[HTML][HTML] A hybrid feature learning approach based on convolutional kernels for ATM fault prediction using event-log data
Abstract Predictive Maintenance (PdM) methods aim to facilitate the scheduling of
maintenance work before equipment failure. In this context, detecting early faults in …
maintenance work before equipment failure. In this context, detecting early faults in …
Machine Learning and Signal Processing for Bridge Traffic Classification with Radar Displacement Time-Series Data
M Arnold, S Keller - Infrastructures, 2024 - mdpi.com
This paper introduces a novel nothing-on-road (NOR) bridge weigh-in-motion (BWIM)
approach with deep learning (DL) and non-invasive ground-based radar (GBR) time-series …
approach with deep learning (DL) and non-invasive ground-based radar (GBR) time-series …
Eigen-entropy based time series signatures to support multivariate time series classification
Most current algorithms for multivariate time series classification tend to overlook the
correlations between time series of different variables. In this research, we propose a …
correlations between time series of different variables. In this research, we propose a …
POCKET: Pruning random convolution kernels for time series classification from a feature selection perspective
In recent years, two competitive time series classification models, namely, ROCKET and
MINIROCKET, have garnered considerable attention due to their low training cost and high …
MINIROCKET, have garnered considerable attention due to their low training cost and high …
An end-to-end machine learning approach with explanation for time series with varying lengths
M Schneider, N Greifzu, L Wang, C Walther… - Neural Computing and …, 2024 - Springer
An accurate prediction of complex product quality parameters from process time series by an
end-to-end learning approach remains a significant challenge in machine learning. A …
end-to-end learning approach remains a significant challenge in machine learning. A …
P-ROCKET: Pruning Random Convolution Kernels for Time Series Classification
In recent years, two time series classification models, ROCKET and MINIROCKET, have
attracted much attention for their low training cost and state-of-the-art accuracy. Utilizing …
attracted much attention for their low training cost and state-of-the-art accuracy. Utilizing …
Multivariate Functional Linear Discriminant Analysis for the Classification of Short Time Series with Missing Data
Functional linear discriminant analysis (FLDA) is a powerful tool that extends LDA-mediated
multiclass classification and dimension reduction to univariate time-series functions …
multiclass classification and dimension reduction to univariate time-series functions …
A Multi-view Feature Construction and Multi-Encoder-Decoder Transformer Architecture for Time Series Classification
Time series data plays a significant role in many research fields since it can record and
disclose the dynamic trends of a phenomenon with a sequence of ordered data points. Time …
disclose the dynamic trends of a phenomenon with a sequence of ordered data points. Time …
Robust Feature Selection With Weight Cost Maximin Optimization
Recently, applying data-independent random feature extraction methods has been
promising for time-series classifications. However, a large portion of the features generated …
promising for time-series classifications. However, a large portion of the features generated …
[PDF][PDF] ENHANCING USER EXPERIENCE IN HOME NETWORKS WITH MACHINE LEARNING‑BASED CLASSIFICATION
R Rai, T Basikolo, SB TSB - 2024 - itu.int
With the rapid development of mobile Internet, home broadband has been integrated into
people's daily lives, and the market has become increasingly saturated. User experience …
people's daily lives, and the market has become increasingly saturated. User experience …