Accurately tracking atmospheric greenhouse gases requires not only fast predictions but also reliable estimates of uncertainty. Researchers have developed a lightweight machine learning framework that ...
By applying new methods of machine learning to quantum chemistry research, Heidelberg University scientists have made significant strides in computational chemistry. They have achieved a major ...
The CMS Collaboration has shown, for the first time, that machine learning can be used to fully reconstruct particle ...
From autonomous cars to video games, reinforcement learning (machine learning through interaction with environments) can have an important impact. That may feel especially true, for example, when ...
Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions or values from labeled historical data, enabling precise signals such as ...
Abstract: Accurate fall risk prediction is crucial for early intervention and prevention, effectively reducing the incidence of falls and the associated harm. This paper proposes a non-contact gait ...
Abstract: This paper conducts a study on the acoustic features of laughter signals and performs experiments to identify the most relevant features. Different acoustic features are extracted from ...
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 ...
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