Machine learning can predict patients' responses to antidepressants—while disentangling drug and placebo effects

Depression is one of the most widespread mental health disorders worldwide, affecting approximately 4% of the global ...
EurekAlert!

Detailed map reveals groundwater levels across the U.S.

Researchers at Princeton University and the University of Arizona have quantified U.S. groundwater in unprecedented breadth ...
IEEE

Machine Learning Approaches for QAM-16 Demodulation: Evaluating Decision Trees and Random Forests as Hardware Alternatives

Abstract: Quadrature Amplitude Modulation (QAM) is widely used in modern wireless communication systems to transmit data efficiently. Conventional QAM transceivers rely on specialized hardware to ...
GitHub

rskworld/fraud-detection-ml

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

Nottingham Forest and UEFA spending rules explained: Might they breach them? What would that mean?

Nottingham Forest are only a few days from making their return to European football after three decades away. Ange Postecoglou’s side will enjoy a landmark moment when they take on Real Betis in the ...
IEEE

Machine Learning Approaches for Banana Quality Classification: A Performance Comparison of J48 and Random Forest Algorithms

Abstract: This study evaluates the performance of using machine learning models; J48 and Random Forest to classify bananas quality. The existing methods of visual inspection are qualitative and take ...
Scientific Research Publishing

Breiman, L. (2001) Random Forests. Machine Learning, 45, 5-32.

ABSTRACT: Biogas is gaining prominence as a renewable energy source with significant potential to reduce greenhouse gas emissions and mitigate environmental impacts associated with fossil fuels. This ...
Scientific Research Publishing

Breiman, L. (2001) Random Forests. Machine Learning, 45, 5-32.

ABSTRACT: This study presents a comparative analysis of machine learning models for threat detection in Internet of Things (IoT) devices using the CICIoT2023 dataset. We evaluate Logistic Regression, ...