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Research Article
Deep Learning Model or Identifying Snakes Using Snakes Bite Marks
Dhilipkumar G1
Kavinraj S P2
Vaanathi S3
12CSBS, Bannari Amman Institute of Technology, Tamilnadu, India. 3 Artificial Intelligence and Data Science, Bannari Amman Institute of Technology, Tamilnadu, India.
Published Online: March-April 2023
Pages: 142-146
Cite this article
No DOIReferences
1. Progga, N.I., Rezoana, N., Hossain, M.S., Islam, R.U., Andersson, K. (2021). A CNN Based Model for Venomous and Nonvenomous Snake Classification. In: Mahmud, M., Kaiser, M.S., Kasabov, N., Iftekharuddin, K., Zhong, N. (eds) Applied
Intelligence and Informatics. AII 2021. Communications in Computer and Information Science, vol 1435. Springer, Cham.
2. Rajabizadeh, M., Rezghi, M. A comparative study on image-based snake identification using machine learning. Sci Rep 11, 19142
(2021). https://doi.org/10.1038/s41598-021-96031-1
3. Vivek Chauhan and Suman Thakur, “The North– South divide in snake bite envenomation in India,” Journal of Emergencies
Trauma and Shock, Dec2016.
4. Miraemiliana Murat, Siow-Wee Chang et al, “Automated classification of tropical shrub species: a hybrid of leaf shape and
machinelearning approach”, PeerJ, Sept 2017.
5. A Nishioka P. Silveria et al, “Bite marks are useful for the differential diagnosis of snakebite inBrazil”, Journal of Wilderness
Medicine, 1995.
6. Kumar V., Maheshwari R, Verma H. K., “Toxicityand symptomatic identification of species involved in snakebites in the Indian
subcontinent”, Journal of Venomous Animals and Toxins including Tropical Diseases, 2006.
7. Kai Zhang, Wangmeng Zuo, Yunjin Chen et al “Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image
Denoising”, IEEE Transactions on Image Processing, Volume 26, Issue 7, 2017.
8. Jiang Wang, Yi Yang et al, “CNN-RNN: A Unified Framework for MultiLabel Image Classification”, The IEEE Conference on
Computer Vision and Pattern Recognition (CVPR), 2016,
9. R. David G. Theakston and Gavin D. Laing, “Diagnosis of Snakebite and the Importance of Immunological Tests in Venom
Research”, Toxins Journal, May 2014.
10. Mohammad Sadegh Norouzzadeh, Anh Nguyen et al “Automatically identifying, counting, and describing wild animals in cameratrap images with deep learning”, PNAS June 19, 2018 115 (25).
11. Nguyen, Hung, et al. "Animal recognition and identification with deep convolutional neural networks for automated wildlife
monitoring." 2017IEEE international conference on data science and advanced Analytics (DSAA). IEEE, 2017.
12. Aakif A, Khan MF. Automatic classification of plants based on their leaves. Biosystems Engineering.2015;139:66–75.doi:
10.1016/j.biosystemseng.2015.08.003. - DOI
13. Ahmed N, Khan UG, Asif S. An automatic leaf based plant identification. The 5th international multidisciplinary conference;
Lahore. 2016. pp. 427–430.
14. Alpaydin E. Introduction to machine learning. MIT press; Cambridge: 2014. Breiman L. Random forests. Machine Learning.
2001;45(1):5–32.
15. Chaki J, Parekh R, Bhattacharya S. Plant leaf recognition using texture and shape features with neural classifiers. Pattern
Recognition Letters. 2015;58:61–68. doi: 10.1016/j.patrec.2015.02.010.
Intelligence and Informatics. AII 2021. Communications in Computer and Information Science, vol 1435. Springer, Cham.
2. Rajabizadeh, M., Rezghi, M. A comparative study on image-based snake identification using machine learning. Sci Rep 11, 19142
(2021). https://doi.org/10.1038/s41598-021-96031-1
3. Vivek Chauhan and Suman Thakur, “The North– South divide in snake bite envenomation in India,” Journal of Emergencies
Trauma and Shock, Dec2016.
4. Miraemiliana Murat, Siow-Wee Chang et al, “Automated classification of tropical shrub species: a hybrid of leaf shape and
machinelearning approach”, PeerJ, Sept 2017.
5. A Nishioka P. Silveria et al, “Bite marks are useful for the differential diagnosis of snakebite inBrazil”, Journal of Wilderness
Medicine, 1995.
6. Kumar V., Maheshwari R, Verma H. K., “Toxicityand symptomatic identification of species involved in snakebites in the Indian
subcontinent”, Journal of Venomous Animals and Toxins including Tropical Diseases, 2006.
7. Kai Zhang, Wangmeng Zuo, Yunjin Chen et al “Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image
Denoising”, IEEE Transactions on Image Processing, Volume 26, Issue 7, 2017.
8. Jiang Wang, Yi Yang et al, “CNN-RNN: A Unified Framework for MultiLabel Image Classification”, The IEEE Conference on
Computer Vision and Pattern Recognition (CVPR), 2016,
9. R. David G. Theakston and Gavin D. Laing, “Diagnosis of Snakebite and the Importance of Immunological Tests in Venom
Research”, Toxins Journal, May 2014.
10. Mohammad Sadegh Norouzzadeh, Anh Nguyen et al “Automatically identifying, counting, and describing wild animals in cameratrap images with deep learning”, PNAS June 19, 2018 115 (25).
11. Nguyen, Hung, et al. "Animal recognition and identification with deep convolutional neural networks for automated wildlife
monitoring." 2017IEEE international conference on data science and advanced Analytics (DSAA). IEEE, 2017.
12. Aakif A, Khan MF. Automatic classification of plants based on their leaves. Biosystems Engineering.2015;139:66–75.doi:
10.1016/j.biosystemseng.2015.08.003. - DOI
13. Ahmed N, Khan UG, Asif S. An automatic leaf based plant identification. The 5th international multidisciplinary conference;
Lahore. 2016. pp. 427–430.
14. Alpaydin E. Introduction to machine learning. MIT press; Cambridge: 2014. Breiman L. Random forests. Machine Learning.
2001;45(1):5–32.
15. Chaki J, Parekh R, Bhattacharya S. Plant leaf recognition using texture and shape features with neural classifiers. Pattern
Recognition Letters. 2015;58:61–68. doi: 10.1016/j.patrec.2015.02.010.
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