| Issue |
E3S Web of Conf.
Volume 402, 2023
International Scientific Siberian Transport Forum - TransSiberia 2023
|
|
|---|---|---|
| Article Number | 03041 | |
| Number of page(s) | 5 | |
| Section | Mathematical Modeling, IT, Industrial IoT, AI, and ML | |
| DOI | https://doi.org/10.1051/e3sconf/202340203041 | |
| Published online | 19 July 2023 | |
Evaluation of the execution of government contracts in the field of energy by means of artificial intelligence
1 Financial University under the Government of the Russian Federation, 125167, Moscow, Leningradsky Prospekt, 49/2
2 Moscow automobile and road construction state technical university (MADI), 125167, Moscow, Leningradsky Prospekt, 64
* Corresponding author: pvnikitin@fa.ru
The methodology for evaluating the execution of government contracts in the energy sector by means of machine learning is presented. The signs describing performers and customers in the public procurement system were identified, the risks of fulfilling contracts on the part of customers and performers were identified, the main categories for compiling a dataset were identified and a dataset was assembled. Data problems are described, and ways to fix these problems are described. The problem of classifying the execution of government contracts is solved, a software package for intelligent forecasting of the execution of government contracts is described
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.