Abstract: Traditional k-means clustering is widely used to analyze regional and temporal variations in time series data, such as sea levels. However, its accuracy can be affected by limitations, ...

The data and soil profiles in this package are not actual measurements but are derived from field measurements. It is assumed that the soil profile and associated data are typical for the soil at a ...

A machine learning-based methodology to uniquely identify network devices using DNS query patterns, combining unsupervised clustering (K-Means) with supervised classification (Random Forest).

Abstract: The rapid development of intelligent transportation systems has led to the generation of massive vehicular data from onboard sensors, GPS devices, and driving logs, paving the way for ...