JITA

JITA Journal of Information Technology and Applications

Vol. 6 No. 2 (2018): JITA - APEIRON

Olja Latinović

Biometric System To Secure The Internet Of Things

Original scientific paper

DOI: https://doi.org/10.7251/JIT1602073L

Abstract

Today, Internet of Things (IoT) is becoming part of a diverse organization, from academic to large enterprises. Also, we use IoT in our daily lives like home appliances, security monitoring such as baby, smoke detectors, health product measure exercise, traffic systems, industrial uses, etc. Biometric is an important segment of IoT, because it proves user’s identity. Biometric security plays the main role in IoT. This paper presents how biometric system secures the Internet of Things and architecture proposal based on one system that connects biometric system and components of Internet of Things.

Keywords: biometrics, Internet of Things, security, authentication.

Vol. 26 No. 2 (2023): JITA - APEIRON

Igor Shubinsky, Alexey Ozerov

Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures

Original scientific paper

Abstract

The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures.

Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.

Vol. 26 No. 2 (2023): JITA - APEIRON

Igor Shubinsky, Alexey Ozerov

Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures

Original scientific paper

Abstract

The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures.

Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.