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Smart Event Forecast for Seaports (SMECS) : Schlussbericht : Projektlaufzeit: 01.09.2017-29.02.2020
Machine learning and deep learning
Recursive logit-based meta-inverse reinforcement learning for driver-preferred route planning
Learning efficient haptic shape exploration with a rigid tactile sensor array
Warning systems triggered by trains increase flight-initiation times of wildlife
Testing the TASP: An experimental investigation of learning in games with unstable equilibria
Deep reinforcement learning for the optimal placement of cryptocurrency limit orders
Estimating wildlife strike costs at US airports: A machine learning approach
Hub-and-spoke network design under congestion: A learning based metaheuristic
Modeling train operation as sequences: A study of delay prediction with operation and weather data
Machine learning approach to ship fuel consumption: A case of container vessel
Towards efficient airline disruption recovery with reinforcement learning