Data analysis in modern times involves working with large volumes of data, including time-
series data. This type of data is characterized by its high dimensionality, enormous volume …

In recent decades, the ultimate output from microarray assay, has produced enormous
numbers of microarray datasets, regardless of the used technology. These datasets include …

We propose a machine-learning approach to construct reduced-order models (ROMs) to
predict the long-term out-of-sample dynamics of brain activity (and in general, high …

In recent years, depression has become an increasingly serious problem globally. Previous
research have shown that EEG-based depression recognition is a promising technique to …

In this work, a data-driven framework based on Phase-Field simulations data is proposed to
highlight the capabilities of neural networks to ensure accurate low dimensionality reduction …

Lignin is an essential components of corn stalk and has a wide range of application. To
realize the rapid detection of lignin content in corn straw and increase the detection …

In recent years, Artificial Intelligence has been used to assist healthcare professionals in
detecting and diagnosing neurodegenerative diseases. In this study, we propose a …

Spatial correlation of functional connectivity profiles across matching anatomical locations in
individuals is often calculated to delineate individual differences in functional networks …

Laplacian Eigenmaps is a nonlinear dimensionality reduction algorithm based on graph
theory. The algorithm adopted the Gaussian function to measure the affinity between a pair …

Over the past decade, there has been a growing emphasis on neuroscience to analyze the
human brain from the perspective of complex networks. Here, connectomics, or the study of …