AIMC Topic: Machine Learning

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Examining unsupervised ensemble learning using spectroscopy data of organic compounds.

Journal of computer-aided molecular design
One solution to the challenge of choosing an appropriate clustering algorithm is to combine different clusterings into a single consensus clustering result, known as cluster ensemble (CE). This ensemble learning strategy can provide more robust and s...

Generalisable machine learning models trained on heart rate variability data to predict mental fatigue.

Scientific reports
A prolonged period of cognitive performance often leads to mental fatigue, a psychobiological state that increases the risk of injury and accidents. Previous studies have trained machine learning algorithms on Heart Rate Variability (HRV) data to det...

Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook.

Nanoscale horizons
In the last few years, electron microscopy has experienced a new methodological paradigm aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine learning and artificial intelligence are answering this call providi...

Self-Supervised Action Representation Learning Based on Asymmetric Skeleton Data Augmentation.

Sensors (Basel, Switzerland)
Contrastive learning has received increasing attention in the field of skeleton-based action representations in recent years. Most contrastive learning methods use simple augmentation strategies to construct pairs of positive samples. When using such...

Classification Framework of the Bearing Faults of an Induction Motor Using Wavelet Scattering Transform-Based Features.

Sensors (Basel, Switzerland)
In the machine learning and data science pipelines, feature extraction is considered the most crucial component according to researchers, where generating a discriminative feature matrix is the utmost challenging task to achieve high classification a...

GediNET for discovering gene associations across diseases using knowledge based machine learning approach.

Scientific reports
The most common approaches to discovering genes associated with specific diseases are based on machine learning and use a variety of feature selection techniques to identify significant genes that can serve as biomarkers for a given disease. More rec...

DNN-PNN: A parallel deep neural network model to improve anticancer drug sensitivity.

Methods (San Diego, Calif.)
With the rapid development of deep learning techniques and large-scale genomics database, it is of great potential to apply deep learning to the prediction task of anticancer drug sensitivity, which can effectively improve the identification efficien...

COMMUTE: Communication-efficient transfer learning for multi-site risk prediction.

Journal of biomedical informatics
OBJECTIVES: We propose a communication-efficient transfer learning approach (COMMUTE) that effectively incorporates multi-site healthcare data for training a risk prediction model in a target population of interest, accounting for challenges includin...