| Issue |
E3S Web Conf.
Volume 627, 2025
VI International Conference on Geotechnology, Mining and Rational Use of Natural Resources (GEOTECH-2025)
|
|
|---|---|---|
| Article Number | 04018 | |
| Number of page(s) | 12 | |
| Section | Automation, Digital Transformation and Intellectualization for the Sustainable Development of Mining and Transport Systems, Energy Complexes and Mechanical Engineering | |
| DOI | https://doi.org/10.1051/e3sconf/202562704018 | |
| Published online | 16 May 2025 | |
Algorithm for forming a geo-map of citizen appeals using artificial intelligence
1 Department of Digital Technologies, Alfraganus University, Tashkent, Uzbekistan
2 Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, Nurafshon Branch, Faculty of Computer Engineering, Tashkent, Uzbekistan
3 Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, Tashkent, Uzbekistan
* Corresponding author: o.mallayev@afu.uz
This article presents the development of a method, mechanism, module, and algorithm for the automatic analysis, real-time visualization, and intelligent resolution of citizen appeals using artificial intelligence (AI) and geo-mapping technologies. The article outlines the key directions and technologies involved in organizing interactive citizen appeal services and details the development process of an algorithm and software tool designed to create a geo-map of appeals requiring prompt attention. The proposed geo-map module offers essential functions such as automatic detection and analysis of appeals, identification and marking of problem locations on the map, prioritization of urgent issues, prediction and prevention of appeal surges, automation of state services, and rapid response facilitation. The module leverages AI for intelligent appeal analysis, employing Natural Language Processing to interpret appeal texts and classify them by category and priority-for example. Sentiment Analysis is used to assess the tone and urgency of appeals, assigning higher priority to urgent messages. Additionally, Machine Learning techniques predict the likelihood and geographic distribution of recurring problems based on historical electronic appeals-for instance, identifying increased gas supply complaints during winter months.
© The Authors, published by EDP Sciences, 2025
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.
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