## 6 Conclusions  
This research proposes a novel method of naval vessel classification and trajectory prediction, based on the XGBoost model for classification and LSTM for time-series forecasting. These models were applied to real-world maritime datasets, which included AIS data. The need for such solutions is multi-fold, as it benefits, most importantly, wildlife by directly reducing negative effects on nature. Furthermore, general safety is also improved, as any illegal behavior is harder to conceal when a sophisticated monitoring system is used.  
A novel variant of the PSO algorithm was proposed as part of this research, devised to tackle the known drawbacks of the elementary PSO. The novel algorithm was named BPSO and later used to tune the XGBoost and LSTM models. The predictions were made using extensive trip data, including various parameters related to the time gaps between the last position from which the ship's signal had been picked up. Route optimization for optimal and efficient delivery is an additional benefit of the performed optimization. To validate the achieved improvements, the proposed method was compared to various high-performing algorithms for NP-hard problem optimization. The experiments indicated that the proposed solution was dominant and obtained the best results. The XGBoost model tuned using the BPSO algorithm attained an overall accuracy of 99.72% with respect to vessel classification, while LSTM-BPSO achieved an MSE of 0.000098 on the marine trajectory prediction dataset.  
As with all research, certain limitations exist for this work. Given the high computational demands of model optimization, only a limited number of algorithms were considered for evaluation. Furthermore, limited population sizes and a relatively modest number of runs were carried out. Certain practical limitations also exist. The data gathered from AIS systems are often noisy and inconsistent, requiring moderate reprocessing to remove noise.  
Future works will focus on further refining the proposed approach, developing methods for accounting for the complexity associated with AIS data, and further improving the accuracy of both vessel classification and trajectory forecasting. Finally, the potential applications of the introduced boosted PSO will be explored in different fields.  
Conceptualization, N.B., M.Z., R.D. and L.J.; methodology, N.B., A.P. and L.J.; software, N.B., M.Z., L.J. and R.D.; validation, A.T., V.S., N.B. and L.J.; formal analysis, A.P. and V.S.; investigation, N.B., M.Z. and A.P.; resources, A.T., A.P. and R.D.; data curation, M.Z., A.T. and A.P.; writing--original draft preparation, R.D., A.T., A.P. and L.J.; writing--review and editing, M.Z., N.B., R.D. and P.S.; visualization, N.B., A.P. and M.Z.; supervision, M.Z., N.B., R.D. and P.S.; project administration, A.T., A.P. and L.J.; funding acquisition, M.Z. and N.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All datasets employed in this study are freely available at the following URLs: [[https://www.kaggle.com/datasets/eminserkanerdonmez/ais-dataset](https://www.kaggle.com/datasets/eminserkanerdonmez/ais-dataset)]([https://www.kaggle.com/datasets/eminserkanerdonmez/ais-dataset](https://www.kaggle.com/datasets/eminserkanerdonmez/ais-dataset)) (accessed on 28 June 2023) and [[https://marinecadastre.gov/ais/](https://marinecadastre.gov/ais/)]([https://marinecadastre.gov/ais/](https://marinecadastre.gov/ais/)) (accessed on 28 June 2023). Partial source code is available via the following Github URL: [[https://github.com/nbacanin/AppliedSciences2023](https://github.com/nbacanin/AppliedSciences2023)]([https://github.com/nbacanin/AppliedSciences2023](https://github.com/nbacanin/AppliedSciences2023)) (accessed on 28 June 2023).  
The authors declare no conflict of interest.