Int J Performability Eng ›› 2020, Vol. 16 ›› Issue (12): 1845-1852.doi: 10.23940/ijpe.20.12.p1.18451852
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Kaire Anupamaa, *, Y. Chalapathi Raob, Vijaya Kumar Gurralab
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Kaire Anupama, Y. Chalapathi Rao, Vijaya Kumar Gurrala. A Machine Learning Approach to Monitor Water Quality in Aquaculture [J]. Int J Performability Eng, 2020, 16(12): 1845-1852.
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1. Y. Ma and W. Ding, “Design of Intelligent Monitoring System for Aquaculture Water Dissolved Oxygen,” in 2. M. Cordova-Rozas, J. Aucapuri-Lecarnaque,P. Shiguihara-Juárez, “A Cloud Monitoring System for Aquaculture using IoT,” in 3. X. D. Su, L. Sutarlie,X. J. Loh, “Sensors, Biosensors, and Analytical Technologies for Aquaculture Water Quality,” 4. “Aquaculture Development and Coordination Programme,” (http://www.fao.org/3/l8156e/l8156e07.htm, accessed FAO 1978 5. “Freshwater Aquaculture, is a Part of National Cooperative Extension Resource,” 6 (https://freshwater-aquaculture.extension.org/water-quality-in-aquaculture/, accessecd August 2019) 6. M. Lafont, S. Dupont, P. Cousin, A. Vallauri,C. Dupont, “Back to the Future: IoT to Improve Aquaculture: Real-Time Monitoring and Algorithmic Prediction of Water Parameters for Aquaculture Needs,” in 7. Z. Shareef and S. R. N. Reddy, “Design and Wireless Sensor Network Analysis of Water Quality Monitoring System for Aquaculture,” in 8. C. Deng, Y. Gao, J. Gu, X. Miao,S. S. Li, “Research on the Growth Model of Aquaculture Organisms based on Neural Network Expert System,” in 9. “Skretting is an Innovative Program about Nutrition Needs,” (https://www.skretting.com/en/faq/why-is-aquaculture-important/, acessed 2020) 10. A. Fredheim and T. Reve, “Future Prospects of Marine Aquaculture,” 11. “Wikipedia, for Analysis of Aqucculture,” (https://en.wikipedia.org/wiki/Fish_farming, accessed August 2020) 12. V. E. Kostin, A. A. Silaev,A. V. Savchic, “Information-Measuring System for Monitoring and Control Aquaculture of Pond Farm,” in 13. P. Whig and S. N. Ahmad, “Modelling and Simulation of Economical Water Quality Monitoring Device,” 14. K. Preetham, B. C. Mallikarjun, K. Umesha, F. M. Mahesh,S. Neethan, “Aquaculture Monitoring and Control System: An IoT based Approach,” 15. G. A.Defe and A. Z. C. Antonio, “Multi-Parameter Water Quality Monitoring Device for Grouper Aquaculture,” in 16. “Spotlight is a Communication Group Used for Different Technologies and Areas,” (https://www.spotlightmetal.com/machine-learning--definition-and-application-examples-a-746226/, accessed August 2018) 17. “Analytics Vidhya Provides a Community based Knowledge Portal,” (https://www.analyticsvidhya.com/blog/2017/09/naive-bayes-explained/, accessed September 2017) 18. K. Raghu Sita Rama Raju and G. Harish Kumar Varma, “Knowledge based Real Time Monitoring System for Aquaculture using IoT,” in Proceedings of the2017 |
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