Abstract Machine learning has proven to be technically flexible in recent years, which allows
it to be successfully implemented in problems in various areas of knowledge. Carbon …

In this paper, a multiple ensemble neural network model with fuzzy response aggregation for
the COVID-19 time series is presented. Ensemble neural networks are composed of a set of …

In-situ hydrogen production and CO 2 conversion to fuels have attracted significant attention
as rational solutions to alleviate energy crises and climate issues. However, the …

Economics and social science research often require analyzing datasets of sensitive
personal information at fine granularity, with models fit to small subsets of the data …

This paper formulates a fuzzy logic neuron that uses n-uninorms to construct uni-
nullneurons. A fuzzy neural network (FNN) composed of these neurons is easy to operate …

This paper presents a new evolving intelligent model capable of combining the techniques
and concepts of artificial neural networks, fuzzy systems and artificial hydrocarbon networks …

Medium-density fibreboard (MDF) belongs to a class of engineered wood products
facilitating efficient use of wood wastes. For this class of materials, the development of …

Real life data often suffer from non-informative objects—outliers. These are objects that are
not typical in a dataset and can significantly decline the efficacy of fuzzy models. In the paper …

Extreme learning machines (ELMs) are efficient for classification, regression, and time series
prediction, as well as being a clear solution to backpropagation structures to determine …

In data sets some attributes may have higher or lower importance. One of the tools used for
data analysis of such datasets are subspace neuro-fuzzy systems. They elaborate fuzzy …