Study of Scalable Deep Neural Network for Wildlife Animal Recognition and Identification
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Study of Scalable Deep Neural Network for Wildlife Animal Recognition and Identification

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– Study of Scalable Deep Neural Network for Wildlife Animal Recognition and Identification –

Download Study of Scalable Deep Neural Network for Wildlife Animal Recognition and Identification. Animal Science students who are writing their projects can get this material to aid their research work.

Abstract

Recently, deep learning techniques have been used significantly for large scale image classification targeting wildlife prediction. This research adopted a deep convolutional neural network (CNN) and proposed a deep scalable CNN.

Our research essentially modifies the network layers (scalability) dynamically in a multitasking system and enables real-time operations with minimum performance loss.

It suggests a straightforward technique to access the performance gains of the network while enlarging the network layers.

Introduction

The task of identifying and recognition of animals from photos has long been standing as there is no unique method that provides a robust and efficient solution to all situations.

Several researchers used long-standing traditional approaches for its implementation with the problem still hanging in limbo as the task hugely involve collecting a large volume of images which predominantly is conducted manually

with possibly images having an imperfect quality which sometimes affect the speed of classification, accuracy even for domain experts.

More so, processing these image sets is time-consuming, effort demanding, and comes at a very high cost as it is an overwhelming amount of data that is collected.

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