AIMC Topic: Machine Learning

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Communication-efficient federated learning via knowledge distillation.

Nature communications
Federated learning is a privacy-preserving machine learning technique to train intelligent models from decentralized data, which enables exploiting private data by communicating local model updates in each iteration of model learning rather than the ...

Predicting diarrhoea outbreaks with climate change.

PloS one
BACKGROUND: Climate change is expected to exacerbate diarrhoea outbreaks across the developing world, most notably in Sub-Saharan countries such as South Africa. In South Africa, diseases related to diarrhoea outbreak is a leading cause of morbidity ...

Application and potential of artificial intelligence in neonatal medicine.

Seminars in fetal & neonatal medicine
Neonatal care is becoming increasingly complex with large amounts of rich, routinely recorded physiological, diagnostic and outcome data. Artificial intelligence (AI) has the potential to harness this vast quantity and range of information and become...

Machine learning models identify gene predictors of waggle dance behaviour in honeybees.

Molecular ecology resources
The molecular characterization of complex behaviours is a challenging task as a range of different factors are often involved to produce the observed phenotype. An established approach is to look at the overall levels of expression of brain genes-or ...

MarkerML - Marker Feature Identification in Metagenomic Datasets Using Interpretable Machine Learning.

Journal of molecular biology
Identification of environment specific marker-features is one of the key objectives of many metagenomic studies. It aims to identify such features in microbiome datasets that may serve as markers of the contrasting or comparable states. Hypothesis te...

Machine Learning for Healthcare Wearable Devices: The Big Picture.

Journal of healthcare engineering
Using artificial intelligence and machine learning techniques in healthcare applications has been actively researched over the last few years. It holds promising opportunities as it is used to track human activities and vital signs using wearable dev...

Multisource Deep Transfer Learning Based on Balanced Distribution Adaptation.

Computational intelligence and neuroscience
The current traditional unsupervised transfer learning assumes that the sample is collected from a single domain. From the aspect of practical application, the sample from a single-source domain is often not enough. In most cases, we usually collect ...

Risk-based implementation of COLREGs for autonomous surface vehicles using deep reinforcement learning.

Neural networks : the official journal of the International Neural Network Society
Autonomous systems are becoming ubiquitous and gaining momentum within the marine sector. Since the electrification of transport is happening simultaneously, autonomous marine vessels can reduce environmental impact, lower costs, and increase efficie...

Deep Possibilistic -means Clustering Algorithm on Medical Datasets.

Computational and mathematical methods in medicine
In the past, the possibilistic -means clustering algorithm (PCM) has proven its superiority on various medical datasets by overcoming the unstable clustering effect caused by both the hard division of traditional hard clustering models and the suscep...

Recommendation System for Privacy-Preserving Education Technologies.

Computational intelligence and neuroscience
Considering the priority for personalized and fully customized learning systems, the innovative computational intelligent systems for personalized educational technologies are the timeliest research area. Since the machine learning models reflect the...