AIMC Topic: Oxygen
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Cloud-based neuro-fuzzy hydro-climatic model for water quality assessment under uncertainty and sensitivity.
Environmental science and pollution research international
Apr 29, 2022
River water quality is a function of various bio-physicochemical parameters which can be aggregated for calculating the Water Quality Index (WQI). However, it is challenging to model the nonlinearity and uncertain behavior of these parameters. When d...
The Effects of Rso2 and PI Monitoring Images on the Treatment of Premature Infants Based on Deep Learning.
Computational and mathematical methods in medicine
Feb 22, 2022
In recent years, due to the combined effects of individual behavior, psychological factors, environmental exposure, medical conditions, biological factors, etc., the incidence of preterm birth has gradually increased, so the incidence of various comp...
LSTM-TCN: dissolved oxygen prediction in aquaculture, based on combined model of long short-term memory network and temporal convolutional network.
Environmental science and pollution research international
Feb 1, 2022
Dissolved oxygen (DO) is an important water quality monitoring parameter of great significance in aquaculture. Accurate prediction of dissolved oxygen can help farmers to take necessary measures in advance to ensure the healthy growth of cultured spe...
Intelligent Monitoring of Care Status for COPD Patients Based on Deep Learning.
Contrast media & molecular imaging
Nov 22, 2021
To discuss the application method and effect of COPD patients in deep learning in intelligent monitoring, two groups were used under a reasonable selection of antibiotics specifically including reasonable and effective oxygen administration, atomizat...
An efficient strategy for predicting river dissolved oxygen concentration: application of deep recurrent neural network model.
Environmental monitoring and assessment
Nov 13, 2021
Dissolved oxygen (DO) concentration in water is one of the key parameters for assessing river water quality. Artificial intelligence (AI) methods have previously proved to be accurate tools for DO concentration prediction. This study presents the imp...
QQ-NET - using deep learning to solve quantitative susceptibility mapping and quantitative blood oxygen level dependent magnitude (QSM+qBOLD or QQ) based oxygen extraction fraction (OEF) mapping.
Magnetic resonance in medicine
Oct 31, 2021
PURPOSE: To improve accuracy and speed of quantitative susceptibility mapping plus quantitative blood oxygen level-dependent magnitude (QSM+qBOLD or QQ) -based oxygen extraction fraction (OEF) mapping using a deep neural network (QQ-NET).
A machine-learning parsimonious multivariable predictive model of mortality risk in patients with Covid-19.
Scientific reports
Oct 27, 2021
The COVID-19 pandemic is impressively challenging the healthcare system. Several prognostic models have been validated but few of them are implemented in daily practice. The objective of the study was to validate a machine-learning risk prediction mo...
Can Deep Learning-Based Volumetric Analysis Predict Oxygen Demand Increase in Patients with COVID-19 Pneumonia?
Medicina (Kaunas, Lithuania)
Oct 22, 2021
: This study aimed to investigate whether predictive indicators for the deterioration of respiratory status can be derived from the deep learning data analysis of initial chest computed tomography (CT) scans of patients with coronavirus disease 2019 ...
eARDS: A multi-center validation of an interpretable machine learning algorithm of early onset Acute Respiratory Distress Syndrome (ARDS) among critically ill adults with COVID-19.
PloS one
Sep 24, 2021
We present an interpretable machine learning algorithm called 'eARDS' for predicting ARDS in an ICU population comprising COVID-19 patients, up to 12-hours before satisfying the Berlin clinical criteria. The analysis was conducted on data collected f...
Machine learning for manually-measured water quality prediction in fish farming.
PloS one
Aug 18, 2021
Monitoring variables such as dissolved oxygen, pH, and pond temperature is a key aspect of high-quality fish farming. Machine learning (ML) techniques have been proposed to model the dynamics of such variables to improve the fish farmer's decision-ma...
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