UDC 510.64–519.816
DOI: 10.36871/2618-9976.2023.04.004
Authors
Alexey N. Averkin,
Candidate of Physical and Mathematical Sciences, Associate Professor, Russian University of Economics,
G.V. Plekhanov, Computing Center. A.A. Dorodnitsyn of the Russian Academy of Sciences of the Federal
Research Center «Informatics and Management» of the Russian Academy of Sciences, Moscow, Russia
Sergei A. Yarushev,
Candidate of Technical Sciences Associate Professor, Russian University of Economics, G.V. Plekhanov,
Moscow, Russia
Abstract
The paper explores the possibilities and prospects for the implementation of hybrid cognitive models for predicting macroeconomic indicators based on fuzzy cognitive maps and neurofuzzy neural network models based on data on recognized natural fires on satellite images. In the course of the work, an analysis of existing satellite image recognition tools was carried out, problems in the field of satellite image recognition as a source of information were considered and analyzed, deep learning methods were compared, and existing image recognition methods were analyzed. Based on the results of the problem analysis, a deep neural network model was developed for recognizing natural fires on satellite images. Based on the obtained results of image classification, a fuzzy cognitive map for the analysis of the macroeconomic situation was constructed.
Keywords
Artificial intelligence, Satellite image recognition, Fuzzy cognitive maps, Macroeconomics, Cognitive modeling, Deep neural networks, Explanatory artificial intelligence
