Researchers have applied object recognition technology to track changes in brain cell structure and provide new insights into how the brain responds to heroin use, withdrawal and relapse.

This valuable study introduces a self-supervised machine learning method to classify C. elegans postures and behaviors directly from video data, offering an alternative to the skeleton-based ...

The method combines two strategies: an ensemble of three unsupervised learning techniques using the SciKit-learn library and a percentile-gradient approach. The goal is to minimize false positives by ...

In the U.S., credit card fraud costs $5 billion annually, identity theft adds $16.4 billion, and Medicare fraud drains $60 billion each year. A new machine learning breakthrough generates accurate ...

However, current deep learning-based image reconstruction methods require a large number of voltage-conductivity samples for training. This article proposes a sensitivity-guided unsupervised learning ...

2010). Common unsupervised learning methods include principal metabolite analysis, hierarchical cluster analysis, Gustafson-Kessel clustering and fuzzy C-means clustering. They are particularly ...

He also discussed the "education" of such machines "by means of rewards and punishments." Turing's ideas ultimately led to the development of reinforcement learning, a branch of artificial ...

Scikit-learn library and Statistics and Machine Learning Toolbox within MathWorks were then used to perform unsupervised clustering and supervised regression learning. The impact of dataset ...

Eligibility criteria Studies were included if they involved the use of machine learning methods to predict functioning, relapse and/or remission among individuals with psychotic spectrum disorders.