Applications of Machine Learning and Data Analytics Models in Maritime Transportation.

Format:

Book

Author/Creator:

Yan, Ran.

Contributor:

Wang, Shuaian.

Series:

Transportation

Language:

English

Subjects (All):

Machine learning.

Physical Description:

1 online resource (217 pages)

Edition:

1st ed.

Place of Publication:

Stevenage : Institution of Engineering & Technology, 2023.

Summary:

This book explores the principles of analysing maritime-transportation related practical problems using data-driven models, with a particular focus on machine learning. Coverage includes data-enabled methodologies, technologies, applications and case studies.

Contents:

Chapter 1: Introduction of maritime transportation

Chapter 2: Ship inspection by port state control

Chapter 3: Introduction to data-driven models

Chapter 4: Key elements of data-driven models

Chapter 5: Linear regression models

Chapter 6: Bayesian networks

Chapter 7: Support vector machine

Chapter 8: Artificial neural network

Chapter 9: Tree-based models

Chapter 10: Association rule learning

Chapter 11: Cluster analysis

Chapter 12: Classic and emerging approaches to solving practical problems in maritime transport

Chapter 13: Incorporating shipping domain knowledge into data-driven models

Chapter 14: Explanation of black-box ML models in maritime transport

Chapter 15: Linear optimization

Chapter 16: Advanced linear optimization

Chapter 17: Integer optimization

Chapter 18: Conclusion.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

1-83724-517-7

1-83953-560-1

OCLC:

1356008875