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• W3022683263 abstract "Understanding the paradox connected with the movement of marine objects has always been a key issue in many areas of communication and other scientific investigations. In developing countries, capsize of boats and missing vessels still top up daily news headlines. Competence of data mining techniques has been widely utilized in various applications to uncover hidden patterns, knowledge extraction and prediction effectively. However, none of such systems are found for analysis of marine data to track and manage information of fisherman and fishing fleets, which has to be attended seriously for effective communication alert dissemination and also to secure them during emergency situations. The work proposes a real-time communication alert system for finding missing vessels in deep sea. The potential of machine learning-based approaches is utilized in this project; fuzzy c-means clustering is used for localizing the vessels in deep sea. Information theory-based parameter selection is used for finding the parameter that influences the movement of the missing vessel and predicting the location to which missing vessel has drifted toward. Knowledge of past trajectories of the vessel is also considered for better spotting of the missing vessel. A decision tree-based classification model is applied along with the proposed clustering for finding the best vessel nodes through which the communication alerts can be extended to save and secure the life of fishermen in time. A performance comparison study of the decision tree and artificial neural network-based classification for identifying best vessel for communication is also done." @default.
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• W3022683263 date "2020-01-01" @default.
• W3022683263 modified "2023-09-27" @default.
• W3022683263 title "Real-Time Communication Alert System for Missing Vessels in Deep Sea" @default.
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• W3022683263 doi "https://doi.org/10.1007/978-981-15-3852-0_13" @default.
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