AIMC Topic: Bayes Theorem

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Probabilistic real-time natural gas jet fire consequence modeling of offshore platforms by hybrid deep learning approach.

Marine pollution bulletin
Natural gas jet fire induced by igniting blowouts has the potential to cause critical structure damage and great casualties of offshore platforms. Real-time natural gas jet fire plume prediction is essential to support the emergency planning to mitig...

Machine learning and deep learning techniques for breast cancer diagnosis and classification: a comprehensive review of medical imaging studies.

Journal of cancer research and clinical oncology
BACKGROUND: Breast cancer is a major public health concern, and early diagnosis and classification are critical for effective treatment. Machine learning and deep learning techniques have shown great promise in the classification and diagnosis of bre...

DeepCGP: A Deep Learning Method to Compress Genome-Wide Polymorphisms for Predicting Phenotype of Rice.

IEEE/ACM transactions on computational biology and bioinformatics
Genomic selection (GS) is expected to accelerate plant and animal breeding. During the last decade, genome-wide polymorphism data have increased, which has raised concerns about storage cost and computational time. Several individual studies have att...

Issue of Data Imbalance on Low Birthweight Baby Outcomes Prediction and Associated Risk Factors Identification: Establishment of Benchmarking Key Machine Learning Models With Data Rebalancing Strategies.

Journal of medical Internet research
BACKGROUND: Low birthweight (LBW) is a leading cause of neonatal mortality in the United States and a major causative factor of adverse health effects in newborns. Identifying high-risk patients early in prenatal care is crucial to preventing adverse...

The effect of seasonality in predicting the level of crime. A spatial perspective.

PloS one
This paper presents an innovative methodology to study the application of seasonality (the existence of cyclical patterns) to help predict the level of crime. This methodology combines the simplicity of entropy-based metrics that describe temporal pa...

Motor decoding from the posterior parietal cortex using deep neural networks.

Journal of neural engineering
Motor decoding is crucial to translate the neural activity for brain-computer interfaces (BCIs) and provides information on how motor states are encoded in the brain. Deep neural networks (DNNs) are emerging as promising neural decoders. Nevertheless...

Recurrent neural network modeling of multivariate time series and its application in temperature forecasting.

PloS one
Temperature forecasting plays an important role in human production and operational activities. Traditional temperature forecasting mainly relies on numerical forecasting models to operate, which takes a long time and has higher requirements for the ...

Optimal machine learning methods for prediction of high-flow nasal cannula outcomes using image features from electrical impedance tomography.

Computer methods and programs in biomedicine
BACKGROUND: High-flow nasal cannula (HNFC) is able to provide ventilation support for patients with hypoxic respiratory failure. Early prediction of HFNC outcome is warranted, since failure of HFNC might delay intubation and increase mortality rate. ...

Machine learning-based approach for efficient prediction of toxicity of chemical gases using feature selection.

Journal of hazardous materials
Toxic gases can be fatal as they damage many living tissues, especially the nervous and respiratory systems. They can cause permanent damage for many years by harming environmental tissue and living organisms. They can also cause mass deaths when use...

Generalising uncertainty improves accuracy and safety of deep learning analytics applied to oncology.

Scientific reports
Uncertainty estimation is crucial for understanding the reliability of deep learning (DL) predictions, and critical for deploying DL in the clinic. Differences between training and production datasets can lead to incorrect predictions with underestim...