AIMC Topic: Cluster Analysis

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Colour segmentation of printed fabrics by integrating adaptive neural network and density peak clustering algorithm.

PloS one Aug 11, 2025
With the development of computer vision and image processing technology, color segmentation of printed fabrics has gradually become a key task in the textile industry. However, the existing methods often face the problems of low segmentation accuracy...
Neural Networks, Computer Color Textiles Clustering Algorithms Algorithms Image Processing, Computer-Assisted Cluster Analysis
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Semi-supervised contrastive learning variational autoencoder Integrating single-cell multimodal mosaic datasets.

BMC bioinformatics Aug 4, 2025
As single-cell sequencing technology became widely used, scientists found that single-modality data alone could not fully meet the research needs of complex biological systems. To address this issue, researchers began simultaneously collect multi-mod...
Supervised Machine Learning Autoencoder Single-Cell Analysis Computational Biology Cluster Analysis Humans Software Algorithms
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Risk assessment of corn borer based on feature optimization and weighted spatial clustering: a case study in Shandong Province, China.

Scientific reports Jul 31, 2025
As a typical pest affecting corn yield and safety, corn borer causes serious economic losses worldwide. Climate warming has intensified the occurrence of pest outbreaks in recent years, but the associated risk has not been precisely assessed or under...
Cluster Analysis Animals Machine Learning Zea mays Risk Assessment Algorithms Moths China
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COVID-19 risk stratification among older adults: a machine learning approach to identify personal and health-related risk factors.

BMC public health Jul 29, 2025
BACKGROUND: The COVID-19 pandemic highlighted the need to understand factors influencing individuals' risk perceptions and health behaviors. This study aimed to explore the roles of individuals' knowledge, perception, and health-related issues in det...
Health Knowledge, Attitudes, Practice Cluster Analysis Risk Factors Aged Aged, 80 and over Male Middle Aged Risk Assessment COVID-19 Female SARS-CoV-2 Humans Machine Learning
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K-Means Clustering and Classification of Breast Cancer Images Using Histogram of Oriented Gradients Features and Convolutional Neural Network Models: Diagnostic Image Analysis Study.

JMIR formative research Jul 29, 2025
BACKGROUND: Breast cancer has proven to be the most common type of cancer among females around the world. However, mortality rates can be reduced if it is diagnosed at the initial stages. Interpretation made by an expert is required by conventional d...
Neural Networks, Computer Female Reproducibility of Results Humans Cluster Analysis Mammography Image Interpretation, Computer-Assisted Breast Neoplasms Convolutional Neural Networks Image Processing, Computer-Assisted
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Use of X means and C4.5 algorithms on lateral cephalometric measurements to identify craniofacial patterns.

BMC oral health Jul 26, 2025
BACKGROUND: Craniofacial phenotyping is essential for individualized orthodontic diagnosis and treatment planning. Traditional skeletal classifications, such as the ANB angle, may oversimplify complex relationships among malocclusion types. Machine l...
Adolescent Child Cluster Analysis Decision Trees Algorithms Humans Cephalometry Malocclusion Female Machine Learning Male
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How large is the universe of RNA-like motifs? A clustering analysis of RNA graph motifs using topological descriptors.

PLoS computational biology Jul 15, 2025
Identifying novel and functional RNA structures remains a significant challenge in RNA motif design and is crucial for developing RNA-based therapeutics. Here we introduce a computational topology-based approach with unsupervised machine-learning alg...
Algorithms Computational Biology Cluster Analysis RNA Nucleic Acid Conformation Nucleotide Motifs
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Identifying and characterising asthma subgroups at high risk of severe exacerbations using machine learning and longitudinal real-world data.

BMJ health & care informatics Jul 7, 2025
OBJECTIVES: To identify and characterise distinct subgroups of patients with asthma with severe acute exacerbations (AEs) by using a multistep clustering methodology that combines supervised and unsupervised machine learning.
Machine Learning Female Unsupervised Machine Learning Aged United States Humans Longitudinal Studies Severity of Illness Index Middle Aged Adult Adolescent Cluster Analysis Risk Assessment Asthma Disease Progression Male
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Unsupervised deep clustering as a tool for the identification of dark taxa in biomonitoring.

Environmental monitoring and assessment Jul 4, 2025
The identification of aquatic macroinvertebrates, particularly dark taxa like Chironomidae, due to their complex morphological features and unresolved taxonomy hinder the efficiency of routine biomonitoring. This study proposes an unsupervised deep c...
Chironomidae Animals Biological Monitoring Larva Environmental Monitoring Cluster Analysis
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Cross modality learning of cell painting and transcriptomics data improves mechanism of action clustering and bioactivity modelling.

Scientific reports Jul 2, 2025
In drug discovery, different data modalities (chemical structure, cell biology, quantum mechanics, etc.) are abundant, and their integration can help with understanding aspects of chemistry, biology, and their interactions. Within cell biology, cell ...
RNA-Seq Drug Discovery Humans Cluster Analysis Gene Expression Profiling Machine Learning Transcriptome
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