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, ...
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 ...
Advanced K-Means clustering system for customer analytics and segmentation using machine learning. Includes RFM analysis, business insights, and actionable marketing strategies. - ...
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 ...
In this project, I explored the Mall_Customers.csv dataset with the main focus on customer segmentation using K-Means clustering. The goal was to identify distinct customer groups based on Age, Annual ...
1 Department of Applied Sciences, Intelligent Asset Reliability Centre, Institute of Emerging Digital Technologies, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia 2 Bursa Malaysia Berhad, ...
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 ...
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 ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results