Comparative Implementation Analysis of AES Algorithm
Vol. 1 No. 2 (2011): JITA – APEIRON Boris Damjanović, Dejan Simić Comparative Implementation Analysis of AES Algorithm Original scientific paper DOI: https://doi.org/10.7251/JIT1102113S Abstract Download Article PDF Advanced Encryption Standard (AES) is the first cryptographic standard aroused as a result of public competition that was established by U.S. National Institute of Standards and Technology. Standard can theoretically be divided into three cryptographic algorithms: AES-128, AES-192 and AES-256. This paper represents a study which compares performance of well known cryptographic packages – Oracle/Sun and Bouncy Castle implementations in relation to our own small and specialized implementations of AES algorithm. The paper aims to determine advantages between the two well known implementations, if any, as well as to ascertain what benefits we could derive if our own implementation was developed. Having compared the well known implementations, our evaluation results show that Bouncy Castle and Oracle/SUN gave pretty equal performance results – Bouncy Castle has produced slightly better results than Oracle/Sun during encryption, while in decryption, the results prove that Oracle/Sun implementation has been slightly faster. It should be noted that the results presented in this study will show some advantages of our own specialized implementations related not only to algorithm speed, but also to possibilities for further analysis of the algorithm. Keywords: computer security, cryptography, algorithms, standards, AES, performance. 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.
Social Media in Marketing and PR
Vol. 1 No. 2 (2011): JITA – APEIRON Velimir Štavljanin, Vinka Filipović, Milica Kostić-Stanković Social Media in Marketing and PR Original scientific paper DOI: https://doi.org/10.7251/JIT1102113S Abstract Download Article PDF Social media as a new communication channel has managed to radicalize the way companies communicate with consumers and other stakeholders. Companies that are not on time engaged in social media weaken its ability for competitive struggle. In this paper we present possibilities of different types of social media in relation to marketing and public relations. Also, the paper will give specific recommendations for the use of social media in mark eting and public relations. Keywords: Marketing, Public Relations, Social Media. 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.
Mutation Testing: Object-Oriented Mutation and Testing Tools
Vol. 1 No. 2 (2011): JITA – APEIRON Z. Ivanković, B. Markoski, D. Radosav Mutation Testing: Object-Oriented Mutation and Testing Tools Original scientific paper DOI: https://doi.org/10.7251/JIT1102105I Abstract Download Article PDF Software testing represents activity in detecting software failures. Mutation testing represents a way to test a test. The basic idea of mutation testing is to seed lots of artificial defects into the program, test all defects individually, focus on those mutations that are not detected, and, finally, improve the test suite until it finds all mutations. Mutants can be created by mutating the grammar and then generating strings, or by mutating values during a production. Object-oriented (OO) programming features changed the requirements for mutation testing. Non object-oriented mutation systems make mutations of expressions, variables and statements, but do not mutate type and component declarations. OO programs are composed of user-defined data types (classes) and references to the user-defined types. It is very likely that user-defined components contain many defects such as mutual dependency between members/classes, inconsistencies or conflicts between the components developed by different programmers. Class Mutation is a mutation technique for OO programs which particularly targets plausible faults that are likely to occur due to features in OO programming. Mutation testing requires automated testing tools, which is not a trivial tool to make. Automated mutation tools must be able to parse the program and know its language. When the program is run, mutant can be killed by one of two possible scenarios: if a mutant crashes, or if the mutant goes into an infinite loop. Keywords: Mutation testing, Object-oriented mutation, schema-based mutation, refl ection. 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.
Functional Dependencies Analyse in Fuzzy Relational Database Models
Vol. 1 No. 2 (2011): JITA – APEIRON Miljan Vučetić Functional Dependencies Analyse in Fuzzy Relational Database Models Original scientific paper DOI: https://doi.org/10.7251/JIT1102090V Abstract Download Article PDF This paper presents a literature overview of Fuzzy Relational Database Models with emphasis on the role of functional dependencies in logical designing and modeling. The aim is the analysis of recent results in this field. Fuzzy set theory is widely applied for the classical relational database extensions resulting in numerous contributions. This is because fuzzy sets and fuzzy logic are powerful tool for manilupating imprecise and uncertain information. A significant body of research in efficient designing FRDM has been developed over the last decades. Knowing the set of functional dependencies, database managers have a chance to normalize the same eliminating redundancy and data anomalies. In this paper we have considered the most important results in this field. Keywords: fuzzy relational database model, functional dependencies, fuzzy functional dependencies, fuzzy set. 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 Signal Processing Applications with Iterative Logarithmic Multipliers
Vol. 1 No. 2 (2011): JITA – APEIRON Aleksej Avramović, Patricio Bulić, Zdenka Babić Digital Signal Processing Applications with Iterative Logarithmic Multipliers Original scientific paper DOI: https://doi.org/10.7251/JIT1102083A Abstract Download Article PDF Many digital signal processing applications demand a huge number of multiplications, which are time, power and area consuming. But input data is often corrupted with noise, which means that a few least significant bits do not carry usable information and do not need to be processed. Therefore, approximate multiplication does not affect application efficiency when approximation error is less than noise introduced during data acquisition. This fact enables usage of faster and less power-consuming algorithms that is important in many cases where processing includes convolution, integral transformations, distance computations etc. This paper discusses logarithm-based approximate multipliers and squarers, their characteristics and digital signal processing applications based on approximate multiplications. Our iterative multipliers and squarers contain arbitrary series of basic blocks that involves only adders and shifters; therefore, it is not power and time consuming and enables achieving arbitrary accuracy. It was shown that proposed approximate multipliers and squarers can be used in several signal processing applications without decreasing of application efficiency. Keywords: Approximate multiplication, Digital signal processing. 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-Commerce in DinaCard System
Vol. 2 No. 1 (2012): JITA – APEIRON Dalibor Vučić, Saša Salapura E-Commerce in DinaCard System Original scientific paper DOI: https://doi.org/10.7251/JIT1201044V Abstract Download Article PDF This paper presents the status of e-commerce in Serbia with the focus on the domestic DinaCard system, its architecture and participants in the system. We reported results on Internet transaction in DinaCard system in 2009, 2010 and 2011. We found that the number of all participants, including banks with the license for acquiring, banks with the license for issuing and Internet merchants was extremely low (up to 5) and showed no significant positive trend. As a consequence, the number of transactions with the DinaCard cards was also unacceptably low. Based on these results, we concluded that the DinaCard system for Internet transactions have a great potential, but all the participants have to make an effort to significantly increase the use of the domestic card in e-commerce. Keywords: e-commerce, DinaCard, Internet payments, e-business. 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.
History-Enriched Digital Objects as a Factor of Improvement of Adaptive Educational Web Site Navigation
Vol. 2 No. 1 (2012): JITA – APEIRON Željko Eremić History-Enriched Digital Objects as a Factor of Improvement of Adaptive Educational Web Site Navigation Original scientific paper DOI: https://doi.org/10.7251/JIT1201032J Abstract Download Article PDF Modern educational websites offer a wealth of information and content intended for both students and teachers. Such facilities often are not grouped in a single location. While students are in need of fast and efficient access to certain content, teachers are in need for an insight into the learning process of students. By using capabilities of Ajax, it is possible to implement a system for mutual support, where teachers and students who have knowledge of the desired resource would share it with students who are in need of such information in real time. History-enriched digital objects can be used to store information about knowledge sharing. In combination with the records of user’s behavior from the log files, this shared knowledge can make a significant contribution to the successful design and navigation of adaptive web sites. Adaptive web sites can change their content and presentation based on the previously recorded user’s behavior. Keywords: History-enriched digital objects, Web design, Internet technology, Data mining. 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.
Relational Model and Missing Information
Vol. 2 No. 1 (2012): JITA – APEIRON Siniša Jakovljević Relational Model and Missing Information Original scientific paper DOI: https://doi.org/10.7251/JIT1201032J Abstract Download Article PDF This paper examines possibilities offered by relational model when using missing information. The overview is conducted and possibilities which occur in practial use were analyzed. The use of predicates in which missing values occur has also been analyzed. Possible effects on system performance have been indicated. Keywords: relational model, missing information, null, three value logic (3VL), integrity, relational operators. 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.
Comparison of Examination Methods Based on Multiple-choice Questions Using Personal Computers and Paper-based Testing
Vol. 2 No. 1 (2012): JITA – APEIRON Sanja Maravić-Čisar, Robert Pinter, Dragica Radosav, Petar Čisar Comparison of Examination Methods Based on Multiple-choice Questions Using Personal Computers and Paper-based Testing Original scientific paper DOI: https://doi.org/10.7251/JIT1201022C Download Article PDF Abstract Computer-based testing, by facilitating the interaction between teaching and learning, can improve the quality of learning through improved formative feedback which is a key aspect of formative assessment. This study makes a contribution to the research on computer-based testing by examining the mode differences between the paper-and-pencil test and computer-based test. The previously conducted researches in this area dealt with the students of primary and secondary schools. In those researches the points of observation were the students’ successes in mathematics, English and social sciences; no research was done in field of programming languages such as C++ with post-secondary students.The main aim of this study was to find out whether there are differences in the achieved results in two ways of testing: computer-based testing and paper-and-pencil test. Also, the intention was to detect those characteristics of computer based test, which may have a negative effect on students’ achievements. The participants were a representative sample of the population of all engineering students studying computer science at Subotica Tech. The findings of this study led the authors to reach the conclusion that there are no significant differences in scored results for the paper-and-pencil testing and the computer-based testing. Keywords: computer-based test; paper-and-pencil test; assessment; testing; post-secondary education 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.
Analyzing the Cost and Benefit of Pair Programming Revisited
Vol. 2 No. 1 (2012): JITA – APEIRON Lev Faynshteyn, Vojislav B. Mišić, Jelena Mišić Analyzing the Cost and Benefit of Pair Programming Revisited Original scientific paper DOI: https://doi.org/10.7251/JIT1201014F Download Article PDF Abstract Pair programming has received a lot of attention from both industry and academia, but most paper focus on its technical aspects, while its business value has received much less attention. In this paper, we focus on the business aspects of pair programming, by using a number of software development related met rics, such as pair speed advantage, module breakdown structure of the software and project value discount rate, and augmenting them by taking into account the cost of change after the initial product release and inherent non-linearity of the discount rate curves. The proposed model allows for a more realistic estimation of the final project value, and the results of System Dynamics simulations demonstrate some useful insights for software development management. Keywords: Pair Programming, Extreme Programming (XP), System Dynamics, Waterfall, Cost of Change 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.