Artificial Intelligence Based Emergency Identification Computer System

• Autorzy: Velychko Diana , Osukhivska Halyna , Palaniza Yuri , Lutsyk Nadiia , Sobaszek Łukasz

Identyfikatory

DOI

10.12913/22998624/184343

• Języki publikacji: EN

Abstrakty

EN

The use of Artificial Intelligence is currently being observed in many areas of life. In addition to assisting in intellectual work, solving complex computational problems, or analyzing various types of data, the aforementioned techniques can also be applied in the process of providing security to people. The paper proposes an emergency identification system based on Artificial Intelligence that aims to provide timely detection and notification of dangerous situations. The proposed solution consider the position of a person "hands up" as an emergency situation that will indicate a potential danger for a person. Because people in the face of potential danger are mostly forced to raise their hands up and this pose attracts attention, emphasizes the emotional reaction to certain events and is usually used as a sign of risk or as a means of subjugation. The system should recognize the pose of a person, detect it, and consequently inform about the threat. In this paper, an AI based emergency identification system was proposed to detect the human pose "hands up" for emergency identification using the PoseNet Machine Learning Model. The assumption consists that the utilization only of 6 key points made allows reducing the computing resources of the system since the conclusion is made taking into account a smaller amount of data. For the study, a dataset of 1510 images was created for training an Artificial Intelligence model, and the decisions were verified. Supervised Machine Learning methods are used to classify the definition of an emergency. Alternative methods: Support Vector Machine, Logistic Regression, Naïve Bayes Classifier, Discriminant Analysis Classifier, and K-nearest Neighbours Classifier based on the accuracy were evaluated. Overall, the paper presents a comprehensive and innovative approach to emergency identification for quick response to them using the proposed system.

Słowa kluczowe

EN

artificial intelligence; deep learning; detection; emergency system; computer system

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2024
• Tom: Vol. 18, no 2
• Strony: 296--304
• Opis fizyczny: Bibliogr. 17 poz., fig., tab.

Twórcy

autor

Velychko Diana

• Computer Systems and Networks Department, Faculty of Computer Information Systems and Software Engineering, Ternopil Ivan Puluj National Technical University, Ternopil, Ukraine

• https://orcid.org/0000-0001-7635-1761

autor

Osukhivska Halyna

• Computer Systems and Networks Department, Faculty of Computer Information Systems and Software Engineering, Ternopil Ivan Puluj National Technical University, Ternopil, Ukraine

• https://orcid.org/0000-0003-0132-1378

autor

Palaniza Yuri

• Department of Radio Engineering Systems, Faculty of Applied Information Technologies and Electrical Engineering, Ternopil Ivan Puluj National Technical University, Ternopil, Ukraine

• https://orcid.org/0000-0002-8710-953X

autor

Lutsyk Nadiia

• Computer Systems and Networks Department, Faculty of Computer Information Systems and Software Engineering, Ternopil Ivan Puluj National Technical University, Ternopil, Ukraine

• https://orcid.org/0000-0002-0361-6471

autor

Sobaszek Łukasz

• Department of Information Technology, Faculty of Mathematics and Information Technology, Lublin University of Technology

• https://orcid.org/0000-0003-1298-2438

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).