As part of, and in effect of the 4th industrial revolution, development in many sectors has been moving towards automation. More and more, applications and systems require less human intervention and have grown to be capable of making unassisted decisions. These applications or systems have been labeled intelligent. This “intelligence” is the result of their ability to “learn”, using appropriate machine learning (ML) techniques. Convolutional Neural Networks (CNNs) are applications of these machine learning techniques. Pooling is a major component of Convolutional Neural Networks (CNNs), having considerable influential on the learning process of classification models, and CNNs in general. pooling functions in reducing the spatial dimensions of feature maps and, consequently, the number of parameters to be learnt by the CNN. This reduces the computational cost and prevents overfitting. Many pooling techniques were proposed in the literature, such as Max Pooling (MaxPool), Average Pooling (AvgPool), Stochastic Pooling, and Spectral Pooling. Each of these techniques has its advantages and limitations. Depending on the type of data, and structure of the CNN, some pooling methods are better suited to certain situations when compared to their counterparts. Some standard or popular pooling methods are the Maximum Pooling (MaxPool) and the Average Pooling (AvgPool). MaxPool is very efficient in extracting the most important features within a receptive field and is robust to minor translations and rotation of the input image, while AvgPool tends to preserve the background information and is robust to outliers. However, these methods present some limitations. MaxPool, when choosing the maximum value, disregards the other nomaximum values, while AvgPool considers the whole of the input in equal importance. This encouraged the development of more pooling methods to satisfy specific CNN needs. This thesis proposes the addition of a parameter to the pooling process through a pooling kernel. The proposed pooling methods are adaptive versions of the two standard or popular existing methods. This investigation, and throughout this thesis, will answer how this addition affects the pooling process and the subsequent CNN model performance. Through this thesis, experimentation, and the produced results, it is found that adding parameters to the pooling process produces better results than the standard aforementioned pooling methods.