International Journal of Marine Science, 2026, Vol.16, No.4, 243-254 http://www.aquapublisher.com/index.php/ijms 254 Udayakumar R., Alhassan A., Abbas Z., Jayanthi K.B., Husain S.O., Shavkidinova D., Kadirov I., and Shodiyev A., 2025, Forecasting disease outbreaks in shrimp aquaculture using LSTM deep learning models, Natural and Engineering Sciences, 10(3): 371. https://doi.org/10.28978/nesciences.1811130 Villamar-Barros H., Coronel-Reyes J., and Haro-Sarango A., 2026, Early anomaly detection in shrimp pond water quality using supervised and unsupervised machine learning models, Digital, 6(2): 27. https://doi.org/10.3390/digital6020027 Yang H.Y., Chou H.H., Hung L.J., Huang J.Y., Tien N.Y., Wang H., and Hsieh S.Y., 2025, Machine learning approach for predicting ovarian maturation in Penaeus monodon, Smart Agricultural Technology, 2025: 101597. https://doi.org/10.1016/j.atech.2025.101597 Zarzar C.A., Fernandes T.J., and Oliveira I.R.C.D., 2023, Modeling the growth of Pacific white shrimp (Litopenaeus vannamei) using the new Bayesian hierarchical approach based on correcting bias caused by incomplete or limited data, Ecological Informatics, 77: 102271. https://doi.org/10.1016/j.ecoinf.2023.102271 Zhao M.M., Yao D., Li S., Zhang Y., and Aweya J., 2020, Effects of ammonia on shrimp physiology and immunity: A review, Reviews in Aquaculture, 12(4): 2194-2211. https://doi.org/10.1111/raq.12429
RkJQdWJsaXNoZXIy MjQ4ODYzNA==