Traders are showing signs of increased speculative appetite for Ethereum, but what do machine learning algorithms think about its momentum?

Abstract: Technological advancements occur rapidly from year to year. Currently, we experience the benefits of this rapid technological development, including the widespread use of smartphones in our ...

Accurate land use/land cover (LULC) classification remains a persistent challenge in rapidly urbanising regions especially, in the Global South, where cloud cover, seasonal variability, and limited ...

The CMS Collaboration has shown, for the first time, that machine learning can be used to fully reconstruct particle collisions at the LHC. This new approach can reconstruct collisions more quickly ...

Accurate prediction of mud loss volume in drilling operations is a critical challenge in industries such as petroleum engineering and geothermal well construction. Unforeseen mud loss leads to ...

XRP has lost some steam over the past twenty-four hours as the Senate delayed a key crypto market structure bill on January 15. At the same time, daily trading volume slipped 30% as the broader market ...

The workflow encompasses patient datacollection and screening, univariate regression analysis for initial variable selection, systematic comparison of 91 machine learning models,selection and ...

A machine learning algorithm used gene expression profiles of patients with gout to predict flares. The PyTorch neural network performed best, with an area under the curve of 65%. The PyTorch model ...

Background: Coronary Artery Disease (CAD) is one of the biggest causes of mortality worldwide. Risk stratification for early detection is essential for the primary prevention of CAD. QRISK3 is known ...

Abstract: Soil organic matter (SOM) is essential for maintaining soil structure, nutrient supply, and water regulation in cultivated land, significantly impacting agricultural productivity and the ...