MACHINE LEARNING FOR THE MARITIME INDUSTRY
| dc.contributor.author | Dr. SAIRAGHUNANDAN PULIBANDLA | |
| dc.contributor.author | Dr. Sherif Kamel | |
| dc.contributor.author | Dr. Mohamed El-dosuky | |
| dc.date.accessioned | 2026-05-20T15:16:39Z | |
| dc.date.issued | 2025-06 | |
| dc.description.abstract | This article handles two problems in maritime industry. The first is how to track ships and vessels. The second is the fact that numerous maritime trade routes are utilized by ships depending on the nation, topographical elements, and ship characteristics. This article proposes a system for tracking ships and developing maritime traffic routes using statistical density analysis. It uses information from an automatic identification system (AIS) to create quantifiable traffic routes. The approach includes preprocessing, deconstruction, and database management. DBSCAN detects boat waypoints, and kernel density estimation analysis (KDE) assesses the breadth of sea routes. The waypoints along the primary route are assessed while taking into account statistical data on all maritime traffic. The findings can be used to plan paths for autonomous surface ships, ensuring safe routes for ships in designated ocean regions. | |
| dc.identifier.uri | https://research.arabeast.edu.sa/handle/123456789/1136 | |
| dc.language.iso | en | |
| dc.publisher | journal of theoretical and applied information technology | |
| dc.title | MACHINE LEARNING FOR THE MARITIME INDUSTRY | |
| dc.type | Article |