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Statistics Using Neural Networks in the Context of Sustainable Development Goal 9.5

Author

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  • Valery Okulich-Kazarin

    (Faculty of Social and Computer Sciences, Higher School of Business—National Louis University, 33-300 Nowy Sącz, Poland
    Faculty of Social Sciences and Humanities, Humanitas University, 41-200 Sosnowiec, Poland)

Abstract

In recent years neural networks have been used to achieve all 17 SDGs. This paper is directly related to SDG 9. In particular, the application of neural networks in statistics indicates the creation and development of a scientific research infrastructure (including encouraging innovation, SDG 9.5). Also, this paper shows the possibility of the mass practical application of neural networks for statistics in the context of sustainable development (with the possibilit of increasing the number of researchers, SDG 9.5). The paper aims to test the following two hypotheses in the context of SDG 9.5: (1) The rapid growth of scientific interest in neural networks will lead to a decrease in the number of scientific publications in statistics. (2) It is possible to use neural networks for calculating statistical indicators. Bibliometric analysis, mathematical modeling, the calculation of statistical indicators using the new prompt and Excel table z-statistics were used. The scientific novelty lies in the new knowledge obtained by the author for the first time. This study integrates advanced technologies (neural networks) and a traditional field (statistics), which is a significant contribution to innovation and infrastructure development (Indicator 9.5.1). The practical value lies in the ease of the mass use of neural networks for statistical data processing of more than 100,000 units, which is related to Indicator 9.5.2. Thus, this paper represents an important contribution to the stimulation of innovation, thereby building up technological potential and leading to a significant increase in the number of researchers (SDG 9.5).

Suggested Citation

  • Valery Okulich-Kazarin, 2024. "Statistics Using Neural Networks in the Context of Sustainable Development Goal 9.5," Sustainability, MDPI, vol. 16(19), pages 1-17, September.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:19:p:8395-:d:1486737
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    References listed on IDEAS

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    1. Zachary K. Collier & Walter L. Leite & Allison Karpyn, 2021. "Neural Networks to Estimate Generalized Propensity Scores for Continuous Treatment Doses," Evaluation Review, , vol. 45(1-2), pages 3-33, February.
    2. Woong Suh & Seongjin Ahn, 2022. "Development and Validation of a Scale Measuring Student Attitudes Toward Artificial Intelligence," SAGE Open, , vol. 12(2), pages 21582440221, May.
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