DODGE-SAFE: Hybrid Dandelion Optimized HAF-BiLSTM for fishermen safety in the Gulf of Mannar

Abstract

Maritime border guidance for fisherman protection provides navigational support and security assistance to those operating in international waters. It is necessary to identify the maritime border and alert the fisherman during the fishing because accidental border crossings can lead to serious repercussions, including arrest, confiscation of boats, and even violence. Border detection systems can address these issues faced by fishermen operating in maritime areas with complex borders and overlapping jurisdiction while operating in such challenging regions. To address these issues, a novel Dandelion Optimized Deep learning for maritime border Guidance for fisherman safety (DODGE-SAFE) has been proposed in this paper to prevent unauthorized maritime border crossings and optimize route planning. The novelty of the system integrates the Hybrid Attention Fusion-based Bidirectional Long Short-Term Memory Network (HAF-BiLSTM) approach to predict the maritime boundaries. Additionally, it incorporates the Dandelion Optimization Algorithm (DOA) to calculate the best alternative route for ensuring safe navigation. These combined innovations offer a predictive, context-aware, and route-adaptive system, a significant departure from traditional alert-only frameworks. The developed model has been executed employing a MATLAB simulator. The developed framework has been examined using specific metrics such as Mean Squared Error (MSE), accuracy, Mean Absolute Error (MAE), F1-score, prevention rate, precision, Mean Absolute Percentage Error (MAPE), recall, and Root Mean Square Error (RMSE). The proposed technique attains a higher accuracy of 99.2%, whereas the accuracy of the suggested framework is 4.74%, 19.45%, and 3.43% better than the traditional approaches such as the Maritime Border Alert System, Marine Life Surveillance and Protection System, and Boundary Alert System respectively. These findings reveal that the developed DODGE-SAFE framework presents an efficient, safe, and secure solution for potential border violations.