Home / Current Issue / Paper 1705428
Eco-Smart Elephant Recognition: A Dual Strategy with CNN Classification and SVM Feature Extraction
Subject area: Science,Engineering and Technology · Area of research: Machine Learning , Deep Learning
Abstract
Our research delves into the multifaceted realm of elephant recognition, employing diverse methodologies to address the challenges posed by image classification and feature extraction. We explore Convolutional Neural Networks (CNNs) for image classification, achieving an impressive 87.50% accuracy in discerning elephant subtypes. Furthermore, we investigate Support Vector Machines (SVMs) in conjunction with the VGG16 model for feature extraction, providing an alternative approach with a commendable 76% accuracy. Our project leverages these techniques to distinguish between African, Asian, and Indian elephants, contributing to wildlife conservation efforts. Through extensive experimentation, we showcase the strengths and limitations of each approach, offering valuable insights for researchers and practitioners in the field.
Keywords
Elephant Recognition, CNN, SVM, Wildlife Conservation, Image Classification
References
[1] G. Chen, T. X. Han, Z. He, R. Kays and T. Forrester, "Deep convolutional neural network based species recognition for wild animal monitoring," 2014 IEEE International Conference on Image Processing (ICIP), Paris, France, 2014, pp. 858-862, doi: 10.1109/ICIP.2014.7025172.
[2] P. Somervuo, A. Harma and S. Fagerlund, "Parametric Representations of Bird Sounds for Automatic Species Recognition," in IEEE Transactions on Audio, Speech, and Language Processing, vol. 14, no. 6, pp. 2252-2263, Nov. 2006, doi: 10.1109/TASL.2006.872624.
[3] J. Cai, D. Ee, B. Pham, P. Roe and J. Zhang, "Sensor Network for the Monitoring of Ecosystem: Bird Species Recognition," 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information, Melbourne, VIC, Australia, 2007, pp. 293-298, doi: 10.1109/ISSNIP.2007.4496859.
[4] L. G. Hafemann, L. S. Oliveira and P. Cavalin, "Forest Species Recognition Using Deep Convolutional Neural Networks," 2014 22nd International Conference on Pattern Recognition, Stockholm, Sweden, 2014, pp. 1103-1107, doi: 10.1109/ICPR.2014.199.
[5] Pu R. Broadleaf species recognition with in situ hyperspectral data. International Journal of Remote Sensing. 2009 Jun 10;30(11):2759-79.
[6] M. Kumar, S. Gupta, X. -Z. Gao and A. Singh, "Plant Species Recognition Using Morphological Features and Adaptive Boosting Methodology," in IEEE Access, vol. 7, pp. 163912-163918, 2019, doi: 10.1109/ACCESS.2019.2952176.
[7] I. Gogul and V. S. Kumar, "Flower species recognition system using convolution neural networks and transfer learning," 2017 Fourth International Conference on Signal Processing, Communication and Networking (ICSCN), Chennai, India, 2017, pp. 1-6, doi: 10.1109/ICSCN.2017.8085675.
[8] B. V. Deep and R. Dash, "Underwater Fish Species Recognition Using Deep Learning Techniques," 2019 6th International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India, 2019, pp. 665-669, doi: 10.1109/SPIN.2019.8711657.
How to cite this paper
@article{1705428,
author = {Aman Mishra, Mithilesh Vishwakarma, Nihal Baranwal},
title = {Eco-Smart Elephant Recognition: A Dual Strategy with CNN Classification and SVM Feature Extraction},
journal = {Iconic Research And Engineering Journals},
year = {2024},
volume = {7},
number = {7},
pages = {351-357},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1705428.pdf},
abstract = {Our research delves into the multifaceted realm of elephant recognition, employing diverse methodologies to address the challenges posed by image classification and feature extraction. We explore Convolutional Neural Networks (CNNs) for image classification, achieving an impressive 87.50% accuracy in discerning elephant subtypes. Furthermore, we investigate Support Vector Machines (SVMs) in conjunction with the VGG16 model for feature extraction, providing an alternative approach with a commendable 76% accuracy. Our project leverages these techniques to distinguish between African, Asian, and Indian elephants, contributing to wildlife conservation efforts. Through extensive experimentation, we showcase the strengths and limitations of each approach, offering valuable insights for researchers and practitioners in the field.},
keywords = {Elephant Recognition, CNN, SVM, Wildlife Conservation, Image Classification},
month = {January},
}