Deep learning for time series classification: a review
Abstract Time Series Classification (TSC) is an important and challenging problem in data
mining. With the increase of time series data availability, hundreds of TSC algorithms have …
mining. With the increase of time series data availability, hundreds of TSC algorithms have …
[HTML][HTML] Knowledge graph quality control: A survey
A knowledge graph (KG), a special form of semantic network, integrates fragmentary data
into a graph to support knowledge processing and reasoning. KG quality control is important …
into a graph to support knowledge processing and reasoning. KG quality control is important …
Reservoir computing approaches for representation and classification of multivariate time series
Classification of multivariate time series (MTS) has been tackled with a large variety of
methodologies and applied to a wide range of scenarios. Reservoir computing (RC) …
methodologies and applied to a wide range of scenarios. Reservoir computing (RC) …
BDANN: BERT-based domain adaptation neural network for multi-modal fake news detection
Nowadays, with the rapid growth of microblogging networks for news propagation, there are
increasingly more people accessing news through such emerging social media. In the …
increasingly more people accessing news through such emerging social media. In the …
Time-series classification methods: Review and applications to power systems data
Chapter Overview The diffusion in power systems of distributed renewable energy
resources, electric vehicles, and controllable loads has made advanced monitoring systems …
resources, electric vehicles, and controllable loads has made advanced monitoring systems …
Unsupervised change detection in satellite images with generative adversarial network
Detecting changed regions in paired satellite images plays a key role in many remote
sensing applications. The evolution of recent techniques could provide satellite images with …
sensing applications. The evolution of recent techniques could provide satellite images with …
Adaptive polygon generation algorithm for automatic building extraction
Y Zhu, B Huang, J Gao, E Huang… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Buildings serve as the main places of human activities, and it is essential to automatically
extract each building instance for a wide range of applications. Recently, automatic building …
extract each building instance for a wide range of applications. Recently, automatic building …
A new deep neural network framework with multivariate time series for two-phase flow pattern identification
L OuYang, N **, W Ren - Expert Systems with Applications, 2022 - Elsevier
Uncovering flow dynamic behavior of different flow patterns is an important foundation of
multiphase flow research. But the traditional classifier is still adopted in the flow pattern …
multiphase flow research. But the traditional classifier is still adopted in the flow pattern …
Functional echo state network for time series classification
Echo state networks (ESNs) are a new approach to recurrent neural networks (RNNs) that
have been successfully applied in many domains. Nevertheless, an ESN is a predictive …
have been successfully applied in many domains. Nevertheless, an ESN is a predictive …
Multiobjective learning in the model space for time series classification
A well-defined distance is critical for the performance of time series classification. Existing
distance measurements can be categorized into two branches. One is to utilize handmade …
distance measurements can be categorized into two branches. One is to utilize handmade …