Safety Aspects in Shared Medical it Environment
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 Download Article PDF 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 DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF 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 DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF 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.
Psychological Connection between Colors and Certain Characteristic Terms
Vol. 8 No. 2 (2018): JITA – APEIRON Nedim Smailović Psychological Connection between Colors and Certain Characteristic Terms Original scientific paper DOI:https://doi.org/10.7251/JIT1802075S Download Article PDF Abstract This paper presents results of a research on psychological connection between 40 offered colors and 91 terms from everyday life. Similar researches have been conducted and published in a number of instances in domestic and foreign bibliography, but this research has certain particularities that are not often present in other articles. For one thing, colors whose associativity with certain terms is being analyzed are shown in a table, their name and code in the RGB system of color marking are provided. Colors were not merely described, e.g. blue, green, yellow, etc., as it may be confusing or lead to misinterpretation of answers since there are many degrees of blue color, for example. In the second part of the poll, the subjects answered the Ishihara test, in order to check the ability to correctly interpret the colors. The third specific is triple visual interpretation of received results using colored graphs. Keywords: color, Ishihara test, psychology, visualization of data. 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 DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF 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 DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF 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.
E-Mail Forensics: Techniques and Tools for ForensicInvestigation of One Court Case
Vol. 8 No. 2 (2018): JITA – APEIRON Ljubomir Lazić E-Mail Forensics: Techniques and Tools for ForensicInvestigation of One Court Case Original scientific paper DOI:https://doi.org/10.7251/JIT1802064L Download Article PDF Abstract E-mail has emerged as the most important application on the Internet for communication of messages, delivery of documents and carrying out transactions and is used not only from computers, but many other electronic gadgets such as mobile phones. This paper is an attempt to illustrate e-mail architecture from forensics perspective. Also, this paper projects the need for e-mail forensic investigation and lists various methods and tools used for its realization. A detailed header analysis of a multiple tactic spoofed e-mail message is carried out in this paper. It also discusses various possibilities for detection of spoofed headers and identification of its originator. Furthermore, difficulties that may be faced by investigators during forensic investigation of an e-mail message have been discussed along with their possible solutions. Our focus is on email header analysis phase offered by the tools. We examine the capability of a particular tools such as EmailTrackerPro and aid4mail in action. The paper describes the court case of cyber crime, the so-called identity theft in Internet communication via electronic mail by two business entities. Identity theft of e-mail addresses and false communications with a foreign company was carried out in order to indicate that a cash transaction of around EUR 100,000 was paid to the account of NN attackers and not to the account in the domestic Serbian bank. Keywords: E-mail forensic, header analysis, E-mail message as evidence. 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 DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF 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 DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF 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.
Implementation of the Neural Network Algorithm in Advanced Databases
Vol. 8 No. 2 (2018): JITA – APEIRON Nedeljko Šikanjić, Zoran Ž. Avramović, Esad F. Jakupović Implementation of the Neural Network Algorithm in Advanced Databases Original scientific paper DOI:https://doi.org/ 10.7251/JIT1802054S Download Article PDF Abstract The progress of humanity is closely related to the progress of technology. The highlight of the development of technology will occur when the machines are able to do what they learn and know; think and make decisions on their own without human help. In this paper we will try to analyze how neural networks work and how they will further develop in terms of application in advanced databases. We will also explore how one of the major IT companies is developing and continuing to use neural networks. Keywords: Neural network, Advanced database, SQL. 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 DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF 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 DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF 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.
MULTIDIMENSIONAL NUMBERS AND SEMANTIC NUMERATION SYSTEMS: THEORETICAL FOUNDATION AND APPLICATION
Vol. 8 No. 2 (2018): JITA – APEIRON Alexander Ju. Chunikhin MULTIDIMENSIONAL NUMBERS AND SEMANTIC NUMERATION SYSTEMS: THEORETICAL FOUNDATION AND APPLICATION Original scientific paper DOI:https://doi.org/ 10.7251/JIT1802049C Download Article PDF Abstract In this article, we present a new class of numeration systems, namely Semantic Numeration Systems. The methodological background and theoretical foundations of such systems are considered. The concepts of abstract entity, entanglement and valence of abstract entities, and the topology of the numeration system are introduced. The proposed classification of semantic numeration systems allows to choose the numeration system depending on the problem being solved. Examples of the use of a two-dimensional number system for image compression problems and computation of a two-dimensional convolution are given. Keywords: Semantics, Abstract Entity, Entanglement, Numeration System. 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 DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF 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 DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF 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.
THE IMPORTANCE OF INFORMATION TECHNOLOGIES IN MANAGING HUMAN POTENTIALS OF THE LOGISTIC CENTERS OF THE REPUBLIC OF SRPSKA
Vol. 9 No. 1 (2019): JITA – APEIRON Nataša Đalić, Edit Terek, Mina Paunović, Mihalj Bakator THE IMPORTANCE OF INFORMATION TECHNOLOGIES IN MANAGING HUMAN POTENTIALS OF THE LOGISTIC CENTERS OF THE REPUBLIC OF SRPSKA Original scientific paper DOI:https://doi.org/ 10.7251/JIT1901036DJ Download Article PDF Abstract Information technologies within logistic systems and their impact on economic, social and personal development have become an important subject of scientific research over the past decades. Theoretical and empirical research has shown the need to achieve and exploit positive outcomes (organization expansion, efficiency, effectiveness, competitive position) adoption and implementation of information technologies in logistics centers. At the time of great technological innovations, the human resources management plays an important role in achieving the competitive advantage of logistics centers on the market. With the development of new technologies, there are also changes in the way human resources management is handled within companies. The theme of this paper is the research of the connection between the importance of information technologies in managing human potentials and the performance of logistics centers. Information technologies have a growing presence in the management of human resources within logistics centers, and therefore their application achieves a great competitive advantage on the market. The aim of this paper is to use the analysis and descriptive methods to find a solution to the importance of information technologies in human resources management within a logistics center with the greatest focus on ERP systems. Keywords: information technology, human resources management, logistics center, ERP system. 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 DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF 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 DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF 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.
SAFETY ANALYSIS OF REVERSE ALGORITHM ENCRYPTION IN DATABASES
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 Download Article PDF 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 DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF 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 DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF 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.
DIGITAL SIGNATURE AND ORGANIZATION OF DECENTRALIZED AUTHENTICATION IN BUSINESS ENVIRONMENT
Vol. 9 No. 1 (2019): JITA – APEIRON Tijana Talić, Gordana Radić, Zoran Ž. Avramović DIGITAL SIGNATURE AND ORGANIZATION OF DECENTRALIZED AUTHENTICATION IN BUSINESS ENVIRONMENT Original scientific paper DOI:https://doi.org/ 10.7251/JIT1901024T Download Article PDF Abstract Modern electronic communication is fast and efficient. It has never been easier to change the document’s content. In this paper, we explain and show through practical work how it is possible to protect the data sent electronically in business communication by using decentralized authentication systems. Keywords: authentication, digital signature. 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 DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF 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 DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF 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.
DIGITALIZATION OF RAILWAYS – ICT APPROACH TO THE DEVELOPMENT OF AUTOMATION
Vol. 9 No. 1 (2019): JITA – APEIRON Zoran Ž. Avramović, Dražen M. Marinković, Igor T. Lastrić DIGITALIZATION OF RAILWAYS – ICT APPROACH TO THE DEVELOPMENT OF AUTOMATION Original scientific paper DOI:https://doi.org/ 10.7251/JIT1901017A Download Article PDF Abstract The concept of digital railway is defined in the European Initiatives, which started in 2016. The basis for this technical development and improvement plan is the Shift²Rail and the Roadmap for Digital Railways, presented by the Community of European Railways and Infrastructure Managers (CER), the International Rail Transport Committee (CIT), the Association of European Rail Infrastructure Managers (EIM), and the International Union of Railways (UIC). Keywords: ATO, automatic train operations, digital railway, ERTMS, ETCS. 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 DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF 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 DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF 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.
STATISTICAL ANALYSIS OF TEXTS OF THE BALKANS ELECTRONIC MEDIA COLUMNISTS
Vol. 9 No. 1 (2019): JITA – APEIRON Nedim Smailović Statistical Analysis of Texts of the Balkans Electronic Media Columnists Original scientific paper DOI:https://doi.org/10.7251/JIT1901005S Download Article PDF Abstract This paper presents results of statistical analysis of some segments in texts of the four columnists in the Balkans electronic media: Bosnia and Herzegovina – Dnevni avaz (Muhamed Filipović), Serbia – Politika (Aleksandar Apostolovski), Croatia – Jutarnji list (Miljenko Jergović) and Montenegro – Vijesti (Miodrag Lekić). They write about different themes, in different language styles, but statistical analysis clearly points to large similarities in certain segments, such as number of particular alphabet letters, most commoncombinations of two or three words, etc. These results leave space to conclude that it is one polycentric language, which is not a rare phenomenon in the modern world. Naturally, the final judgement about this should be given by the linguists. Keywords: linguistics, language, electronic media, text analysis, visualization of data. 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 DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF 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 DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF 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.