Amel Ksibi1, Amina Salhi1, Ala Saleh Alluhaidan1,*, Sahar A. El-Rahman2
CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2073-2096, 2023, DOI:10.32604/cmc.2023.032566
Abstract The quality of the air we breathe during the courses of our daily lives has a significant impact on our health and well-being as individuals. Unfortunately, personal air quality measurement remains challenging. In this study, we investigate the use of first-person photos for the prediction of air quality. The main idea is to harness the power of a generalized stacking approach and the importance of haze features extracted from first-person images to create an efficient new stacking model called AirStackNet for air pollution prediction. AirStackNet consists of two layers and four regression models, where the first layer generates meta-data from… More >
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