Machine learning approaches are increasingly used to extract patterns and insights from the ever-increasing stream of geospatial data, but current approaches may not be optimal when system behaviour is dominated by spatial or temporal context. Here, ...
BACKGROUND: Traditional statistical approaches to prediction of outcomes have drawbacks when applied to large clinical databases. It is hypothesized that machine learning methodologies might overcome these limitations by considering higher-dimensiona...
Computational intelligence and neuroscience
Feb 5, 2019
Stock index prediction is considered as a difficult task in the past decade. In order to predict stock index accurately, this paper proposes a novel prediction method based on S-system model. Restricted gene expression programming (RGEP) is proposed ...
Neural networks : the official journal of the International Neural Network Society
Feb 1, 2019
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 (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...
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...
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...
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...
International journal of environmental research and public health
Jan 25, 2019
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...
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...
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