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Vol. 10 No. 1 (2020): JITA - APEIRON

Nedim Smailović, Zoran Ž. Avramović

SOME POSSIBILITIES OF COMPUTER LINGUISTICS ON AN EXAMPLE OF ANALYSIS OF NOVELS

Original scientific paper

DOI:https://doi.org/ 10.7251/JIT2001005S

Abstract

This paper shows some aspects of statistical analysis of well-known novels: Death and the Dervish by Meša Selimović (1966), Autobiography by Branislav Nušić (1924) and In the Registrar’s Office by Ante Kovačić (1888). The goal of the analysis is to point to mutual similarities and differences of statistical data in those texts and to compare them with the up to date findings in that field. A part of the analysis relates to comparison of languages of these writers with today’s language, used by column authors in electronic media. These kinds of researches belong to linguistics, as a science on language, but the results may be used in the contemporary development of artificial intelligence.

Keywords: computer linguistics, language, 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.