№AP27511747

Project Supervisor: Altayeva Aigerim Bakatkaliyevna, PhD, assistant professor

Goal of the project.

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 rapid response to emergencies, thereby enhancing public safety and improving the efficiency of emergency services.

Relevance of the project.

The project's results can be applied across diverse sectors, from urban planning to the development of industrial complexes where safety is a top priority. Additionally, the implementation of these new technologies will elevate the level of scientific research in artificial intelligence and machine learning, fostering the creation of new algorithmic approaches and advancements in data analysis technologies. In the realm of social relations, the project will contribute to building a safer and more stable society, enhancing citizens' trust in government safety measures.

Overall, the project anticipates not only technological but also social impact, offering solutions that will make society more secure and informed about potential threats. This will undoubtedly improve the quality of life and ensure long-term sustainable development.

Expected Results:

• At least 2 (two) articles and/or reviews in peer-reviewed scientific journals related to the project's field of study, indexed in the Science Citation Index Expanded database (Web of Science) and/or with a CiteScore percentile of no less than 50 in the Scopus database.
• At least 2 (two) articles or reviews in peer-reviewed international or domestic journals recommended from lists 1 and 2 of the Committee for Control in the Sphere of Education and Science (CCSES).

Alternatively:

• At least 1 (one) article or review in a peer-reviewed scientific journal related to the project's field of study, indexed in the Science Citation Index Expanded database (Web of Science) and/or with a CiteScore percentile of no less than 50 in the Scopus database, and at least 1 (one) patent related to the project, included in the Derwent Innovations Index database (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 listed in the first quartile (Q1) by impact factor in the Web of Science database and/or with a CiteScore percentile of no less than 80 in the Scopus database.
• At least 1 (one) patent for an invention (including a positive decision on the patent).

Composition of the research group: