XL, dynamic interest modeling, and distributed stream computing to analyze large-scale e-commerce user behavior. By improving long-sequence prediction, real-time processing, and behavioral clustering, ...

Introduction National essential medicines lists (NEMLs) guide medicine selection and procurement and are key tools for ...

A privacy-preserving marketing framework applies homomorphic encryption to perform machine learning on encrypted ...

J&K Salvage has been ordered to close by the Pennsylvania Department of Environmental Protection after years of violations. The scrapyard's owner, Joe Darrah, faces potential jail time for contempt of ...

Dr. James McCaffrey presents a complete end-to-end demonstration of anomaly detection using k-means data clustering, implemented with JavaScript. Compared to other anomaly detection techniques, ...

Automated apple harvesting is hindered by clustered fruits, varying illumination, and inconsistent depth perception in complex orchard environments. While deep learning models such as Faster R-CNN and ...

This repository contains computational notebooks and analysis code for research on smart K-means clustering algorithms applied to social exclusion indicators. The project implements and compares ...

Clustering is an unsupervised machine learning technique used to organize unlabeled data into groups based on similarity. This paper applies the K-means and Fuzzy C-means clustering algorithms to a ...

ABSTRACT: Domaining is a crucial process in geostatistics, particularly when significant spatial variations are observed within a site, as these variations can significantly affect the outcomes of ...

Abstract: The K-means is sensitive to the initial choice of cluster centers, leading to the results to be different every time. To address this, a new K-means variant based on decision values is ...