Social media algorithms driven by AI shape far more than what appears on our screens, including how we think, feel, and ...
IEEE Spectrum on MSN
Can AI find physics beyond the standard model?
AI is searching particle colliders for the unexpected ...
Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions ...
Machine learning requires humans to manually label features while deep learning automatically learns features directly from raw data. ML uses traditional algorithms like decision tress, SVM, etc., ...
Introduction: Myocardial ischemia can result in severe cardiovascular complications. However, the impact of clinical factors on myocardial ischemia in individuals with T2DM remains unclear. we applied ...
Abstract: Although deep reinforcement learning (DRL) has made massive progress in policy learning, its reliance on a large number of real-world data samples presents a significant barrier to broader ...
Objectives: This study aims to investigate the efficacy of unsupervised machine learning algorithms, specifically the Gaussian Mixture Model (GMM), K-means clustering, and Otsu automatic threshold ...
ABSTRACT: Purpose: The purpose of this study is to develop a scalable, risk-aware artificial intelligence (AI) framework capable of detecting financial fraud in high-throughput digital transaction ...
Abstract: Feature extraction and selection in the presence of nonlinear dependencies among the data is a fundamental challenge in unsupervised learning. We propose using a Gram-Schmidt (GS) type ...
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