AIMC Topic: Machine Learning

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Path-aware multi-scale learning for heterogeneous graph neural network.

Neural networks : the official journal of the International Neural Network Society
Heterogeneous Graph Neural Networks (HGNNs) are a powerful tool for modeling data with diverse node and edge types, found in applications like social networks, recommendation systems, and knowledge graphs, including tasks such as node classification,...

Identification of natural food-derived emulsifiers using QSAR and machine learning: Application in dairy emulsions.

Food chemistry
Emulsifiers maintain the stability of emulsions, and milk protein-formed emulsions are unstable. Hence, efficient ways to screen food-derived compounds need to be identified. This study combined molecular descriptors with machine learning algorithms ...

Unified semantic space learning for cross-modal retrieval.

Neural networks : the official journal of the International Neural Network Society
With the increasing amount of multimodal data on the Internet, cross-modal retrieval has gradually become a hot research topic and has achieved significant progress, especially since graph convolutional networks were introduced. Most methods based on...

Learning multi-regularized mutation-aware correlation filter for object tracking via an adaptive hybrid model.

Neural networks : the official journal of the International Neural Network Society
Discriminative Correlation Filters (DCF) have emerged as a popular and effective approach in object tracking. With promising performance and efficiency, DCF-based trackers achieved impressive attention and reliable tracking results in several challen...

A two-stage machine learning-based risk assessment model for intravenous thrombolysis in acute ischemic stroke (AIS): A multi-center modeling study of pooled datasets.

International journal of medical informatics
OBJECTIVE: Develop a two-stage, machine learning-based thrombolysis risk stratification model from existing medical datasets and electronic health records to predict the risk of early hemorrhagic transformation(HT) and in-hospital mortality(IM) follo...

SmartAlert: Machine learning-based patient-ventilator asynchrony detection system in intensive care units.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Patient-ventilator asynchronies (PVA) are associated with ventilator-induced lung injury and increased mortality. Current detection methods rely on static thresholds, extensive preprocessing, or proprietary ventilator data. ...

Cross-jurisdictional factors linked to gambling frequency in adolescents from 28 European countries: a machine learning approach.

Psychiatry research
Adolescents are vulnerable to experiencing problematic gambling, although its prevalence and potential risk factors vary across countries. This study aims to identify cross-jurisdictional factors associated with higher gambling frequency among adoles...

Predictive modeling of postoperative hyponatremia after pituitary adenoma surgery.

Clinical neurology and neurosurgery
OBJECTIVE: To improve the prediction of postoperative hyponatremia after pituitary surgery by comparing six machine learning (ML) models.

Stabilizing machine learning for reproducible and explainable results: A novel validation approach to subject-specific insights.

Computer methods and programs in biomedicine
INTRODUCTION: Machine Learning (ML) is transforming medical research by enhancing diagnostic accuracy, predicting disease progression, and personalizing treatments. While general models trained on large datasets identify broad patterns across populat...

Ecological risk assessment of oilfield soil through the use of machine learning combining with spatial interaction effects.

Ecotoxicology and environmental safety
With the intensification of oil extraction activities, total petroleum hydrocarbons (TPHs) and toxic elements contamination in soil around oil wells have become severe environmental problems. This paper proposed a novel method based on machine learni...