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Vol. 9 No. 1 (2019): JITA - APEIRON

Nedim Smailović

Statistical Analysis of Texts of the Balkans Electronic Media Columnists

Original scientific paper

DOI:https://doi.org/10.7251/JIT1901005S

Abstract

This paper presents results of statistical analysis of some segments in texts of the four columnists in the Balkans electronic media: Bosnia and Herzegovina – Dnevni avaz (Muhamed Filipović), Serbia – Politika (Aleksandar Apostolovski), Croatia – Jutarnji list (Miljenko Jergović) and Montenegro – Vijesti (Miodrag Lekić). They write about different themes, in different language styles, but statistical analysis clearly points to large similarities in certain segments, such as number of particular alphabet letters, most common
combinations of two or three words, etc. These results leave space to conclude that it is one polycentric language, which is not a rare phenomenon in the modern world. Naturally, the final judgement about this should be given by the linguists.

Keywords: linguistics, language, electronic media, text analysis, visualization of data.

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.