This paper aims to describe and evaluate the proposed calibration model based on a neural network for post-processing of two essential meteorological parameters, namely near-surface air temperature (2 m) and 24 h accumulated precipitation. The main i...
Short-term forecasting of electric energy consumption has become a critical issue for companies selling and buying electricity because of the fluctuating and rising trend of its price. Forecasting tools based on Artificial Intelligence have proved to...
Computational intelligence and neuroscience
May 10, 2022
To study the intelligent and efficient stock portfolio in China's financial market, based on the relevant theories such as deep learning (DL) neural network (NN) and stock portfolio, this study selects 111 stable stocks from the constituent stocks of...
Recently, there has been an increasing need for new applications and services such as big data, blockchains, vehicle-to-everything (V2X), the Internet of things, 5G, and beyond. Therefore, to maintain quality of service (QoS), accurate network resour...
Neural networks : the official journal of the International Neural Network Society
May 7, 2022
Multivariate time series forecasting remains a challenging task because of its nonlinear, non-stationary, high-dimensional, and spatial-temporal characteristics, along with the dependence between variables. To address this limitation, we propose a no...
IEEE journal of biomedical and health informatics
May 5, 2022
BACKGROUND: Artificial Intelligence (AI) has proven to be an invaluable asset in the healthcare domain, where massive amounts of data are produced. Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous chronic condition with multiscale mani...
The recent release of large-scale healthcare datasets has greatly propelled the research of data-driven deep learning models for healthcare applications. However, due to the nature of such deep black-boxed models, concerns about interpretability, fai...
To learn spatiotemporal representations and anomaly predictions from geophysical data, we propose STANet, a spatiotemporal neural network with a trainable attention mechanism, and apply it to El Niño predictions for long-lead forecasts. The STANet ma...
Computational intelligence and neuroscience
Apr 30, 2022
Scientific and accurate prediction of high-tech industries is of great practical significance for government departments to grasp the future economic operation and formulate development strategies. In this paper, aiming at some shortcomings of neural...
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