Master Market Segmentation for Enhanced Profitability and Growth

Discover how effective market segmentation identifies profitable customers and optimizes pricing, distribution, and product ...
Frontiers

Cluster segmentation and stereo vision-based apple localization algorithm for robotic harvesting

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

Customer Segmentation with K-Means Clustering

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 ...
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

K-means Clustering for Image Segmentation Using Approximate Booth Multiplier

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 ...