Time-series prediction is an important area that inspires numerous research disciplines for
various applications, including air quality databases. Developing a robust and accurate …

Time-series is a sequence of observations that are taken sequentially over time. Modelling a
system that generates a future value from past observations is considered as time-series …

Air quality is a major problem in the world, having severe health implications. Long-term
exposure to poor air quality causes pulmonary and cardiovascular diseases. Several studies …

Developments in deep learning for time-series problems have shown promising results for
data prediction. Particulate Matter equal or smaller than 10 μm (PM 10) have increased …

As air pollution becomes increasingly serious, accurate forecasting of air quality has
become an important issue. Many studies related to machine learning and deep learning …

Air pollution has become a serious problem. Thus, this study formulated a method based on
a multi-input multi-output, hybrid, deep neural network for air quality prediction. In our …

The global environment has become more polluted due to the rapid development of
industrial technology. However, the existing machine learning prediction methods of air …

Time series prediction is a challenging predictive modeling case. It is essential to have a
prediction model that can adapt to dynamic data. Air quality data show a significant …

In order to better predict the high aerosol concentrations associated with air pollution and
climate change, a machine learning model was developed using transfer learning and the …

Time series prediction (forecasting) has become the essential issue in many fields, such as
stock market, supply chain management, speech recognition, traffic problem and etc …