AI Medical Compendium Topic:
Forecasting

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Prediction and identification of discrete-time dynamic nonlinear systems based on adaptive echo state network.

Neural networks : the official journal of the International Neural Network Society
In this paper, a new prediction and identification method based on adaptive echo state network (AESN) is proposed to identify a class of discrete-time dynamic nonlinear systems (DDNS). Firstly, according to the characteristics of input signals, the r...

Artificial intelligence and the radiologist: the future in the Armed Forces Medical Services.

BMJ military health
Artificial intelligence (AI) involves computational networks (neural networks) that simulate human intelligence. The incorporation of AI in radiology will help in dealing with the tedious, repetitive, time-consuming job of detecting relevant findings...

Artificial Intelligence Transforms the Future of Health Care.

The American journal of medicine
Life sciences researchers using artificial intelligence (AI) are under pressure to innovate faster than ever. Large, multilevel, and integrated data sets offer the promise of unlocking novel insights and accelerating breakthroughs. Although more data...

How far have we come? Artificial intelligence for chest radiograph interpretation.

Clinical radiology
Due to recent advances in artificial intelligence, there is renewed interest in automating interpretation of imaging tests. Chest radiographs are particularly interesting due to many factors: relatively inexpensive equipment, importance to public hea...

An ensemble long short-term memory neural network for hourly PM concentration forecasting.

Chemosphere
To protect public health by providing an early warning, PM concentration forecasting is an essential and effective work. In this paper, an ensemble long short-term memory neural network (E-LSTM) is proposed for hourly PM concentration forecasting. Th...

Predicting Future Driving Risk of Crash-Involved Drivers Based on a Systematic Machine Learning Framework.

International journal of environmental research and public health
The objective of this paper is to predict the future driving risk of crash-involved drivers in Kunshan, China. A systematic machine learning framework is proposed to deal with three critical technical issues: 1. defining driving risk; 2. developing r...

Forecasting Pedestrian Movements Using Recurrent Neural Networks: An Application of Crowd Monitoring Data.

Sensors (Basel, Switzerland)
Currently, effective crowd management based on the information provided by crowd monitoring systems is difficult as this information comes in at the moment adverse crowd movements are already occurring. Up to this moment, very little forecasting tech...

Integration of A Deep Learning Classifier with A Random Forest Approach for Predicting Malonylation Sites.

Genomics, proteomics & bioinformatics
As a newly-identified protein post-translational modification, malonylation is involved in a variety of biological functions. Recognizing malonylation sites in substrates represents an initial but crucial step in elucidating the molecular mechanisms ...

Incoming Work-In-Progress Prediction in Semiconductor Fabrication Foundry Using Long Short-Term Memory.

Computational intelligence and neuroscience
Preventive maintenance activities require a tool to be offline for long hour in order to perform the prescribed maintenance activities. Although preventive maintenance is crucial to ensure operational reliability and efficiency of the tool, long hour...

Prediction of fatty liver disease using machine learning algorithms.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Fatty liver disease (FLD) is a common clinical complication; it is associated with high morbidity and mortality. However, an early prediction of FLD patients provides an opportunity to make an appropriate strategy for preven...