Abstract
Machine Learning (ML) can be defined as a level of algorithm which may allow software applications to create more accurate in forecasting outputs without being external programmed. Since maritime transportation requires smart technologies, adaptation of machine learning tools might provide utmost benefit for efficiency, sustainability and reduction of operational costs. As the data is core element to unlocking the uncertainty, it may help to improve shipping. So far, the data acquisition on maritime transportation is quite limited. Therefore, adaptation of machine learning techniques in the maritime transportation is narrow as compared to other industries. The aim of this paper is to discuss machine learning applications and their impacts to future of maritime transportation industry. A sets of comparative researches will be undertaken to present current situation and potential impacts to future in maritime transportation. With the help of this research, the maritime practitioners and professionals will gain an idea on focusing appropriate algorithm for a specific shipping problem such as voyage optimization and economics, sustainability of transportation, controlling of freight rates, maintenance forecasting, digitalization on bridge and engine control room, etc.
| Original language | English |
|---|---|
| Pages (from-to) | 275-280 |
| Number of pages | 6 |
| Journal | Procedia Computer Science |
| Volume | 158 |
| DOIs | |
| Publication status | Published - 2019 |
| Event | 3rd World Conference on Technology, Innovation and Entrepreneurship, WOCTINE 2019 - Istanbul, Turkey Duration: 21 Jun 2019 → 23 Jun 2019 |
Bibliographical note
Publisher Copyright:© 2019 The Authors. Published by Elsevier B.V.
Keywords
- Data mining
- Machine learning
- Maritime transportation