2026-04-17T17:49:06.475Z http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai

ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:3:9790 2025-01-03T14:40:07Z 3

The eXtreme Gradient Boosting Method Optimized by Hybridized Sine Cosine Metaheuristics for Ship Vessel Classification, Chapter in LNNS Lecture Notes in Networks and Systems: ADCIS 2023: Advances in Data-Driven Computing and Intelligent Systems, Springer, volume 891 2024 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/3/9790 https://link.springer.com/chapter/10.1007/978-981-99-9524-0_20 M. Bukumira M. Zivkovic M. Antonijevic L. Jovanovic N. Bacanin T. Zivkovic Ship classification is essential in coastal areas to ensure safety, protect the environment and improve maritime security. It also allows the optimization of resource allocation and can boost economic growth. Therefore, vessel identification is crucial to employ appropriate security measures. However, communication interruptions can happen during poor weather conditions, which could hinder the overall safety of vessels in the area. Security is, therefore, a main pivotal aspect that drives forward the vessel identification systems. This paper tackles this problem by proposing an XGBoost machine learning model that is optimized by an enhanced variant of the sine cosine metaheuristic algorithm that has the role of identifying and classifying naval vessels. The proposed method has been compared to the results attained by other cutting-edge metaheuristics algorithms, and experimental outcomes show that it obtained supreme results for this particular task. bookPart Springer, Singapore 255 270 10.1007/978-981-99-9524-0_20 LNNS Lecture Notes in Networks and Systems: ADCIS 2023: Advances in Data-Driven Computing and Intelligent Systems, volume 891