AIMC Topic: Unsupervised Machine Learning

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An unsupervised machine learning approach to evaluate sports facilities condition in primary school.

PloS one
Sports facilities have been acknowledged as one of the crucial environmental factors for children's physical education, physical fitness, and participation in physical activity. Finding a solution for the effective and objective evaluation of the con...

An analysis framework for clustering algorithm selection with applications to spectroscopy.

PloS one
Cluster analysis is a valuable unsupervised machine learning technique that is applied in a multitude of domains to identify similarities or clusters in unlabelled data. However, its performance is dependent of the characteristics of the data it is b...

Applications of multivariate analysis and unsupervised machine learning to ToF-SIMS images of organic, bioorganic, and biological systems.

Biointerphases
Time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging offers a powerful, label-free method for exploring organic, bioorganic, and biological systems. The technique is capable of very high spatial resolution, while also producing an enormo...

Multimodal driver state modeling through unsupervised learning.

Accident; analysis and prevention
Naturalistic driving data (NDD) can help understand drivers' reactions to each driving scenario and provide personalized context to driving behavior. However, NDD requires a high amount of manual labor to label certain driver's state and behavioral p...

Bayesian machine learning analysis of single-molecule fluorescence colocalization images.

eLife
Multi-wavelength single-molecule fluorescence colocalization (CoSMoS) methods allow elucidation of complex biochemical reaction mechanisms. However, analysis of CoSMoS data is intrinsically challenging because of low image signal-to-noise ratios, non...

Unsupervised machine learning for identifying important visual features through bag-of-words using histopathology data from chronic kidney disease.

Scientific reports
Pathologists use visual classification to assess patient kidney biopsy samples when diagnosing the underlying cause of kidney disease. However, the assessment is qualitative, or semi-quantitative at best, and reproducibility is challenging. To discov...

Recreating the Motion Trajectory of a System of Articulated Rigid Bodies on the Basis of Incomplete Measurement Information and Unsupervised Learning.

Sensors (Basel, Switzerland)
Re-creating the movement of an object consisting of articulated rigid bodies is an issue that concerns both mechanical and biomechanical systems. In the case of biomechanical systems, movement re-storation allows, among other things, introducing chan...

Few-Shot Learning for Deformable Medical Image Registration With Perception-Correspondence Decoupling and Reverse Teaching.

IEEE journal of biomedical and health informatics
Deformable medical image registration estimates corresponding deformation to align the regions of interest (ROIs) of two images to a same spatial coordinate system. However, recent unsupervised registration models only have correspondence ability wit...

Unsupervised Learning in Drug Design from Self-Organization to Deep Chemistry.

International journal of molecular sciences
The availability of computers has brought novel prospects in drug design. Neural networks (NN) were an early tool that cheminformatics tested for converting data into drugs. However, the initial interest faded for almost two decades. The recent succe...

Feature engineering solution with structured query language analytic functions in detecting electricity frauds using machine learning.

Scientific reports
Detecting fraud related to electricity consumption is usually a difficult challenge as the input datasets are sometimes unreliable due to missing and inconsistent records, faults, misinterpretation of meter reading remarks, status, etc. In this paper...