MMM-DADCL-Net: An Integrated Multi-Model Deep Attention Network based on Machine Learning and Recommendation System
DOI:
https://doi.org/10.63278/mme.v31i1.1233Keywords:
Gulf of Guinea, Bacterial Foraging Optimization Algorithm, Alex-Net, Google-Net, LeNet, VGG-19, LSTM, densely connected FCN.Abstract
The Gulf of Guinea has been in the center of attention since 2010, when the International Maritime Organization labeled it one of the most dangerous areas due to the persistence of piracy and armed robbery against ships. This study presented a novel way to identifying attacks in the GoG. Initially, the dataset is pre-processed using standard methodologies, and features are retrieved using statistical methods. The retrieved characteristics are used to choose features using a self-adaptive bacterial foraging optimization algorithm. Finally, classification is performed using a novel MMM-DADCL Net approach that classifies ship type, ship state, protection level, and attacks in four phases using the self-attention mechanism and CNN models such as Alex-Net, Google-Net, LeNet, VGG-19, LSTM, and Densely connected FCN. The proposed model achieved an Accuracy of 97.09% when implemented on a Python platform. The results demonstrate the proposed model's superior efficiency on comparisons with the current techniques.
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https://springernature.figshare.com/articles/dataset/Processed_csv_file_of_the_piracy_ dat aset/24119643
https://www.kaggle.com/code/teeyee314/classification-of-ship-images/input
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