Time Series Classification and Extrinsic Regression are important and challenging machine
learning tasks. Deep learning has revolutionized natural language processing and computer …

With the gradual deepening of the research and governance of air pollution, chemical
transport models (CTMs), especially the third-generation CTMs based on the" 1 atm" theory …

Air pollution concentrations in Ho Chi Minh City (HCMC) have been found to surpass the
WHO standard, which has become a very serious problem affecting human health and the …

Since air pollution negatively affects human health and causes serious diseases, accurate
air pollution prediction is essential regarding environmental sustainability. Although …

Many studies use machine learning to predict atmospheric pollutant levels, prioritizing
accuracy over interpretability. This systematic review will focus on reviewing studies that …

Smart cities are urban areas that utilize digital solutions to enhance the efficiency of
conventional networks and services for sustainable growth, optimized resource …

Recent studies have demonstrated that the Ukraine–Russia war has incurred evident
changes to anthropogenic activities in the Kiev metropolis. Hence, this work employs …

We explore the influence and advantages of integrating chaotic systems with deep learning
for time series forecasting in this paper. It proposes a novel deep learning method based on …

In today's urban environments, accurately measuring and forecasting air pollution is crucial
for combating the effects of pollution. Machine learning (ML) is now a go-to method for …

For identifying the optimal model for real-time conflict prediction, there is a necessity for
proposing a quantitative analysis approach that adaptively selects the optimal prediction …