaining stable ...

Series B and C start-ups are evaluated across three main areas: financial performance, funding and valuation and operational capabilities ...

Financial word of the day: Heteroscedasticity describes a situation where risk (variance) changes with the level of a ...

New research shows supervised machine learning models combining Helicobacter pylori genomic data with patient demographics can accurately predict gastric cancer risk.

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 ...

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 ...

This paper presents a novel framework for optimizing Carbon Release (CR) through an AI-driven approach to Fossil Fuel Intake (FFI) management. We propose a new training methodology for AI models to ...

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 ...