Convolutional neural networks (CNN) is a contemporary technique for computer vision applications, where pooling implies as an integral part of the deep CNN. Besides, pooling provides the ability to learn invariant features and also acts as a regularizer to further reduce the problem of overfitting. Additionally, the pooling techniques significantly reduce the computational cost and training time of networks which are equally important to consider. Here, the performances of pooling strategies on different datasets are analyzed and discussed qualitatively. This study presents a detailed review of the conventional and the latest strategies which would help in appraising the readers with the upsides and downsides of each strategy. Also, we have identified four fundamental factors namely network architecture, activation function, overlapping and regularization approaches which immensely affect the performance of pooling operations. It is believed that this work would help in extending the scope of understanding the significance of CNN along with pooling regimes for solving computer vision problems.

An assessment and prediction of PM_2.5 for a port city of eastern peninsular India is presented. Fifteen machine learning (ML) regression models were trained, tested and implemented to predict the PM_2.5 concentration. The predicting ability of regression models was validated using air pollutants and meteorological parameters as input variables collected from sites located at Visakhapatnam, a port city on the eastern side of peninsular India, for the assessment period 2018–2019. Highly correlated air pollutants and meteorological parameters with PM_2.5 concentration were evaluated and presented during the period under study. It was found that the CatBoost regression model outperformed all other employed regression models in predicting PM_2.5 concentration with an R² score (coefficient of determination) of 0.81, median absolute error (MedAE) of 6.95 µg/m³, mean absolute percentage error (MAPE) of 0.29, root mean square error (RMSE) of 11.42 µg/m³ and mean absolute error (MAE) of 9.07 µg/m³. High PM_2.5 concentration prediction results in contrast to Indian standards were also presented. In depth seasonal assessments of PM_2.5 concentration were presented, to show variance in PM_2.5 concentration during dominant seasons.