The subtype composition of seasonal influenza waves varies in space and time. Influenza subtypes A/H1N1, A/H3N2 and B tend to have different impacts on population groups; therefore, understanding the ...
WiFi CSI signals encode a rich manifold of environmental and human information: room geometry via multipath reflections, human body configuration via Fresnel zone perturbations, and temporal dynamics ...
👉 Complete articles on Geometric Deep Learning, Graph Neural Networks, Topological Data Analysis with exercises are available on my Substack newsletter Hands-on Geometric Deep Learning The authors ...
A representation of the cause-effect mechanism is needed to enable artificial intelligence to represent how the world works. Bayesian Networks (BNs) have proven to be an effective and versatile tool ...
We show how random feature maps can be used to forecast dynamical systems with excellent forecasting skill. We consider the tanh activation function and judiciously choose the internal weights in a ...
Abstract: Anomaly detection in multivariate time series (MTS) is crucial in domains such as industrial monitoring, cybersecurity, healthcare, and autonomous driving. Deep learning approaches have ...
Support Vector Machines (SVMs) are a type of supervised machine learning algorithm that can be used for classification and regression tasks. In this article, we will focus on using SVMs for image ...
Forecasting solar irradiance is a critical task in the renewable energy sector, as it provides essential information regarding the potential energy production from solar panels. This study aims to ...
VAR models analyse and predict multivariate time series data, unlike univariate autoregressive models. These models are particularly useful in fields such as economics and weather forecasting. VAR ...
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