The role of artificial intelligence as a stimulus in the global and Russian economy development

Authors: Daibage D.S., Polikovskiy T.A.
Published in issue: #11(88)/2023
DOI: 10.18698/2541-8009-2023-11-947
Category: Humanities | Chapter: Social sciences
Keywords: digitalization, scientometrics, scientific studies, artificial intelligence, neural networks, machine learning, databases, digital economy, scientific potential
Published: 19.12.2023

The paper analyzes domestic and foreign literature sources devoted to the impact of artificial intelligence on the global and Russian economy based on queries to the Google Scholar scientometric database for the 2018–2022. The literature sources were selected using the most frequently appearing keywords: neural networks, machine learning and economics. In order to identify development dynamics in this area in the Russian-speaking segment, a sample formed using similar queries in Russian was studied. Steady increase in the number of documents in regard to applying artificial intelligence in the economics was found, which was explained by the topic relevance making it possible to predict further increase in the number of works in this area. The analysis results provide understanding of the current trends in the area under consideration within the scientific community.

References

[1] Cenggoro T.W., Pardamean B. A systematic literature review of machine learning application in COVID-19 medical image classification. Procedia computer science, 2023, vol. 216, pp. 749–756. http://doi.org/10.1016/j.procs.2022.12.192

[2] Abubakar A.I., Ahmad I., Omeke K.G., Ozturk M., Ozturk C., Abdel-Salam A.M., Mollel M.S., Abbasi Q.H., Hussain S., Imran M.A. A survey on energy optimization techniques in UAV-based cellular networks: from conventional to machine learning approaches. Drones, 2023, vol. 7, no. 3, pp. 214. http://doi.org/10.3390/drones7030214

[3] Toma C., Popa M., Iancu B., Doinea M., Pascu A., Ioan-Dutescu F. Edge machine learning for the automated decision and visual computing of the robots, IoT embedded devices or UAV-drones. Electronics, 2022, vol. 11, no. 21, art. 3507. http://doi.org/10.3390/electronics11213507

[4] Shan L., Miura R., Kagawa T., Ono F., Li H.B., Kojima F. Machine learning-based field data analysis and modeling for drone communications. IEEE Access, 2019, vol. 7, pp. 79127–79135. http://doi.org/10.1109/ACCESS.2019.2922544

[5] Nagato T., Shibuya H., Okamoto H., Koezuka T. Machine learning technology applied to production lines: image recognition system. Fujitsu Scientific & Technical Journal, 2017, vol. 53, no. 4, pp. 52–58.

[6] Allyn J., Allou N., Vidal C., Renou A., Ferdynus C. Adversarial attack on deep learning-based dermatoscopic image recognition systems: risk of misdiagnosis due to undetectable image perturbations. Medicine, 2020, vol. 99, no. 50. http://doi.org/10.1097/MD.0000000000023568

[7] Jia D., Wu Z. Intelligent evaluation system of government emergency management based on BP neural network. IEEE Access, 2020, vol. 8, pp. 199646–199653. http://doi.org/10.1109/ACCESS.2020.3032462

[8] Rosenblatt F. Perceptron simulation experiments. Proceedings of the IRE, 1960, vol. 48, no. 3, pp. 301–309. http://doi.org/10.1109/JRPROC.1960.287598

[9] Gallant S.I. Perceptron-based learning algorithms. IEEE Transactions on neural networks, 1990, vol. 1, no. 2, pp. 179–191. http://doi.org/10.1109/72.80230

[10] Andres A.R., Otero A., Amavilah V.H. Using deep learning neural networks to predict the knowledge economy index for developing and emerging economies. Expert Systems with Applications, 2021, vol. 184, art. 115514. http://doi.org/10.1016/j.eswa.2021.115514

[11] Gogas P., Papadimitriou T. Machine learning in economics and finance. Computational Economics, 2021, vol. 57, pp. 1–4. https://doi.org/10.1007/s10614-021-10094-w

[12] Nosratabadi S., Mosavi A., Duan P., Ghamisi P., Filip F., Band S.S., Reuter U., Gama J., Gandomi A.H. Data science in economics: comprehensive review of advanced machine learning and deep learning methods. SSRN Electronic Journal, 2020, vol. 8, no. 10. http://doi.org/10.2139/ssrn.3711309

[13] Builova N.M., Osipov A.I. Scientometric analysis of publications in the area of nanoenergy based on the materials of the peer-reviewed journal of VINITI RAS physics of nanoobjects and nanotechnology. Scientific and Technical Information Processing, 2012, vol. 39, no. 4, pp. 215–219. (In Russ.).