A hybrid model combining LM, GA, and BP neural networks improves TCM's diagnostic accuracy for IPF, achieving 81.22% ...

Researchers developed and validated a machine-learning algorithm for predicting nutritional risk in patients with nasopharyngeal carcinoma.

In this video, we will study Supervised Learning with Examples. We will also look at types of Supervised Learning and its ...

Acute systemic inflammation has long been suspected to trigger harmful processes within the brain, contributing to ...

Overview: In 2025, Java is expected to be a solid AI and machine-learning language.Best Java libraries for AI in 2025 can ease building neural networks, predict ...

Discover the power of predictive modeling to forecast future outcomes using regression, neural networks, and more for improved business strategies and risk management.

Based Detection, Linguistic Biomarkers, Machine Learning, Explainable AI, Cognitive Decline Monitoring Share and Cite: de Filippis, R. and Al Foysal, A. (2025) Early Alzheimer’s Disease Detection from ...

According to the authors, incorporating a broad spectrum of biomarkers allows the models to reflect the continuous and ...

Background Although chest X-rays (CXRs) are widely used, diagnosing mitral stenosis (MS) based solely on CXR findings remains ...

Fraud detection is defined by a structural imbalance that has long challenged data-driven systems. Fraudulent transactions typically account for a fraction of a percent of total transaction volume, ...

Artificial intelligence brings to classification a scalable, accurate alternative. Using natural language processing and ...

The BCTVNet neural network provides accurate and rapid target volume delineation for cervical cancer brachytherapy ...