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Vol. 9 No. 2 (2019): JITA - APEIRON

Goran Đorđević, Milan Marković

ON POSSIBLE CRYPTOGRAPHIC OPTIMIZATION OF MOBILE HEALTHCARE APPLICATION

Original scientific paper

DOI:https://doi.org/ 10.7251/JIT1902080DJ

Abstract

The paper deals with a possible SOA based m-healthcare online system with secure mobile communication between patients and medical professionals with medical and insurance organizations. An example of an Android-based secure mobile client application is presented which can be used in the described secure m-healthcare model and it is experimentally evaluated. In the paper, we focus on possible optimization of cryptographic algorithms implemented in the secure Android mobile client application. The presented experimental results justify that security operations related to X.509v3 digital certificate generation and XML/WSS digital signature creation/verification are feasible on some current smart phones and justify the use of the proposed optimization techniques for implemented cryptographic algorithms.

Keywords: Secure Android Mobile Application, SOA, M-Healthcare, Digital Signature, Encryption.

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.