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Cluster Analysis

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Cluster-CAM: Cluster-weighted visual interpretation of CNNs' decision in image classification.

Neural networks : the official journal of the International Neural Network Society
Despite the tremendous success of convolutional neural networks (CNNs) in computer vision, the mechanism of CNNs still lacks clear interpretation. Currently, class activation mapping (CAM), a famous visualization technique to interpret CNN's decision...

Clothing-invariant contrastive learning for unsupervised person re-identification.

Neural networks : the official journal of the International Neural Network Society
Clothing change person re-identification (CC-ReID) aims to match images of the same person wearing different clothes across diverse scenes. Leveraging biological features or clothing labels, existing CC-ReID methods have demonstrated promising perfor...

Machine learning models and performance dependency on 2D chemical descriptor space for retention time prediction of pharmaceuticals.

Journal of chromatography. A
The predictive modeling of liquid chromatography methods can be an invaluable asset, potentially saving countless hours of labor while also reducing solvent consumption and waste. Tasks such as physicochemical screening and preliminary method screeni...

Machine-learning clustering analysis identifies novel phenogroups in patients with ST-elevation acute myocardial infarction.

International journal of cardiology
BACKGROUND: Machine learning clustering of patients with ST-elevation acute myocardial infarction (STEMI) may provide important insights into their risk profile, management and prognosis.

Real-Time Tracking Data and Machine Learning Approaches for Mapping Pedestrian Walking Behavior: A Case Study at the University of Moratuwa.

Sensors (Basel, Switzerland)
The growing urban population and traffic congestion underline the importance of building pedestrian-friendly environments to encourage walking as a preferred mode of transportation. However, a major challenge remains, which is the absence of such ped...

A GA-stacking ensemble approach for forecasting energy consumption in a smart household: A comparative study of ensemble methods.

Journal of environmental management
The considerable amount of energy utilized by buildings has led to various environmental challenges that adversely impact human existence. Predicting buildings' energy usage is commonly acknowledged as encouraging energy efficiency and enabling well-...

DCRELM: dual correlation reduction network-based extreme learning machine for single-cell RNA-seq data clustering.

Scientific reports
Single-cell ribonucleic acid sequencing (scRNA-seq) is a high-throughput genomic technique that is utilized to investigate single-cell transcriptomes. Cluster analysis can effectively reveal the heterogeneity and diversity of cells in scRNA-seq data,...

Establishing central sensitization inventory cut-off values in Dutch-speaking patients with chronic low back pain by unsupervised machine learning.

Computers in biology and medicine
BACKGROUND: Human Assumed Central Sensitization (HACS) is involved in the development and maintenance of chronic low back pain (CLBP). The Central Sensitization Inventory (CSI) was developed to evaluate the presence of HACS, with a cut-off value of 4...

Symptom phenotyping in people with cystic fibrosis during acute pulmonary exacerbations using machine-learning K-means clustering analysis.

Journal of cystic fibrosis : official journal of the European Cystic Fibrosis Society
INTRODUCTION: People with cystic fibrosis (PwCF) experience frequent symptoms associated with chronic lung disease. A complication of CF is a pulmonary exacerbation (PEx), which is often preceded by an increase in symptoms and a decline in lung funct...

Customer churn modeling in telecommunication using a novel multi-objective evolutionary clustering-based ensemble learning.

PloS one
Customer churn prediction is vital for organizations to mitigate costs and foster growth. Ensemble learning models are commonly used for churn prediction. Diversity and prediction performance are two essential principles for constructing ensemble cla...