In the last decade, there has been a growing body of literature addressing the utilization of
complex network methods for the characterization of dynamical systems based on time …

In recent years, emerging intelligent algorithms have achieved great success in the domain
of fault diagnosis due to effective feature extraction and powerful learning ability. However …

In the last two decades, there has been an explosion of interest in modeling the brain as a
network, where nodes correspond variously to brain regions or neurons, and edges …

Identifying and quantifying dissimilarities among graphs is a fundamental and challenging
problem of practical importance in many fields of science. Current methods of network …

The analysis and discrimination of time series data has important practical significance.
Currently, transforming the time series data into networks through visibility graph (VG) …

Visibility algorithms are a family of methods that map time series into graphs, such that the
tools of graph theory and network science can be used for the characterization of time …

In this work, we propose a solution for detecting botnet attacks on the Internet of Things (IoT)
by identifying anomalies in the temporal dynamics of their devices. Given their limited …

We present a new approach for handwritten signature classification and verification based
on descriptors stemming from time causal information theory. The proposal uses the …

Strategies based on the extraction of measures from ordinal patterns transformation, such as
probability distributions and transition graphs, have reached relevant advancements in …

Major depressive disorder (MDD) is a chronic mental illness which affects people's well-
being and is often detected at a later stage of depression with a likelihood of suicidal …