Development of deep learning models for automatic detection and warning of potential public safety threats based on audio data analysis
GOAL
Project Goal
The goal of the project is to develop and validate advanced deep learning models capable of automatically detecting and warning about potential public safety threats based on audio data analysis. This will enable a rapid response to emergencies, thereby improving public safety and the efficiency of emergency services.
ABOUT THE PROJECT
Relevance
The project results can be applied in a wide range of sectors, from urban planning to the development of industrial complexes where safety is a priority. In addition, the introduction of new technologies will raise the level of scientific research in artificial intelligence and machine learning, stimulating the development of new algorithmic approaches and the improvement of data analysis technologies. In terms of social relations, the project contributes to building a safer and more stable society by strengthening citizens' trust in the security measures taken by the state.
Overall, the project is expected to have not only a technological but also a social impact, offering solutions that will help make society better protected and better informed about potential threats, which will undoubtedly improve the quality of life and ensure long-term sustainable development.
PLANS
Expected Results
It is planned to publish:
- at least 2 (two) articles and/or reviews in peer-reviewed scientific journals in the research area of the project indexed in the Science Citation Index Expanded of Web of Science and/or having a CiteScore percentile of at least 50 (fifty) in Scopus;
- at least 2 (two) articles or a review in a peer-reviewed foreign or domestic journal recommended from lists 1 and 2 of the Committee for Quality Assurance in Science and Higher Education (CQASHE)
or
- at least 1 (one) article or review in a peer-reviewed scientific journal in the research area of the project indexed in the Science Citation Index Expanded of Web of Science and/or having a CiteScore percentile of at least 50 (fifty) in Scopus, and at least 1 (one) patent on the project topic included in the Derwent Innovations Index (Web of Science, Clarivate Analytics);
or
- at least 1 (one) article or review in a peer-reviewed scientific journal indexed in the Science Citation Index Expanded and ranked in the 1st (first) quartile by impact factor in Web of Science and/or having a CiteScore percentile of at least 80 (eighty) in Scopus.
- at least 1 (one) patent for an invention (including a positive decision on its grant)
TEAM
Research Team
Altaeva A.B.
IITU JSC, Assistant Professor
Scopus ID: 57226765602
ORCID: 0000-0001-9238-7131
Omarov B.S.
IITU JSC
Sultan D.R.
Momynkulov Z.Z.
IITU JSC
Ikram Zh.
Olzhayev O.
IITU JSC
Ordakhanova T.
IITU JSC
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