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Smart Event Forecast for Seaports (SMECS) : Schlussbericht : Projektlaufzeit: 01.09.2017-29.02.2020
Real-time demand forecasting for an urban delivery platform
Generating bunkering statistics from AIS data: A machine learning approach
Machine learning in information systems - a bibliographic review and open research issues
Machine Learning im Cost Engineering des Supply Managements
A novel self-organizing constructive neural network for estimating aircraft trip fuel consumption
Applications of machine learning methods in port operations – A systematic literature review
Using a decision tree to compare rural versus highway motorcycle fatalities in Thailand
Development of a two-stage ship fuel consumption prediction and reduction model for a dry bulk ship
Artificial intelligence and machine learning
A data-driven Bayesian model for evaluating the duration of detention of ships in PSC inspections
Feature subset selection for predicting the success of crowdfunding project campaigns
Estimating urban freight flow using limited data: The case of Delhi, India