Abstract Machine Learning (ML) and Deep Learning (DL) methods have contributed to the
progression of models in the different aspects of planning, prediction, and uncertainty …

Background The environment has been significantly impacted by rapid urbanization, leading
to a need for changes in climate change and pollution indicators. The 4IR offers a potential …

Deep learning (DL) and machine learning (ML) methods have recently contributed to the
advancement of models in the various aspects of prediction, planning, and uncertainty …

The number of people moving to cities as reported by the Urban Population website for the
year 2019 was 55.714%[‎ 1]. As the UN revealed, it is expected that 68% of the world's …

Over the past few decades, due to human activities, industrialization, and urbanization, air
pollution has become a life-threatening factor in many countries around the world. Among …

Purpose The purpose of this paper is to propose a distributed deep learning architecture for
smart cities in big data systems. Design/methodology/approach We have proposed an …

In smart cities, air pollution has detrimental impacts on human physical health and the
quality of living environment. Therefore, correctly predicting air quality plays an important …

The development in our urban cities has increased significant risks with everyday lives, like
traffic congestion, pollution of the atmosphere, energy use, and public safety among others …

Sustainable and inventive city design is becoming more and more dependent on the use of
cutting-edge technology as smart cities develop further. Energy efficiency optimization in …

Rapid growth in urbanization and industrialization leads to an increase in air pollution and
poor air quality. Because of its adverse effects on the natural environment and human …