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

From autonomous cars to video games, reinforcement learning (machine learning through interaction with environments) can have ...

Accurately tracking atmospheric greenhouse gases requires not only fast predictions but also reliable estimates of ...

Ensemble integrating three architectures achieved area under the curve of 0.9208, outperforming individual models ...

Interpretable AI model could offer new insights into why medicines cause certain side effects, helping to improve future drug safety predictions.

The IT team at North-Eastern Hill University has launched an innovative AI-driven landslide mapping tool specifically for Meghalaya. Utilizing an impressive combination of ten machine learning ...

(2026) AI Assisted Material Selection Framework for Corrosion Resistant Steels in Onshore Oil and Gas Pipelines. Open Journal ...

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 ...

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

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

But what has a jar in a desert cave to do with machine learning? A Bedouin shepherd who sought one of the limestone hills ...

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 ...