Time series classification has attracted significant interest over the past decade. One of the
promising approaches is shapelet based algorithms, which are interpretable, more accurate …

Natural language processing (NLP) is one of the key techniques in intelligent question-
answering (Q&A) systems. Although recurrent neural networks and long short-term memory …

Multivariate time series classification is a machine learning problem that can be applied to
automate a wide range of real-world data analysis tasks. RandOm Convolutional KErnel …

Shapelet is a time series subsequence that can best represent the time series of one class.
Shapelet can improve the accuracy and efficiency of classification, as well as the …

Time series classification is an essential part of time series analysis research and has
attracted generous researchers' attention. Representation learning and feature space …

Time series classification (TSC) has attracted significant interest over the past decade. A lot
of TSC methods have been proposed. Among these TSC methods, shapelet based methods …

The nanopore technique employs a nanoscale cavity to electrochemically confine individual
molecules, achieving ultrasensitive single-molecule analysis based on evaluating the …

Objective. Electroencephalogram (EEG) based emotion recognition mainly extracts
traditional features from time domain and frequency domain, and the classification accuracy …

Time series classification attracted significant interest over the past decade as a result of the
enormous data which can be inserted into the Cyber-Physical System. However, in such …

The application of anomaly detection to data monitoring is a fundamental requirement of the
public service systems of a smart city. Many detection methods have been proposed for …