AN OVERVIEW OF DEEP LEARNING ALGORITHMS FOR ANIMAL DETECTION
DOI:
https://doi.org/10.65009/4gx6zr96Keywords:
SVM, DNN, CNN, Resnet50, PCA, Machine learning, etc,,Abstract
—Support Vector Machine (SVM), Principal Component Analysis (PCA), Linear Discriminant
Analysis (LDA), and Local Binary Pattern Histogram (LBPH) are some of the additional methods that
CNN takes into consideration when calculating the greatest accuracy. The Convolution Neural Network
(CNN) is a model that suggests the classification of the input image of the animal. We are in the process
of developing a database of wild animals; our database system is comprised of pictures of each category.
The results of this experiment demonstrate that overall results were produced in order to check the impact
that various processing images have on the beneficial impact that their output has on other processes.
Deep Convolutional Neural Networks, often known as DCNNs, are a way of learning picture features that
is both efficient and selective. This technology has been extensively researched and widely used in the
field of computer vision and pattern recognition. An investigation into the application of machine
learning strategies to animal photographs is presented in this work. The purpose of this research is to
improve the accuracy of scene classification.
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