In the present scenario, air quality prediction (AQP) is a complex task due to high variability, volatility, and dynamic nature in space and time of particulates and pollutants. Recently, several nations have had poor air quality due to the high emission of particulate matter (PM_2.5) that affects human health conditions, especially in urban areas. In this research, a new optimization-based regression model was implemented for effective forecasting of air pollution. Firstly, the input data were acquired from a real-time Beijing PM_2.5 dataset recorded from 1 January 2010 to 31 December 2014. Additionally, the newer real-time dataset was recorded from 2016 to 2022 for four Indian cities: Cochin, Hyderabad, Chennai, and Bangalore. Then, data normalization was accomplished using the Min-Max normalization technique, along with correlation analysis for selecting highly correlated variables (wind direction, temperature, dew point, wind speed, and historical PM_2.5). Next, the important features from the highly correlated variables were selected by implementing an optimization algorithm named reinforced swarm optimization (RSO). Further, the selected optimal features were given to the bi-directional gated recurrent unit (Bi-GRU) model for effective AQP. The extensive numerical analysis shows that the proposed model obtained a mean absolute error ($MAE$) of 9.11 and 0.19 and a mean square error ($MSE$) of 2.82 and 0.26 on the Beijing PM_2.5 dataset and a real-time dataset. On both datasets, the error rate of the proposed model was minimal compared to other regression models.

Recently, the Hyper Spectral Image (HSI) classification relies as a well-established study area in the topic related to Remote Sensing (RS). The classification of HSI is used in various applications such as military, agriculture, mineral mapping and so on. However, the existing techniques have underlying difficulties related to curse of dimensionalities and the lack of training data. To overcome these issues, this research using Butterfly Optimization Integrated Snake Optimization (BOISO) optimized U-Net for segmenting HSI. After data acquisition from Indian Pines dataset, the pre-processing is done using Weiner filter. Next to this, the proposed BOISO optimized U-Net is used to segment the pre-processed HSI. The spatial feature weight map is obtained based on spatial information path and corresponding features are obtained by multiplying semantic feature map. Finally, the feature map is linked with the spatial location to obtain final feature map which is optimized using the proposed BOISO. Then, the classification using hybrid classification approach is based on geometric mean of improved Deep Belief network (DBN) and Quantum Neural Network (QNN). The results exhibit that the BOISO achieves a sensitivity of 0.939 which is higher than SO, BOA, BES and PRO, with respective sensitivities of 0.877, 0.893, 0.847 and 0.857.