References
A century of world population trends: 1950 to 2050 (pp. 15–24). (2022). United Nations eBooks. https://doi.org/10.18356/9789210014380c006 Ahmed, S. D. (2023). CNN-CNN: Dual convolutional neural network approach for feature selection and attack detection on Internet of Things networks. Sensors, 23(14), 6507. https://doi.org/10.3390/s23146507 Alanazi, J. M. (2025). Detection of skin lesions in dogs using advanced convolutional neural network technology. Indian Journal of Animal Research, 59(12), 2108–2115. https://doi.org/10.18805/IJAR.BF-1820 Ding, L., Zhang, C., Yue, Y., Yao, C., & Hu, Y. (2026). Advances in audio classification and artificial intelligence for respiratory health and welfare monitoring in swine. Biology, 15(2), 177. https://doi.org/10.3390/biology15020177 Ding, L., Zhang, C., Yue, Y., Yao, C., Li, Z., Hu, Y., Yang, B., Ma, W., Yu, L., & Li, Q. (2025). Wearable sensors-based intelligent sensing and application of animal behaviors: A comprehensive review. Sensors, 25(14), 4515. https://doi.org/10.3390/s25144515 Farkas, S., Chakraborty, S., Sim, J.-S., Jang, D.-J., & Kim, H. C. (2019). The design of an automated system for the analysis of the activity and emotional patterns of dogs with wearable sensors using machine learning. Applied Sciences, 9(22), 4938. https://doi.org/10.3390/app9224938 Filipova, A., Stoyanova, Z., & Ivanov, I. (2023). Health status classification for cows using machine learning and data management on AWS Cloud. Animals, 13(20), 3254. https://doi.org/10.3390/ani13203254 Gowthami, D., Aakash, K., Mohammed Arsath, A., & Santhosh Kumar, V. (2023). Smart Animal Health Monitoring System using IoT. International Research Journal of Education and Technology , 05(05), May 2023. https://www.irjweb.com/Smart%20Animal% 20Health%20Monitoring.df Kumar, A., Goel, S., Chaudhary, A., Dutt, S., Mishra, V. K., & Kumar, R. (2025). Artificial Intelligence-Based Wearable Sensing Technologies for the Management of Cancer, Diabetes, and COVID-19. Biosensors, 15(11), 756. doi:10.3390/bios15110756 Kumar, R., Raj, M., V., R., Poorani, R., Vishnu Priya, T., & Reka Raj, R. R. (2022). Internet of Things (IoT Devices)-A Literature Review. International Journal of Current Engineering and Technology, 12(03). https://doi.org/10.14741/ijcet/v.12.3.3 Li, S., Wang, Z., Visser, L. C., Wisner, E. R., & Cheng, H. (2020). Pilot study: Application of artificial intelligence for detecting left atrial enlargement on canine thoracic radiographs. Veterinary Radiology & Ultrasound, 61(6), 611–618. https://doi.org/10.1111/vru.12901. Liu, H., Wang, X., Tana, Xie, T., Hurichabilige, Z., Zhen, Q., & Li, W. (2025). Predicting winter ammonia and methane emissions from a naturally ventilated dairy barn in a cold region using an adaptive neural fuzzy inference system. Agriculture, 15(14), 1560. https://doi.org/10.3390/agriculture15141560 Manzoor, S., Syed, Z., & Abubabakar, M. (2023). Global Perspectives of Intensive Animal Farming and Its Applications. IntechOpen. https://doi.org/10.5772/intechopen.112271 Meng, H., Zhang, L., Yang, F., Hai, L., Wei, Y., Zhu, L., & Zhang, J. (2025). Livestock Biometrics Identification Using Computer Vision Approaches: A Review. Agriculture, 15(1), 102. https://doi.org/10.3390/agriculture15010102 Navarro, E., Costa, N., & Pereira, A. (2020). A systematic review of IoT solutions for smart farming. Sensors, 20(15), 4231. https://doi.org/10.3390/s20154231. Nayeri, S., Sargolzaei, M., & Tulpan, D. (2019). A review of traditional and machine learning methods applied to animal breeding. Animal Health Research Reviews, 20(1), 31–46. https://doi.org/10.1017/S1466252319000148 Sahin, M., & Yilmaz, M. (2025). A systematic literature review of wearable sensor technologies used in poultry research. Computers and Electronics in Agriculture, 239, 111030. https://doi.org/10.1016/j.compag.2025.111030 Shirani Shamsabadi, J., Ansari Mahyari, S. & Ghaderi-Zefrehei, M. Adaptive neuro-fuzzy inference systems for improved mastitis classification and diagnosis. Sci Rep 15, 20456 (2025). https://doi.org/10.1038/s41598-025-03008-5 Turyasingura, B., Ayiga, N., Tumwesigye, W., & Hegarty, J. P. (2023). Climate smart agriculture for sustainable agriculture nexus: A tool for transforming food systems. Turkish Journal of Agriculture - Food Science and Technology, 11(6), 1195–1199. https://doi.org/10.24925/turjaf.v11i6.1195-1199.5591 Yamsakul, P., Yano, T., Junchum, K., Anukool, W., & Kittiwan, N. (2025). Machine Learning- Based Detection of Pig Coughs and Their Association with Respiratory Diseases in Fattening Pigs. Veterinary Sciences, 12(9), 818. https://doi.org/10.3390/vetsci12090818 Yin, B., Tan, G., Muhammad, R., Liu, J., & Bi, J. (2025). AI-powered innovations in food safety from farm to fork. Foods, 14(11), 1973. https://doi.org/10.3390/foods14111973 Zhang, D., Zhang, M., Sun, Y., Meng, W., Li, Z., Wang, C., & Luo, H. (2025). Rapid and accurate classification of retinal OCT diseases using reparameterized convolutional networks. Biomedical Signal Processing and Control, 83, Article 108167. https://doi.org/10.1016/j.bspc.2025.108167 Zhang, Y., Li, J., Wu, X., & Chen, L. (2025). An ensemble of deep learning frameworks applied for pig cough recognition in a complex piggery environment. Computers and Electronics in Agriculture, 237, 110755. https://doi.org/10.1016/j.compag.2025.110755
