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BEZBJEDNO

URBANO

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Vol. 11 No. 1 (2021): JITA - APEIRON

Dalibor P. Drljača, Dušan Starčević, Siniša Tomić

Four-Layered Structure of E-Government Systems

Original scientific paper

DOI:https://doi.org/10.7251/JIT2101044D

Abstract

The structure of the e-government systems plays a vital role for provision of quality of e-services offered. These systems are quite complex deploying the most advanced technologies and developed and rich countries minimised this complexity with centralised systems. However, the less developed and countries with limited financial support are creating distributed and decentralised systems trying to keep the pace with more developed in provision of e-government services. The common identifier for both types of the systems is four-layered structure, which provides quality of service provision. This paper discusses the fourlayered structure of e-government systems on cases of Estonia, Serbia and Bosnia and Herzegovina. The four-layered structure was found as the quality solution for distributed and decentralised e-government systems.

Keywords: e-government, quality, structure, four-layer, X-tee, X-road. Introduction

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.