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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 16 Issue 3, 2025.
Abstract: Climate change is one of the most talked-about topics of this decade, affecting all economic output sectors, including the economy of cow farming. In many scenarios, exceptionally severe climate change is predicted for the Mediterranean region. As a result, practical measures must be taken to strengthen the sector's resilience, particularly for smallholders involved in the cattle production industry. As a result, technology is required to stop animal disease outbreaks. There are benefits to using automatic methods for detecting animal disease and cellulite. Climate change seriously threatens animal health, which is changing ecosystems, changing weather patterns, and posing new difficulties for animal existence. But this crisis also offers a chance for imagination and cooperation in a changing climate, a comprehensive strategy that includes adaptation and mitigation strategies that can boost resilience and safeguard animal populations. In conclusion, knowledge of climate change and adaptation measures are the main factors driving the rising demand for animal products. Furthermore, we have a variety of adaptation strategies at our disposal to mitigate the effects of climate change, which must be used to limit its further expansion.
Gehad K. Hussien, Mohamed H. Khafagy and Hossam M. Elbehiery, “The Effect of Climate Change on Animal Diseases by Using Image Processing and Deep Learning Techniques” International Journal of Advanced Computer Science and Applications(IJACSA), 16(3), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160360
@article{Hussien2025,
title = {The Effect of Climate Change on Animal Diseases by Using Image Processing and Deep Learning Techniques},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160360},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160360},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {3},
author = {Gehad K. Hussien and Mohamed H. Khafagy and Hossam M. Elbehiery}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.