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Vol. 8 No. 2 (2018): JITA - APEIRON

Igor Dugonjić, Mihajlo Travar, Gordan Bajić

Safety Aspects in Shared Medical it Environment

Original scientific paper

DOI:https://doi.org/ 10.7251/JIT1802086D

Abstract

Regional PACS and other shared medical systems are primary intended for sharing medical images. In these systems, the number of users is significantly increased in relation to local systems, and the fact is that the public network is very frequently used for data transfer. As medical data are very sensitive, such situation creates considerable risk regarding privacy, integrity and right to access to these data. This paper includes the most frequent risks and methods to solve these issues as well as recommendations for safe use of cloud computing systems in order to implement these systems.

Keywords: PACS, DICOM, IHE.

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.