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 …

Time series analysis is a field of data science which is interested in analyzing sequences of
numerical values ordered in time. Time series are particularly interesting because they allow …

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) …

In this paper, we presented a long short-term memory (LSTM) network and adaptive particle
swarm optimization (PSO)-based hybrid deep learning model for forecasting the stock price …

In the literature of mitigating unfairness in machine learning (ML), many fairness measures
are designed to evaluate predictions of learning models and also utilized to guide the …

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 …

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 …

Deep neural networks (DNNs) have been regarded as fundamental tools for many
disciplines. Meanwhile, they are known for their large-scale parameters, high redundancy in …

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 …

Transformer-based deep learning methods have significantly facilitated multivariate time
series classification (MTSC) tasks. However, due to the inherent operation of self-attention …