Financial derivative features based integrated potential fishing zone (IPFZ) Future forecast
نویسندگان
چکیده
In India, around 7 million people depend on fishing for their livelihoods. They are assisted with a reliable and fast brief forecast the areas of fish aggregations. Habitat mapping is critical in supporting strategic choices usage protection. conjunction techniques machine learning, remote control has made comprehensive relevant scales possible. supervised algorithms utilized to make forecasts from datasets, when data accessible without relating output factors. this research work, Ocean Surface Temperature (OST) Satellite derived Chlorophyl material basic inputs generating information Potential Fishing Zone (PFZ). The 16 features additional financial derivative used accurate future prediction PFZ. unwanted missing removed using effective pre-processing techniques. Among various methods available forecasting nonlinear phenomena, Neural Network best efficient method get forecast. Therefore, Function Fitting (FFNN) technique mainly predicting Integrated (IPFZ). practical analyses performed by analysing 80% -20%, 60% -40% terms parameters. From results, it proved that suggested FFNN achieved 90% accuracy, where existing neural network 86% accuracy implementing -20% dataset.
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1Center for Computation & Technology, Louisiana State University, Baton Rouge, LA 70803, U.S.A. 2Department of Computer Science, Louisiana State University, Baton Rouge, LA 70803, U.S.A. 3Southeastern Universities Research Association, 1201 New York Avenue, Washington, DC 20005, U.S.A. 4GoMOOS, 350 Commercial Street, Portland, ME 04101, U.S.A. 5Academy for Advanced Telecommunications, 3139 TAMU...
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ژورنال
عنوان ژورنال: Journal of Intelligent and Fuzzy Systems
سال: 2023
ISSN: ['1875-8967', '1064-1246']
DOI: https://doi.org/10.3233/jifs-231447