AIMC Topic: Radon

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Edaphic and meteorological parameters as determinants of radon exhalation and its environmental implication in Peruvian agroecosystems.

Scientific reports Jul 1, 2025
Radon exhalation is a natural process by which atoms of the radioactive gas radon diffuse in the soil and then exhale to an indoor and/or outdoor environment. High radon concentration levels, possibly from high radon exhalation rate levels, can gener...
Agriculture Peru Neural Networks, Computer Soil Air Pollutants, Radioactive Radon Soil Pollutants, Radioactive Monte Carlo Method Humans Radiation Monitoring
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Neural architecture search with Deep Radon Prior for sparse-view CT image reconstruction.

Medical physics Feb 10, 2025
BACKGROUND: Sparse-view computed tomography (CT) reduces radiation exposure but suffers from severe artifacts caused by insufficient sampling and data scarcity, which compromise image fidelity. Recent advancements in deep learning (DL)-based methods ...
Tomography, X-Ray Computed Humans Radon Deep Learning Neural Networks, Computer Image Processing, Computer-Assisted
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Identifying predictors of spatiotemporal variations in residential radon concentrations across North Carolina using machine learning analytics.

Environmental pollution (Barking, Essex : 1987) Jan 4, 2025
Radon is a naturally occurring radioactive gas derived from the decay of uranium in the Earth's crust. Radon exposure is the leading cause of lung cancer among non-smokers in the US. Radon infiltrates homes through soil and building foundations. This...
Machine Learning North Carolina Housing Radiation Monitoring Spatio-Temporal Analysis Air Pollution, Indoor Air Pollutants, Radioactive Radon
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Development of a High-Resolution Indoor Radon Map Using a New Machine Learning-Based Probabilistic Model and German Radon Survey Data.

Environmental health perspectives Sep 18, 2024
BACKGROUND: Radon is a carcinogenic, radioactive gas that can accumulate indoors and is undetected by human senses. Therefore, accurate knowledge of indoor radon concentration is crucial for assessing radon-related health effects or identifying radon...
Radon Radiation Monitoring Air Pollutants, Radioactive Machine Learning Models, Statistical Germany Housing Air Pollution, Indoor Humans
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Imputation by feature importance (IBFI): A methodology to envelop machine learning method for imputing missing patterns in time series data.

PloS one Jan 13, 2022
A new methodology, imputation by feature importance (IBFI), is studied that can be applied to any machine learning method to efficiently fill in any missing or irregularly sampled data. It applies to data missing completely at random (MCAR), missing ...
Radon Machine Learning Time Factors Research Design Pakistan Soil Algorithms
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Radon potential mapping in Jangsu-gun, South Korea using probabilistic and deep learning algorithms.

Environmental pollution (Barking, Essex : 1987) Oct 18, 2021
The adverse health effects associated with the inhalation and ingestion of naturally occurring radon gas produced during the uranium decay chain mean that there is a need to identify high-risk areas. This study detected radon-prone areas using a geog...
Algorithms Radon Uranium Deep Learning Air Pollutants, Radioactive Radiation Monitoring
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Radon Inversion via Deep Learning.

IEEE transactions on medical imaging Jan 6, 2020
The Radon transform is widely used in physical and life sciences, and one of its major applications is in medical X-ray computed tomography (CT), which is significantly important in disease screening and diagnosis. In this paper, we propose a novel r...
Tomography, X-Ray Computed Deep Learning Radon Humans Image Processing, Computer-Assisted Neural Networks, Computer
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Estimation of radon flux spatial distribution in Rize, Turkey by the artificial neural networks method.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine Jun 6, 2019
In this study, average radon flux distribution in the Rize province (Turkey) was estimated by the artificial neural networks (ANN) method. For this purpose, terrestrial gamma dose rate (TGDR), which is defined as an important proxy in determining rad...
Soil Pollutants, Radioactive Spectrometry, Gamma Neural Networks, Computer Turkey Radon
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Automated anomalous behaviour detection in soil radon gas prior to earthquakes using computational intelligence techniques.

Journal of environmental radioactivity Mar 9, 2019
In this article, three computational intelligence (CI) models were developed to automatically detect anomalous behaviour in soil radon gas (Rn) time series data. Data were obtained at a fault line and analysed using three machine learning techniques ...
Radiation Monitoring Radon Artificial Intelligence Soil Soil Pollutants, Radioactive Earthquakes
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Bagged neural network model for prediction of the mean indoor radon concentration in the municipalities in Czech Republic.

Journal of environmental radioactivity Jul 18, 2016
The purpose of the study is to determine radon-prone areas in the Czech Republic based on the measurements of indoor radon concentration and independent predictors (rock type and permeability of the bedrock, gamma dose rate, GPS coordinates and the a...
Radiation Monitoring Air Pollution, Radioactive Air Pollution, Indoor Neural Networks, Computer Air Pollutants, Radioactive Radon Models, Theoretical Czech Republic
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