Experimental Evaluation of Systematic Animal Classification System using Advanced Deep Learning Principle

In this paper, a system for automatically identifying images of animals are offered. To begin, the background of the image is removed by segmentation using an exponential incision based approach. After an animal picture has been segmented, it is divided into a number of blocks such that the colour texture moments may be recovered independently from each block. The threats that animals face now are far higher than they were in the past. In this study, we offer a suite of algorithms for animal categorization and detection across a range of visual modalities, with potential applications in the realm of animal conservation. In this paper, we propose a new animal classification principle, the Semantic Learning Principle for Animal Classification (SLPAC), and compare its performance to that of a more conventional deep learning algorithm, the Convolutional Neural Network (CNN). The suggested technique has broad applications in the field of object identification and picture categorization. In addition, two statistical studies are provided to provide light on the animal categorization system’s prediction behaviour. These tests probe the indicators that cause the system to settle on a certain option. Statistical hypothesis testing shows that the input image including an animal has a substantial impact on the system’s final verdict. The resultant part displays the performance of the suggested technique in clear manner with graphical demonstrations.