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Vol. 1 No. 1 (2011): JITA - APEIRON

Miroslav Minović, Dušan Starčević

Trends in Educational Games Development

Original scientific paper

DOI: https://doi.org/10.7251/JIT1101041M

Abstract

In this paper we will give a literature review related to game-based education, in the first place at university, as well as the analysis of existing solutions which should enable this type of eLearning. The main topic of this research will be capacity for applying modern information technologies for developing game-based learning platform. When we chose this topic, we started form the fact that there are no applied game-based eLearning systems at universities. During analysis phase, we found that more research is needed in order to improve application of games in education. In the first place, these studies should cover listed problems: how to design educative games in order to achieve better learning effects; how to develop software tools to automate educative game development process; establish methods and techniques for knowledge and skills assessment utilizing educative games.

Keywords: Game-based learning, eLearning, Games, Motivation for 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.