Application of Machine Learning Techniques in Aquaculture
||International Journal of Computer Trends and Technology (IJCTT)||
|© 2014 by IJCTT Journal|
|Year of Publication : 2014|
|Authors : Akhlaqur Rahman , Sumaira Tasnim|
|DOI : 10.14445/22312803/IJCTT-V10P137|
Akhlaqur Rahman , Sumaira Tasnim. "Application of Machine Learning Techniques in Aquaculture". International Journal of Computer Trends and Technology (IJCTT) V10(4):214-215 Apr 2014. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
In this paper we present applications of different machine learning algorithms in aquaculture. Machine learning algorithms learn models from historical data. In aquaculture historical data are obtained from farm practices, yields, and environmental data sources. Associations between these different variables can be obtained by applying machine learning algorithms to historical data. In this paper we present applications of different machine learning algorithms in aquaculture applications.
 Bourke, G., Stagnitti, F., and Mitchell, B. 1993. A decision support system for aquaculture research and management. Aquacultural Engineering. 12, 2, 111–123.
 Wang, R., Chen, D. Q., and Fu, Z. 2006. AWQEE-DSS: A Decision Support System for Aquaculture Water Quality Evaluation and Early-warning. International Conference on Computational Intelligence and Security. 2, 959–962.
 Padala, A., Zilber, S. 1991. Expert Systems and Their Use in Aquaculture. Rotifer and Microalgae Culture Systems, Proceedings of a US-Asia Workshop, Honolulu, Hawaii ( 1991).
 Ernst, D. H., Bolte, J. P., and Nath, S. S. 2000. AquaFarm: simulation and decision support for aquaculture facility design and management planning. Aquacultural Engineering. 23, 1–3 (September 2000), 121–179.
 Silvert, W. 1994. Decision support systems for aquaculture licensing. Journal of Applied Ichthyology. 10, 307–311.
 Halide, H. Stigebrandt, A., Rehbein, M., and McKinnon, A. D. 2009. Developing a decision support system for sustainable cage aquaculture. Environmental Modelling & Software. 24, 6 (June 2009), 694–702.
 C. D’ Este, A. Rahman, and A. Turnbull, “Predicting Shellfish Farm Closures with Class Balancing Methods,” AAI 2012: Advances in Artificial Intelligence, Lecture Notes in Computer Science, pp. 39–48, 2012.
 A. Rahman, C. D`Este, and J. McCulloch, “Ensemble Feature Ranking for Shellfish Farm Closure Cause Identification,” Proc. Workshop on Machine Learning for Sensory Data Analysis in conjunction with Australian AI conference, DOI: 10.1145/2542652.2542655 http://doi.acm.org/10.1145/2542652.2542655, 2013.
 M. S. Shahriar, A. Rahman, and J. McCulloch, “Predicting Shellfish Farm Closures using Time Series Classification for Aquaculture Decision Support,” Elsevier Computer and Electronics in Agriculture.
 M. S. Shahriar, C. D. Este and A. Rahman, “On Detecting and Predicting Harmful Algal Blooms in Coastal Information Systems,” Proc. IEEE Oceans, 2012.
 A. Rahman and MS Shahriar, “Algae Bloom Prediction through Identification of Influential Environmental Variables: A Machine Learning Approach,” International Journal of Computational Intelligence and Applications, vol. 12, no. 2, 2013.
 M. S. Shahriar and A. Rahman, “Spatio–temporal Prediction of Algal Bloom,” Proc. IEEE International Conference on Natural Computation (ICNC), pp. 968–972, China, 2013.
 Q. Zhang, A. Rahman, and C. D`Este, “Impute vs. Ignore: Missing Values for Prediction,” Proc. IEEE International Joint Conference on Neural Networks (IJCNN), pp. 2193–2200, Dallas, Texas, 2013.
 A. Rahman, Claire D’ Este, and G. Timms, “Dealing with Missing Sensor Values in Predicting Shellfish Farm Closure,” Proceedings IEEE Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), pp. 351–356, Melbourne, 2013.
 A. Rahman and M. Murshed, “Feature weighting methods for abstract features applicable to motion based video indexing,” IEEE International Conference on Information Technology: Coding and Computing (ITCC), vol. 1, pp. 676–680, USA, 2004.
 A. Rahman and M. Murshed, “Feature Weighting and Retrieval Methods for Dynamic Texture Motion Features,” International Journal of Computational Intelligence Systems, vol. 2, no. 1, pp. 27–38, March 2009.
 C. D’ Este and A. Rahman, “Similarity Weighted Ensembles for Relocating Models of Rare Events,” Proc. International Workshop on Multiple Classifier Systems (MCS), Lecture Notes in Computer Science, pp. 25–36, Nanjing, China, May 15-17, 2013.
 A. Rahman, “Benthic Habitat Mapping from Seabed Images using Ensemble of Color, Texture, and Edge Features,” International Journal of Computational Intelligence Systems, vol. 6, issue 6, pp. 1072–1081, DOI: 10.1080/18756891.2013.816055, 2013.
 A. Rahman, D. Smith, and G. Timms, “A Novel Machine Learning Approach towards Quality Assessment of Sensor Data,” IEEE Sensors Journal, DOI: 10.1109/JSEN.2013.2291855.
 A. Rahman, D. Smith, and G. Timms “Multiple Classifier System for Automated Quality Assessment of Marine Sensor Data,” Proceedings IEEE Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), pp. 362–367, Melbourne, 2013.
aquaculture, machine learning, decision support system