AIMC Topic: Machine Learning

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Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics.

Nature methods
Keypoint tracking algorithms can flexibly quantify animal movement from videos obtained in a wide variety of settings. However, it remains unclear how to parse continuous keypoint data into discrete actions. This challenge is particularly acute becau...

Machine learning vs. regression models to predict the risk of Legionella contamination in a hospital water network.

Annali di igiene : medicina preventiva e di comunita
INTRODUCTION: The periodic monitoring of Legionella in hospital water networks allows preventive measures to be taken to avoid the risk of legionellosis to patients and healthcare workers.

A novel approach for the effective prediction of cardiovascular disease using applied artificial intelligence techniques.

ESC heart failure
AIMS: The objective of this research is to develop an effective cardiovascular disease prediction framework using machine learning techniques and to achieve high accuracy for the prediction of cardiovascular disease.

Illegal Online Gambling Site Detection using Multiple Resource-Oriented Machine Learning.

Journal of gambling studies
The COVID-19 pandemic has led to faster digitalization and illegal online gambling has become popular. As illegal online gambling brings not only financial threats but also breaches in overall cyber security, this study defines the concept of absolut...

Role of artificial intelligence applied to ultrasound in gynecology oncology: A systematic review.

International journal of cancer
The aim of this paper was to explore the role of artificial intelligence (AI) applied to ultrasound imaging in gynecology oncology. Web of Science, PubMed, and Scopus databases were searched. All studies were imported to RAYYAN QCRI software. The ove...

DPGCL: Dual pass filtering based graph contrastive learning.

Neural networks : the official journal of the International Neural Network Society
Graph Contrastive Learning (GCL), which learns node or graph representation from supervision signals derived from the graph data itself, has recently attracted extensive research attention and achieved great success. Remarkably, most of the existing ...

Deep network embedding with dimension selection.

Neural networks : the official journal of the International Neural Network Society
Network embedding is a general-purpose machine learning technique that converts network data from non-Euclidean space to Euclidean space, facilitating downstream analyses for the networks. However, existing embedding methods are often optimization-ba...

The impact of machine learning on the prediction of diabetic foot ulcers - A systematic review.

Journal of tissue viability
INTRODUCTION: Globally, diabetes mellitus poses a significant health challenge as well as the associated complications of diabetes, such as diabetic foot ulcers (DFUs). The early detection of DFUs is important in the healing process and machine learn...

Discovering transformation products of pharmaceuticals in domestic wastewaters and receiving rivers by using non-target screening and machine learning approaches.

The Science of the total environment
Wastewater treatment plants (WWTPs) are an important source of pharmaceuticals in surface water, but information about their transformation products (TPs) is very limited. Here, we investigated occurrence and transformation of pharmaceuticals and TPs...

Patient-ventilator asynchrony classification in mechanically ventilated patients: Model-based or machine learning method?

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Patient-ventilator asynchrony (PVA) is associated with poor clinical outcomes and remains under-monitored. Automated PVA detection would enable complete monitoring standard observational methods do not allow. While model-bas...