Vinay Deeti is driving a new wave in asset management through intelligent automation, making investment analytics smarter, ...

Characterization of intracellular synapse heterogeneity aides to understand the intricate computational logic of neuronal circuits. Despite recent advances in connectomics, the spatial patterns of ...

Comparing Modeling Approaches for Distributed Contested Logistics. American Journal of Operations Research, 15, 125-145. doi: ...

This important study presents a new method for longitudinally tracking cells in two-photon imaging data that addresses the specific challenges of imaging neurons in the developing cortex. It provides ...

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, particularly ...

Computation application for the k-means algorithm is an unsupervised clustering technique that organizes data into clusters based on similarity. It iteratively assigns data points to centroids and ...

The k-means algorithm is a popular data clustering technique due to its speed and simplicity. However, it is susceptible to issues such as sensitivity to the chosen seeds, and inaccurate clusters due ...

This program implements the K-means clustering algorithm using OpenMP APIs. The K-means algorithm is a popular method of vector quantization that aims to partition n observations into k clusters. Each ...