Deevesh Chaudhary

Abstract Accurate prediction of urban air quality is vital for protecting public health and the environment. The current challenges in air quality prediction models include noisy data, missing data, interactions of pollutants, temporal and spatial variations, external variables, lack of generalization, and real-time prediction. To overcome these challenges, a hybrid bio-inspired model for predicting urban air pollution using deep learning (AQP-SAPINN-HMRFO) is proposed.

Abstract Herbs have long been integral to various health and medicinal benefits. Identifying the correct herb species from thousands of diverse options is a laborious and time-consuming process. To address this issue, an automated computer vision system that reduces the traditional labor-intensive work of herbal species classification is needed.