AIMC Topic: Unsupervised Machine Learning

Clear Filters Showing 511 to 520 of 828 articles

Unsupervised method for normalization of hematoxylin-eosin stain in histological images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Histological images stained with hematoxylin-eosin are widely used by pathologists for cancer diagnosis. However, these images can have color variations that highly influence the histological image processing techniques. To deal with this potential l...

Physiological indices of challenge and threat: A data-driven investigation of autonomic nervous system reactivity during an active coping stressor task.

Psychophysiology
We utilized a data-driven, unsupervised machine learning approach to examine patterns of peripheral physiological responses during a motivated performance context across two large, independent data sets, each with multiple peripheral physiological me...

HetEnc: a deep learning predictive model for multi-type biological dataset.

BMC genomics
BACKGROUND: Researchers today are generating unprecedented amounts of biological data. One trend in current biological research is integrated analysis with multi-platform data. Effective integration of multi-platform data into the solution of a singl...

Mapping differential responses to cognitive training using machine learning.

Developmental science
We used two simple unsupervised machine learning techniques to identify differential trajectories of change in children who undergo intensive working memory (WM) training. We used self-organizing maps (SOMs)-a type of simple artificial neural network...

Characterization of clinical patterns of dengue patients using an unsupervised machine learning approach.

BMC infectious diseases
BACKGROUND: Despite the greater sensitivity of the new dengue clinical classification proposed by the World Health Organization (WHO) in 2009, there is a need for a better definition of warning signs and clinical progression of dengue cases. Classic ...

AULD: Large Scale Suspicious DNS Activities Detection via Unsupervised Learning in Advanced Persistent Threats.

Sensors (Basel, Switzerland)
In recent years, sensors in the Internet of things have been commonly used in Human's life. APT (Advanced Persistent Threats) has caused serious damage to network security and the sensors play an important role in the attack process. For a long time,...

Unsupervised Learning Techniques for the Investigation of Chronic Rhinosinusitis.

The Annals of otology, rhinology, and laryngology
OBJECTIVES: This article reviews the principles of unsupervised learning, a novel technique which has increasingly been reported as a tool for the investigation of chronic rhinosinusitis (CRS). It represents a paradigm shift from the traditional appr...

A demonstration of unsupervised machine learning in species delimitation.

Molecular phylogenetics and evolution
One major challenge to delimiting species with genetic data is successfully differentiating population structure from species-level divergence, an issue exacerbated in taxa inhabiting naturally fragmented habitats. Many fields of science are now usin...

Unsupervised learning of probabilistic diffeomorphic registration for images and surfaces.

Medical image analysis
Classical deformable registration techniques achieve impressive results and offer a rigorous theoretical treatment, but are computationally intensive since they solve an optimization problem for each image pair. Recently, learning-based methods have ...