Minh Thanh Vo¹, Anh H. Vo², Huong Bui³, Tuong Le^4,5,*

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3029-3041, 2023, DOI:10.32604/iasc.2023.034636

Abstract Nowadays, air pollution is a big environmental problem in developing countries. In this problem, particulate matter 2.5 (PM2.5) in the air is an air pollutant. When its concentration in the air is high in developing countries like Vietnam, it will harm everyone’s health. Accurate prediction of PM2.5 concentrations can help to make the correct decision in protecting the health of the citizen. This study develops a hybrid deep learning approach named PM25-CBL model for PM2.5 concentration prediction in Ho Chi Minh City, Vietnam. Firstly, this study analyzes the effects of variables on PM2.5 concentrations in Air Quality HCMC dataset. Only… More >

Anh H. Vo¹, Trang Nguyen², Tuong Le^1,3,*

Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1307-1317, 2020, DOI:10.32604/iasc.2020.013189

Abstract Brent oil price fluctuates continuously causing instability in the economy. Therefore, it is essential to accurately predict the trend of oil prices, as it helps to improve profits for investors and benefits the community at large. Oil prices usually fluctuate over time as a time series and as such several sequence-based models can be used to predict them. Hence, this study proposes an efficient model named BOP-BL based on Bidirectional Long Short-Term Memory (Bi-LSTM) for oil price prediction. The proposed framework consists of two modules as follows: The first module has three Bi-LSTM layers which help learning useful information features… More >