The Temporal Fusion Transformer model provides near-real-time insights into sintering temperatures, addressing critical ...

Researchers from Politecnico di Milano propose a data-driven water leak detection method that treats leaks as anomalies in ...

Abstract: We introduce a new convolutional autoencoder architecture for user modeling and recommendation tasks with several improvements over the state of the art. First, our model has the flexibility ...

ABSTRACT: Traffic prediction is the core of intelligent transportation system, and accurate traffic speed prediction is the key to optimize traffic management. Currently, the traffic speed prediction ...

ABSTRACT: Convolutional auto-encoders have shown their remarkable performance in stacking deep convolutional neural networks for classifying image data during the past several years. However, they are ...

Abstract: Electrocardiograms (ECG) are vital for diagnosing various cardiac conditions but are often corrupted by noise from multiple sources, which can hinder accurate interpretation. Denoising ECG ...

Human inventions, namely engineered systems, have relied on fundamental discoveries in physics and mathematics, e.g., Maxwell’s equations, Quantum mechanics, Information theory, etc., thereby applying ...

This important work presents a novel approach to infer causal relations in non-stationary time series data. To do so, the authors introduce a novel machine-learning model of Temporal Autoencoders for ...

Classification of power system event data is a growing need, particularly where non-protective relaying-based sensors are used to monitor grid performance. Given the high burden of obtaining event ...