Food is one of the most traded goods, and the conflict in Ukraine, one of the European
breadbaskets, has triggered a significant additional disruption in the global food supply …

The wave energy sector has not reached a sufficient level of maturity for commercial
competitiveness, thus requiring further efforts towards optimizing existing technologies and …

In this paper we present a novel methodology, referenced as ORFEO (Ordinal classifier and
Regressor Fusion for Estimating an Ordinal categorical target), to enhance the performance …

The prediction of wave height and flux of energy is essential for most ocean engineering
applications. To simultaneously predict both wave parameters, this paper presents a novel …

One of the most promising solutions that stands out to mitigate climate change is floating
offshore wind turbines (FOWTs). Although they are very efficient in producing clean energy …

This study aimed to develop a neural network (NN)-based method to predict the long-term
mean radiant temperature (MRT) around buildings by using meteorological parameters as …

The variation tendency of oxygen demand plays a crucial role in planning and scheduling in
the steel industry. Traditional prediction methods merely utilize historical data without …

In this paper we have tackled the problem of long-term air temperature prediction with
eXplainable Artificial Intelligence (XAI) models. Specifically, we have evaluated the …

Ocean surface wave environment due to its randomness has mostly been analyzed and
understood from a statistical way. Accurate and reliable wave prediction is the basic …

In this study, we present EBANO (Ensemble BAsed on uNimodal Ordinal classifiers), which
is a novel ensemble approach of ordinal classifiers that includes four soft labelling …