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INTERNATIONAL JOURNAL OF RECENT TRENDS IN TECHNOLOGY AND ENGINEERING (IJRTTE)

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Deep Neural Network for Prediction of Different Categories of Animal

Senthil. R
Assistant Professor, Department of Computer Science and Engineering, SRM Institute of Science & Technology, India.
Prem Narayan Singh
Research Scholar, Department of Computer Science and Engineering, SRM Institute of Science & Technology, India.

Keywords: DNN; Animals detection; Ubuntu OS

Abstract

Certain living creatures are now uncommon to find, and even when they are available, their classification and prognosis remain challenging. From a logical standpoint, species across different environments exhibit variations in size, texture, color, and behavior. Moreover, image-based recognition has proven to be more effective than auditory-based classification for identifying animal species. Additionally, visual perception offers a more interpretable and reliable means of distinguishing between different species.

As a result, this approach utilizes the Caltech-UCSD Birds-200 (CUB-200) dataset for both training and validation. The input images are converted into grayscale and processed using a deep convolutional neural network (DCNN) model. Feature signatures are then generated using data flow graphs, where multiple similarity nodes are identified. High-similarity features are compared against validation data, and a scoring mechanism is produced accordingly. Based on the analysis of the dataset, the system achieves an accuracy ranging between 80% and 90% in species identification. The implementation of the proposed model is carried out using Ubuntu 16.04 and the TensorFlow framework.
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Details
Published
2022-03-19
Pages
1-5
Issue
Vol. 1 No. 1 (2022): IJRTTE - 01 - 01
Section
Articles

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