Recognition and classification tasks have become increasingly popular for automation in several fields. These tasks are commonly carried out using convolutional neural networks (CNNs) and feedforward neural networks (FFNNs). Their adaptability and feature extraction lead to high-accuracy image recognition results; despite being computationally expensive. However, high computational demands, large volumes of labeled data, and storage requirements all hinder CNN efficiency. In this paper, we first