AIMC Topic: Machine Learning

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IMU, sEMG, or their cross-correlation and temporal similarities: Which signal features detect lateral compensatory balance reactions more accurately?

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Falls are the leading cause of fatal and non-fatal injuries among seniors worldwide. While laboratory evidence supports the view that impaired ability to execute compensatory balance responses (CBRs) is linked to an increase...

Adaptive robust principal component analysis.

Neural networks : the official journal of the International Neural Network Society
Robust Principal Component Analysis (RPCA) is a powerful tool in machine learning and data mining problems. However, in many real-world applications, RPCA is unable to well encode the intrinsic geometric structure of data, thereby failing to obtain t...

Multi-Level Comparison of Machine Learning Classifiers and Their Performance Metrics.

Molecules (Basel, Switzerland)
Machine learning classification algorithms are widely used for the prediction and classification of the different properties of molecules such as toxicity or biological activity. the prediction of toxic vs. non-toxic molecules is important due to tes...

Massive metagenomic data analysis using abundance-based machine learning.

Biology direct
BACKGROUND: Metagenomics is the application of modern genomic techniques to investigate the members of a microbial community directly in their natural environments and is widely used in many studies to survey the communities of microbial organisms th...

Automated age estimation from MRI volumes of the hand.

Medical image analysis
Highly relevant for both clinical and legal medicine applications, the established radiological methods for estimating unknown age in children and adolescents are based on visual examination of bone ossification in X-ray images of the hand. Our group...

Automated lesion segmentation with BIANCA: Impact of population-level features, classification algorithm and locally adaptive thresholding.

NeuroImage
White matter hyperintensities (WMH) or white matter lesions exhibit high variability in their characteristics both at population- and subject-level, making their detection a challenging task. Population-level factors such as age, vascular risk factor...

Machine Learning and Brain Imaging: Opportunities and Challenges.

Trends in neurosciences
Machine learning approaches may provide ways to link brain activation patterns to behavior at an individual-subject level. Using a comparative performance analysis, Jollans and colleagues (Neuroimage, 2019) highlight in a recent paper key considerati...

Sequence assignment for low-resolution modelling of protein crystal structures.

Acta crystallographica. Section D, Structural biology
The performance of automated model building in crystal structure determination usually decreases with the resolution of the experimental data, and may result in fragmented models and incorrect side-chain assignment. Presented here are new methods for...

The future of activity space and health research.

Health & place
We discuss the future of activity space and health research in the context of a recently published systematic review. Our discussion outlines a number of elements for reflection among the research community. We need to think beyond activity space and...