The prediction of stock groups values has always been attractive and challenging for
shareholders due to its inherent dynamics, non-linearity, and complex nature. This paper …

Among various soft computing approaches for time series forecasting, fuzzy cognitive maps
(FCMs) have shown remarkable results as a tool to model and analyze the dynamics of …

During the last decades, in the area of machine learning and data mining, the development
of ensemble methods has gained a significant attention from the scientific community …

Demand forecasting is an important aspect in supply chain management that could
contribute to enhancing the profit and increasing the efficiency by aligning the supply …

The present research study explores three types of neural network approaches for
forecasting natural gas consumption in fifteen cities throughout Greece; a simple perceptron …

Currently, artificial intelligence is facing several problems with its practical implementation in
various application domains. The explainability of advanced artificial intelligence algorithms …

Representing and analyzing the complexity of models constructed by data is a difficult and
challenging task, hence the need for new, more effective techniques emerges, despite the …

Food waste generated at the household level is known to be the main contributor to total
food waste, particularly in developed regions. Reducing household food waste (HFW) …

Ozone prediction, a key role for ozone pollution control, is facing the following challenges,
ie, the complex evolution trend of ozone, the cross-interference phenomena between ozone …

The aim of this paper is to present a novel approach to energy use forecasting. We propose
a nested fuzzy cognitive map in which each concept at a higher level can be decomposed …