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Vol. 9 No. 1 (2019): JITA - APEIRON

Branko Latinović, Zoran Ž. Avramović, Mаhir Zаjmović

SAFETY ANALYSIS OF REVERSE ALGORITHM ENCRYPTION IN DATABASES

Original scientific paper

DOI:https://doi.org/ 10.7251/JIT1901029L

Abstract

Encryption provides security for databases. This paper provides a new encryption algorithm ,”Reverse Encryption Algorithm (REА)“. Furthermore, designing a REA algorithm has improved data encryption security. Safe and successful proposed encryption algorithm REA is evaluated and compared with the most common encryption algorithms. The designing of the REA algorithm also improves the security of data encryption. Additionally, the safety and the performance of the suggested encryption algorithm REА represents evaluation and enhancement with the most common encryption algorithms. Experimental results show that the proposed encryption algorithm REA surpasses other encryption algorithms in performance and security of databases. All in all, the proposed encryption algorithm REA achieves a balance between security and efficiency.

Keywords: encryption, cryptography, databases, algorithm, REА.

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.