AIMC Topic: Unsupervised Machine Learning

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Improving the Reliability of Scale-Free Image Morphometrics in Applications with Minimally Restrained Livestock Using Projective Geometry and Unsupervised Machine Learning.

Sensors (Basel, Switzerland)
Advances in neural networks have garnered growing interest in applications of machine vision in livestock management, but simpler landmark-based approaches suitable for small, early stage exploratory studies still represent a critical stepping stone ...

Public sentiment on the global outbreak of monkeypox: an unsupervised machine learning analysis of 352,182 twitter posts.

Public health
OBJECTIVES: This study aimed to study the public's sentiments on the current monkeypox outbreaks via an unsupervised machine learning analysis of social media posts.

Development of Deep Learning Technique of Features for the Analysis of Clinical Images Integrated with CANN.

BioMed research international
Computer tomography is an extensively used method for the detection of the disease in the subjects. Basically, computer-aided tomography depending on the artificial intelligence reveals its significance in smart health care monitoring system. Owing t...

Differentiating a pachychoroid and healthy choroid using an unsupervised machine learning approach.

Scientific reports
The purpose of this study was to introduce a new machine learning approach for differentiation of a pachychoroid from a healthy choroid based on enhanced depth-optical coherence tomography (EDI-OCT) imaging. This study included EDI-OCT images of 103 ...

Unsupervised machine learning identifies predictive progression markers of IPF.

European radiology
OBJECTIVES: To identify and evaluate predictive lung imaging markers and their pathways of change during progression of idiopathic pulmonary fibrosis (IPF) from sequential data of an IPF cohort. To test if these imaging markers predict outcome.

ULMR: An Unsupervised Learning Framework for Mismatch Removal.

Sensors (Basel, Switzerland)
Due to radiometric and geometric distortions between images, mismatches are inevitable. Thus, a mismatch removal process is required for improving matching accuracy. Although deep learning methods have been proved to outperform handcraft methods in s...

Predicting the oxidation states of Mn ions in the oxygen-evolving complex of photosystem II using supervised and unsupervised machine learning.

Photosynthesis research
Serial Femtosecond Crystallography at the X-ray Free Electron Laser (XFEL) sources enabled the imaging of the catalytic intermediates of the oxygen evolution reaction of Photosystem II (PSII). However, due to the incoherent transition of the S-states...

AXEAP: a software package for X-ray emission data analysis using unsupervised machine learning.

Journal of synchrotron radiation
The Argonne X-ray Emission Analysis Package (AXEAP) has been developed to calibrate and process X-ray emission spectroscopy (XES) data collected with a two-dimensional (2D) position-sensitive detector. AXEAP is designed to convert a 2D XES image into...

Identifying endotypes of individuals after an attack of pancreatitis based on unsupervised machine learning of multiplex cytokine profiles.

Translational research : the journal of laboratory and clinical medicine
After an attack of pancreatitis, individuals may develop metabolic sequelae (eg, new-onset diabetes) and/or pancreatic cancer. These new-onset morbidities are, at least in part, driven by low-grade inflammation. The aim was to study the profiles of c...