In marine vessel operations, fuel costs are major operating costs which affect the overall
profitability of the maritime transport industry. The effective enhancement of using ship fuel …

This paper deals with modelling ship speed and fuel consumption using artificial neural
network (ANN) techniques. These tools allowed us to develop ANN models that can be used …

Recent advances to combine structured regression models and deep neural networks for
better interpretability, more expressiveness, and statistically valid uncertainty quantification …

Interval prediction is an efficient approach to quantifying the uncertainties associated with
landslide evolution. In this paper, a novel method, termed lower upper bound estimation …

Deep ensembles can be considered as the current state-of-the-art for uncertainty
quantification in deep learning. While the approach was originally proposed as a non …

In this work a conceptual theory of neural networks (NNs) from the perspective of functional
analysis and variational calculus is presented. Within this formulation, the learning problem …

Sažetak This paper proposes the usage of an Artificial neural network (ANN) to predict the
values of the residuary resistance per unit weight of displacement from the variables …

A major challenge in sample-based inference (SBI) for Bayesian neural networks is the size
and structure of the networks' parameter space. Our work shows that successful SBI is …

Extreme learning machine (ELM) is a novel and fast learning method to train single layer
feed-forward networks. However due to the demand for larger number of hidden neurons …

Geometric semantic genetic programming (GSGP) represents one of the most promising
developments in the area of evolutionary computation (EC) in the last decade. The results …