Visual Studio Magazine

Anomaly Detection Using K-Means Clustering with JavaScript

Dr. James McCaffrey presents a complete end-to-end demonstration of anomaly detection using k-means data clustering, ...

Meta’s new image segmentation models can identify objects and people and reconstruct them in 3D

Meta's new image segmentation models can identify objects and people and reconstruct them in 3D - SiliconANGLE ...
Frontiers

Efficient vegetation filtering method using K-means clustering algorithm: case study of high steep rock slope

Rocky high steep slopes are among the most dangerous disaster-causing geological bodies in large-scale engineering projects, like water conservancy and hydropower projects, railway tunnels, and metal ...
GitHub

alam025/customer-segmentation-kmeans-clustering

Advanced K-Means clustering system for customer analytics and segmentation using machine learning. Includes RFM analysis, business insights, and actionable marketing strategies. - ...
IEEE

Optimizing Thermal Image Segmentation Using K-Means Clustering and Morphological Operations for Photovoltaic

Abstract: Thermal imaging has become a critical tool in the diagnosis and maintenance of photovoltaic (PV) panels, particularly in detecting localized hotspots that indicate underlying faults. We ...
GitHub

Used Unsupervised Machine Learning using KMeans for user segmentation of mall customers dataset.

There was an error while loading. Please reload this page. This project demonstrates how to build a K-Means clustering algorithm from scratch to segment mall ...
Scientific Research Publishing

From U-Net to Swin-Unet Transformers: The Next-Generation Advances in Brain Tumor Segmentation with Deep Learning ()

Brain tumor segmentation is a vital step in diagnosis, treatment planning, and prognosis in neuro-oncology. In recent years, deep learning approaches have revolutionized this field, evolving from the ...
IEEE

Image segmentation using Gabor filter and K-means clustering method

Abstract: Segmentation in digital images is a process to separate an object from a background so that the object can be processed for other purposes. Often also used in supporting technology related ...
Scientific Research Publishing

Ng, H.P., Ong, S.H., Foong, K.W.C., Goh, P.S. and Nowinski, W.L. Medical Image Segmentation Using K-Means Clustering and Improved Watershed Algorithm. 2006 IEEE Southwest ...

ABSTRACT: 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 ...