JITA

JITA Journal of Information Technology and Applications

Vol. 12 No. 1 (2022): JITA - APEIRON

Boris Kovačić, Branko Latinović

Improving the Process of Online Education by Introducing Innovation

Original scientific paper

DOI:https://doi.org/10.7251/JIT2201040K

Abstract

The aim of this paper is to point out the information chain of supply to graduate pharmacists and masters of pharmacy, members of the Pharmaceutical Chamber of the Republic of Srpska. In addition, point to CRM in order to achieve greater satisfaction of members. Point out solutions and innovations that improve data exchange of associations that provide continuous education of graduate pharmacists and masters of pharmacy, members of the Pharmaceutical Chamber of Republika Srpska, as well as online platforms for online education of the Pharmaceutical Chamber of Republika Srpska and local databases.

Keywords: Information supply chain, CRM, Online education, Moodle, data exchange, data exchange difficulties.

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.