The analysis of time series and images is significant across different fields due to their
widespread applications. In the past few decades, many approaches have been developed …

Transform of basic probability assignment to probability distribution is an important aspect of
decision making process. To address this issue, a weighted network method based on the …

How to identify influential spreaders in complex networks is still an open issue in network
science. Many approaches from different perspectives have been proposed to identify vital …

In response to global warming, carbon trading market emerges to reduce carbon emissions.
However, uncertain fluctuations and complicated price mechanisms in the market have …

In the past two decades, network-based analysis has garnered considerable attention for
analyzing time series data across various fields. Time series data can be transformed into …

We address the problem of user intent prediction from clickstream data of an e-commerce
website via two conceptually different approaches: a hand-crafted feature-based …

In this article, we propose a sparse spectra graph convolutional network (SSGCNet) for
epileptic electroencephalogram (EEG) signal classification. The goal is to develop a …

Modern power grids are faced with a series of challenges, such as the ever-increasing
demand for renewable energy sources, extensive urbanization, climate and energy crisis …

Inspired by human brain, the emerging analog‐type memristor employed in neuromorphic
computing systems has attracted tremendous interest. However, existing analog memristors …

Time series has a wide range of applications in various fields. Recently, a new math tool,
named as visibility graph, is developed to transform the time series into complex networks …