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Vol. 4 No. 1 (2014): JITA - APEIRON

Tijana Talić

Full Text Search and Indexing in Languages With Two Alphabets

Original scientific paper

DOI: https://doi.org/10.7251/JIT1401041T

Abstract

The languages spoken in Bosnia and Herzegovina use both Cyrillic and Latin equally. This is an additional problem with indexing and full text searching. In this paper, we are analyzing this problem. Using the tools available on PostgreSQL and ispell dictionaries, we made a solution. As part of the solutions, we created a dictionary of stop words, adjusted the affix file for both alphabets and from the list of words made functional vocabularies for indexing and searching. We made a full search configuration which is useful for indexing texts in both alphabets.

Keywords: Semantic full-text search; Indexing; Artifi cial intelligence.

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.