Deep learning for time series classification and extrinsic regression: A current survey
Time Series Classification and Extrinsic Regression are important and challenging machine
learning tasks. Deep learning has revolutionized natural language processing and computer …
learning tasks. Deep learning has revolutionized natural language processing and computer …
[HTML][HTML] Multivariate Time-Series Forecasting: A Review of Deep Learning Methods in Internet of Things Applications to Smart Cities
V Papastefanopoulos, P Linardatos… - Smart Cities, 2023 - mdpi.com
Smart cities are urban areas that utilize digital solutions to enhance the efficiency of
conventional networks and services for sustainable growth, optimized resource …
conventional networks and services for sustainable growth, optimized resource …
[HTML][HTML] Unsupervised feature based algorithms for time series extrinsic regression
Abstract Time Series Extrinsic Regression (TSER) involves using a set of training time series
to form a predictive model of a continuous response variable that is not directly related to the …
to form a predictive model of a continuous response variable that is not directly related to the …
3D elastic wave propagation with a factorized Fourier neural operator (F-FNO)
Numerical simulations are computationally demanding in three-dimensional (3D) settings
but they are often required to accurately represent physical phenomena. Neural operators …
but they are often required to accurately represent physical phenomena. Neural operators …
Phase neural operator for multi‐station picking of seismic arrivals
Seismic wave arrival time measurements form the basis for numerous downstream
applications. State‐of‐the‐art approaches for phase picking use deep neural networks to …
applications. State‐of‐the‐art approaches for phase picking use deep neural networks to …
[HTML][HTML] Deep Learning for Intrusion Detection Systems (IDSs) in Time Series Data
K Psychogyios, A Papadakis, S Bourou, N Nikolaou… - Future Internet, 2024 - mdpi.com
The advent of computer networks and the internet has drastically altered the means by
which we share information and interact with each other. However, this technological …
which we share information and interact with each other. However, this technological …
Real-time seismic intensity prediction using self-supervised contrastive gnn for earthquake early warning
Seismic intensity prediction from early or initial seismic waves received by a few seismic
stations can enhance earthquake early warning systems (EEWSs), particularly in ground …
stations can enhance earthquake early warning systems (EEWSs), particularly in ground …
Enhancing road safety through accurate detection of hazardous driving behaviors with graph convolutional recurrent networks
Car accidents remain a significant public safety issue worldwide, with the majority of them
attributed to driver errors stemming from inadequate driving knowledge, non-compliance …
attributed to driver errors stemming from inadequate driving knowledge, non-compliance …
Geology-constrained dynamic graph convolutional networks for seismic facies classification
Knowing a land's facies type before drilling is an essential step in oil exploration. In seismic
surveying, subsurface images are analyzed to segment and classify the facies in that …
surveying, subsurface images are analyzed to segment and classify the facies in that …
Exploring Challenges in Deep Learning of Single-Station Ground Motion Records
Contemporary deep learning models have demonstrated promising results across various
applications within seismology and earthquake engineering. These models rely primarily on …
applications within seismology and earthquake engineering. These models rely primarily on …