Abstract: One advancing machine-learning-based analysis approach for multivariate time-series data is representing data as a third-order tensor and then applying dimensionality reduction (DR) methods.

Introduction: Immunosenescence is a dynamic process, where both genetic and environmental factors account for the substantial inter-individual variability. This paper integrates all the data on ...

A data science project on a Netflix-style dataset with 8,807 entries. Includes data cleaning, EDA (univariate, bivariate, multivariate), PCA, correlation, ML models ...

The present study introduces a direct approach for classifying blood serum samples as either positive or negative for coronavirus disease (COVID-19) by associating the electrochemical impedance data ...

The world as we know it has been transformed by AI, but perhaps no field has been more profoundly affected than analytics and data science. While traditional data science practices have paved the way ...

ABSTRACT: This research examines the interrelationships among uncertainty avoidance (UA), entrepreneurial motivations, and entrepreneurial intention (EI) within the context of Vietnamese higher ...

ABSTRACT: Identification of variables that influence differences between predicted and observed cycling times for one cyclist commuting to Okanagan College (OC) was performed using principal ...

1 Center for Signal Analysis of Complex Systems, Ansbach University of Applied Sciences, Ansbach, Germany 2 Institute of Mathematics, Julius-Maximilians-Universität, Würzburg, Germany A signal ...

Leveraging AI to help analyze and visualize data gathered from a variety of data sets enables data-driven insights and fast analysis without the high costs of talent and technology. In today's ...