AIMC Topic: Bayes Theorem

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Spatial differentiation of carbon emissions from energy consumption based on machine learning algorithm: A case study during 2015-2020 in Shaanxi, China.

Journal of environmental sciences (China)
Carbon emissions resulting from energy consumption have become a pressing issue for governments worldwide. Accurate estimation of carbon emissions using satellite remote sensing data has become a crucial research problem. Previous studies relied on s...

Layer adaptive node selection in Bayesian neural networks: Statistical guarantees and implementation details.

Neural networks : the official journal of the International Neural Network Society
Sparse deep neural networks have proven to be efficient for predictive model building in large-scale studies. Although several works have studied theoretical and numerical properties of sparse neural architectures, they have primarily focused on the ...

Comparative evaluation of uncertainty estimation and decomposition methods on liver segmentation.

International journal of computer assisted radiology and surgery
PURPOSE: Deep neural networks need to be able to indicate error likelihood via reliable estimates of their predictive uncertainty when used in high-risk scenarios, such as medical decision support. This work contributes a systematic overview of state...

Denoising and uncertainty estimation in parameter mapping with approximate Bayesian deep image priors.

Magnetic resonance in medicine
PURPOSE: To mitigate the problem of noisy parameter maps with high uncertainties by casting parameter mapping as a denoising task based on Deep Image Priors.

Artificial intelligence algorithm comparison and ranking for weight prediction in sheep.

Scientific reports
In a rapidly transforming world, farm data is growing exponentially. Realizing the importance of this data, researchers are looking for new solutions to analyse this data and make farming predictions. Artificial Intelligence, with its capacity to han...

Fetal electrocardiography and artificial intelligence for prenatal detection of congenital heart disease.

Acta obstetricia et gynecologica Scandinavica
INTRODUCTION: This study aims to investigate non-invasive electrocardiography as a method for the detection of congenital heart disease (CHD) with the help of artificial intelligence.

Improving drug discovery with a hybrid deep generative model using reinforcement learning trained on a Bayesian docking approximation.

Journal of computer-aided molecular design
Generative approaches to molecular design are an area of intense study in recent years as a method to generate new pharmaceuticals with desired properties. Often though, these types of efforts are constrained by limited experimental activity data, re...

Metadata and Image Features Co-Aware Personalized Federated Learning for Smart Healthcare.

IEEE journal of biomedical and health informatics
Recently, artificial intelligence has been widely used in intelligent disease diagnosis and has achieved great success. However, most of the works mainly rely on the extraction of image features but ignore the use of clinical text information of pati...

Remote Blood Oxygen Estimation From Videos Using Neural Networks.

IEEE journal of biomedical and health informatics
Peripheral blood oxygen saturation (SpO ) is an essential indicator of respiratory functionality and received increasing attention during the COVID-19 pandemic. Clinical findings show that COVID-19 patients can have significantly low SpO before any ...

Physiologically-Informed Gaussian Processes for Interpretable Modelling of Psycho-Physiological States.

IEEE journal of biomedical and health informatics
The widespread popularity of Machine Learning (ML) models in healthcare solutions has increased the demand for their interpretability and accountability. In this paper, we propose the Physiologically-Informed Gaussian Process (PhGP) classification mo...