Why today’s AI systems struggle with consistency, and how emerging world models aim to give machines a steady grasp of space ...

Machine learning can help predict whether people newly diagnosed with MS will experience disability worsening that occurs ...

Gas sensing material screening faces challenges due to costly trial-and-error methods and the complexity of multi-parameter ...

Abstract: A precise change detection in the multi-temporal optical images is considered as a crucial task. Although a variety of machine learning-based change detection algorithms have been proposed ...

Background Annually, 4% of the global population undergoes non-cardiac surgery, with 30% of those patients having at least ...

Introduction Application of artificial intelligence (AI) tools in the healthcare setting gains importance especially in the domain of disease diagnosis. Numerous studies have tried to explore AI in ...

Advanced fraud detection system using machine learning to identify fraudulent transactions and activities. This project implements multiple machine learning algorithms including Random Forest, XGBoost ...

Accurately quantifying forest volume and identifying its driving mechanisms are critical for achieving carbon neutrality objectives. Using data from the National Forest Inventory (NFI), plot-level ...

Abstract: This paper presents the design of a predictive model based on a combination of ARIMA and Random Forest algorithms, specifically tailored for forecasting medal scenarios. By establishing a ...

Background: Neovascular glaucoma (NVG) is one of the most severe complications of proliferative diabetic retinopathy (PDR), carrying a high risk of blindness. Establishing an effective risk prediction ...