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Relevant information is extracted from these attributes and used to design fuzzy rules that generate opinion-based alerts. More precisely, the proposed algorithm, Overlap-based Fuzzified rated Marine Behavior, (OBF_MB), derives alert rules when executed on a sea turtle behavior dataset obtained from an online repository. The deployed underwater sensor-collected dataset includes the following measurements: induced electromagnetic field, undersea turtle count, and angle of deviation (in terms of the turtles' navigation direction formulated per month and per day). These values are used as the inputs to the proposed system. To generate an opinion, an information gain-based opinion score is used to calculate the opinion deviations from the generated opinions of the default rule. For future data values, 2004 is used here as the default opinion year and the scenarios is the default rule. This paper formulates three classes of opinions using the proposed algorithm: Alert, Pre-Alert and No-Alert. These opinions can be used in the future to generate real-time alerts based on aquatic animal behavior.<\/p>","DOI":"10.4018\/ijdst.2019010105","type":"journal-article","created":{"date-parts":[[2018,11,23]],"date-time":"2018-11-23T05:43:10Z","timestamp":1542951790000},"page":"56-81","source":"Crossref","is-referenced-by-count":4,"title":["Overlap Function Based Fuzzified Aquatic Behaviour Information Extracted Tsunami Prediction Model"],"prefix":"10.4018","volume":"10","author":[{"given":"Nikita","family":"Jain","sequence":"first","affiliation":[{"name":"University School of Information, Communication and Technology, Gobind Singh Indraprastha Universty, Delhi, India & Bharati Vidyapeeth's College of Engineering , New Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deepali","family":"Virmani","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Bhagwan Parshuram Institute of Technology, Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ajith","family":"Abraham","sequence":"additional","affiliation":[{"name":"Machine Intelligence Research Labs (MIR Labs), Auburn, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"IJDST.2019010105-0","doi-asserted-by":"publisher","DOI":"10.1155\/2016\/6965725"},{"key":"IJDST.2019010105-1","doi-asserted-by":"publisher","DOI":"10.5194\/nhess-10-2631-2010"},{"key":"IJDST.2019010105-2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijdrr.2017.04.005"},{"key":"IJDST.2019010105-3","unstructured":"Armono, H.D. & Suntoyo. 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