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Vol. 3 No. 1 (2013): JITA - APEIRON

Marko Marković, Katarina Plečić

Software Simulations Usage in Business Decision Making Education

Original scientific paper

DOI: https://doi.org/10.7251/JIT1301051M

Abstract

Because of great importance in improving business decision making teaching process in educational institutions, a large number of software simulators are developed. Based on that information, it was necessary to present simulations as one of the most modern educational solutions, with possibilities of their usage. The basic features of a software system developed to support the teaching of business decision making and machine learning algorithms used in this field at the the Singidunum University Faculty of Business Valjevo, have been presented in the paper.

Keywords: software business simulations, business decision making, machine learning.

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.