Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
Bake off redux: a review and experimental evaluation of recent time series classification algorithms
In 2017, a research paper (Bagnall et al. Data Mining and Knowledge Discovery 31 (3): 606-
660.) compared 18 Time Series Classification (TSC) algorithms on 85 datasets from the …
660.) compared 18 Time Series Classification (TSC) algorithms on 85 datasets from the …
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 …
Human activity recognition based on multienvironment sensor data
With the development of artificial intelligence and the broad application of sensors, human
activity recognition (HAR) technologies based on noninvasive environmental sensors have …
activity recognition (HAR) technologies based on noninvasive environmental sensors have …
Densely knowledge-aware network for multivariate time series classification
Multivariate time series classification (MTSC) based on deep learning (DL) has attracted
increasingly more research attention. The performance of a DL-based MTSC algorithm is …
increasingly more research attention. The performance of a DL-based MTSC algorithm is …
Deep contrastive representation learning with self-distillation
Recently, contrastive learning (CL) is a promising way of learning discriminative
representations from time series data. In the representation hierarchy, semantic information …
representations from time series data. In the representation hierarchy, semantic information …
[HTML][HTML] Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry
Recent developments in maintenance modelling fueled by data-based approaches such as
machine learning (ML), have enabled a broad range of applications. In the automotive …
machine learning (ML), have enabled a broad range of applications. In the automotive …
A transformer-based framework for multivariate time series representation learning
We present a novel framework for multivariate time series representation learning based on
the transformer encoder architecture. The framework includes an unsupervised pre-training …
the transformer encoder architecture. The framework includes an unsupervised pre-training …
Attack graph model for cyber-physical power systems using hybrid deep learning
Electrical power grids are vulnerable to cyber attacks, as seen in Ukraine in 2015 and 2016.
However, existing attack detection methods are limited. Most of them are based on power …
However, existing attack detection methods are limited. Most of them are based on power …
The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances
Abstract Time Series Classification (TSC) involves building predictive models for a discrete
target variable from ordered, real valued, attributes. Over recent years, a new set of TSC …
target variable from ordered, real valued, attributes. Over recent years, a new set of TSC …
HIVE-COTE 2.0: a new meta ensemble for time series classification
Abstract The Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE)
is a heterogeneous meta ensemble for time series classification. HIVE-COTE forms its …
is a heterogeneous meta ensemble for time series classification. HIVE-COTE forms its …