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 ...
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 ...
Abstract: K-means clustering is an unsupervised learning algorithm that assigns unlabeled data to different clusters depending on the similarity rather than predefined labels. It finds application in ...
ABSTRACT: An ancient fossil fuel, oil is a crucial energy source for various daily activities, such as electricity generation and vehicle operation. However, its ship transportation poses a ...
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