Ran Yan

Assistant Professor

Nanyang Technological University (NTU)

Singapore

Research Interests

Explore related searches

Professors in Computer Vision

Contact this professor

Get a free first email built on one of their papers, ready for you to edit and send yourself.

About

Dr. Ran Yan is an Assistant Professor at Nanyang Technological University in Singapore. His research areas include marine engineering, machine learning applications in vessel fuel consumption prediction, and intelligent shipping technologies. Recent publications highlight his work on semi-supervised learning for ship risk prediction and enhancing port state control through advanced modeling techniques. Dr. Yan's interdisciplinary approach integrates maritime knowledge with cutting-edge algorithms to address challenges in the maritime industry.

Articles (27)

Year:

2025

Year:

2025

Year:

2025

Year:

2025

Year:

2025

Year:

2025

Year:

2025

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2022

Year:

2022

Year:

2022

Year:

2022

Year:

2022

Year:

2022

Year:

2022

Year:

2022

Year:

2022

Year:

2022

Collaborators (1)

Ponnuthurai Nagaratnam Suganthan

Nanyang Technological University (NTU)

SINGAPORE

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Ran Yan

Research Interests

Explore related searches

Contact this professor

About

Articles (27)

Comparison of grey models and machine learning models in vessel fuel consumption prediction

Semi-supervised learning for multi-target regression in ship risk prediction

Prediction of vessel arrival time to port: a review of current studies

Marine Fault Diagnosis With Self-Supervised Learning Incorporating Out-of-Distribution Detection

Intelligent shipping: integrating autonomous maneuvering and maritime knowledge in the Singapore-Rotterdam Corridor

Improving port state control through a transfer learning-enhanced XGBoost model

Ship fuel consumption prediction based on transfer learning: Models and applications

Energy-Efficient Satellite Range Scheduling Using a Reinforcement Learning-Based Memetic Algorithm

Development of computer vision informed container crane operator alarm methods

Sora for Intelligent Vehicles: A Step From Constraint-Based Simulation to Artificiofactual Experiments Through Dynamic Visualization

A Viewpoint Adaptation Ensemble Contrastive Learning framework for vessel type recognition with limited data

A Viewpoint Adaptation Ensemble Contrastive Learning framework for vessel type recognition with limited data

A decentralized federated learning-based spatial-temporal model for freight traffic speed forecasting

A decentralized federated learning-based spatial–temporal model for freight traffic speed forecasting

After five years' application of the European Union monitoring, reporting, and verification (MRV) mechanism: Review and prospectives

After five years’ application of the European Union monitoring, reporting, and verification (MRV) mechanism: Review and prospectives

Classification and regression in prescriptive analytics: Development of hybrid models and an example of ship inspection by port state control

Efficient and explainable ship selection planning in port state control

Quantitative analysis of the impact of COVID-19 on ship visiting behaviors to ports- A framework and a case study

Ship detention prediction using anomaly detection in port state control: model and explanation

“Predict, then optimize” with quantile regression: A global method from predictive to prescriptive analytics and applications to multimodal transportation

Predicting Maximum Work Duration for Construction Workers

Integrating prediction with optimization: Models and applications in transportation management

Coordinated approaches for port state control inspection planning

Is port state control influenced by the COVID-19? Evidence from inspection data

A global method from predictive to prescriptive analytics considering prediction error for “Predict, then optimize” with an example of low-carbon logistics

Ship selection in port state control: status and perspectives

Collaborators (1)

Ponnuthurai Nagaratnam Suganthan

Make your next move.