1Department of Information Technology, JSS Academy of Technical Education, Noida-201 301, Uttar Pradesh, India.
2Department of Operations and Information Technology, ICFAI Business School, The ICFAI Foundation for Higher Education (Deemed-to-be-University u/s 3 of the UGC Act, 1956), Hyderabad-501 203, Telangana, India.
3Department of Public Health, College of Nursing and Health Sciences, Jazan University, Jazan-45142, Kingdom of Saudi Arabia.
4Department of Biotechnology, KLE Technological University, Hubballi-580 031, Karnataka, India.
5Vishwakarma Institute of Technology, Pune-411 001, Maharashtra, India.
Corresponding Author: J. Prince Vijai, Department of Operations and Information Technology, ICFAI Business School, The ICFAI Foundation for Higher Education (Deemed-to-be-University u/s 3 of the UGC Act, 1956), Hyderabad-501 203, Telangana, India. Email: jpvijai@ibsindia.org
Avian influenza viruses (AIVs) are one of the most important viral families on the planet, having considerable health implications for humans, domestic animals and wildlife. Wild birds serve as natural reservoirs for such infections and regular observation of wild bird groups gives crucial data regarding viral development, which is used to build risk evaluation and countermeasures. However, active monitoring systems use a lot of resources, thus improving them for greater efficiency is critical.
Machine learning (ML), a subset of artificial intelligence, offers statistical learning processes that may be used in disease monitoring systems to acquire new insights. In this study, the researchers focus on the application of gradient boosted trees, a type of machine learning, to estimate the chance of identifying avian influenza viruses from wild bird samples collected during monitoring efforts in the United States.
The study highlights the emergence of new technology, specifically Artificial Intelligence, in the diagnosis of Avian influenza viruses (AIVs) in avian species.
Artificial intelligence, Avian influenza viruses, Convolutional neural networks, Deep learning, Machine learning, Recurrent neural networks
