Trends in Educational Games Development
Vol. 1 No. 1 (2011): JITA – APEIRON Miroslav Minović, Dušan Starčević Trends in Educational Games Development Original scientific paper DOI: https://doi.org/10.7251/JIT1101041M Download Article PDF Abstract In this paper we will give a literature review related to game-based education, in the first place at university, as well as the analysis of existing solutions which should enable this type of eLearning. The main topic of this research will be capacity for applying modern information technologies for developing game-based learning platform. When we chose this topic, we started form the fact that there are no applied game-based eLearning systems at universities. During analysis phase, we found that more research is needed in order to improve application of games in education. In the first place, these studies should cover listed problems: how to design educative games in order to achieve better learning effects; how to develop software tools to automate educative game development process; establish methods and techniques for knowledge and skills assessment utilizing educative games. Keywords: Game-based learning, eLearning, Games, Motivation for learning. 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.
Model for Managing Software Development Projects by Fixing Some of the Six Project Management Constraints
Vol. 1 No. 1 (2011): JITA – APEIRON Boris Todorović, Miroslav Matić Model for Managing Software Development Projects by Fixing Some of the Six Project Management Constraints Original scientific paper DOI: https://doi.org/10.7251/JIT1101033T Download Article PDF Abstract This study is focused on the software development process, viewed from perspective of information technology project manager. Main goal of this research is to identify challenges in managing such projects and provide a model for delivering software solutions that satisfies client’s expectations. Project management theory describes six constraints or variables in every project, which project managers can use to better control the project and its outputs. Fixing some of the six project management constraints (scope, cost, time, risks, resources or quality) will allow project manager to focus on most important project aspects, rather than being drawn between all of the variables.This paper is aimed at information technology project managers and portfolio managers, as it describes the practical application of this model on a software development project. Findings of this research support the theory that, by applying good project management practice and focusing on project/business-critical requirements, will enable project managers to complete projects successfully and within tolerance limits. Results show that by identifying key business constraints, project managers can create good balance of six constraints and focus on the most important ones, while allowing other constraints to move between limits imposed by clients and stakeholders. Keywords: software development, project management, PMBOK, six project constraints, fi xed project constraints, risk management, quality management, project scope management. 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 Usage of Information Technology in the Implementation of the Bologna Principle of the Student Mobility
Vol. 1 No. 1 (2011): JITA – APEIRON Gordana Radić The Usage of Information Technology in the Implementation of the Bologna Principle of the Student Mobility Original scientific paper DOI: https://doi.org/10.7251/JIT1101024R Download Article PDF Abstract In this paper, student mobility is observed as one of the steps in realization of the Digital Agenda of the European Union. Student mobility, as one of the main principles of the Bologna process, is the means of effectiveness increase and quality of the educational system in European Higher Education Area, EHEA, because it enables better exchange and flow of knowledge and ideas, as well as the adoption of good practices. Management Identity (IdM) system of the Higher Educational institution is a system which supports student mobility by using personal information when accessing data. The basic identity document in this system is a student smart card with the owner’s fingerprint. This biometrical data insures high level of data and identity protection. This paper proposes informational system which, in itself, contains standards for student mobility support as one of the modules of the IdM system of the Higher Educational institution. Keywords: Identity management, student mobility, smart card, biometrics. 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.
A Multimodal Approach to Design of Aircraft Cockpit Displays
Vol. 1 No. 1 (2011): JITA – APEIRON Mlađan Jovanović, Dušan Starčević, Mirko Petrović A Multimodal Approach to Design of Aircraft Cockpit Displays Original scientific paper DOI: https://doi.org/10.7251/JIT1101016J Download Article PDF Abstract In this paper, we present an approach to design of command tables in aircraft cockpits. To date, there is no common standard for designing this kind of command tables. Command tables impose high load on human visual senses for displaying flight information such as altitude, attitude, vertical speed, airspeed, heading and engine power. Heavy visual workload and physical conditions significantly influence cognitive processes of an operator in an aircraft cockpit. Proposed solution formalizes the design process describing instruments in terms of estimated effects they produce on flight operators. In this way, we can predict effects and constraints of particular type of flight instrument and avoid unexpected effects early in the design process. Keywords: Aircraft cockpit, multimodal user interfaces, aircraft instrument, formal description of cockpit display. 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.
Applications of Smartphones for Ubiquitous Health Monitoring and Wellbeing Management
Vol. 1 No. 1 (2011): JITA – APEIRON Mladen Milošević, Michael T. Shrove, Emil Jovanov Applications of Smartphones for Ubiquitous Health Monitoring and Wellbeing Management Original scientific paper DOI: https://doi.org/10.7251/JIT1101007M Download Article PDF Abstract Advances in smartphone technology and data communications facilitate the use of ubiquitous health monitoring and mobile health application as a solution of choice for the overwhelming problems of the healthcare system. In addition to easier management and seamless access to historical records, ubiquitous technology has the potential to motivate users to take an active role and manage their own conditions.In this paper we present capabilities of the current generation of smartphones and possible applications for ubiquitous health monitoring and wellness management. We describe the architecture and organization of ubiquitous health monitoring systems, Body Sensor Networks, and integration of wearable and environmental sensors. We also describe mainstream mobile health related applications in today’s mobile marketplaces such as Apple App Store and Google Android Marketplace. Finally, we present the development of UAHealth – our integrated mobile health monitoring system for wellness management, designed to monitor physical activity, weight, and heart activity. Keywords: Smartphone, Body Sensor Networks, Health, Wellbeing. 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.
Monitoring of Jee Applications and Performance Prediction
Vol. 1 No. 2 (2011): JITA – APEIRON Dušan Okanović, Milan Vidaković, Zora Konjović Monitoring of Jee Applications and Performance Prediction Original scientific paper DOI: https://doi.org/10.7251/JIT1102136O Download Article PDF Abstract This paper presents one solution for continuous monitoring of JEE application. In order to reduce overhead, Kieker monitoring framework was used. This paper presents the architecture and basic functionality of the Kieker framework and how it can be extended for adaptive monitoring of JEE applications. Collected data was used for analysis of application performance. In order to predict application performance, regression analysis was employed. Keywords: continuous monitoring, Java, JMX, regression analysis. 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 Way of Students’ Efficiency Improvement in Knowledge Acquisition and Transfer Knowledge Model in Clarolina CMS
Vol. 1 No. 2 (2011): JITA – APEIRON Nevzudin Buzađija The Way of Students’ Efficiency Improvement in Knowledge Acquisition and Transfer Knowledge Model in Clarolina CMS Original scientific paper DOI: https://doi.org/10.7251/JIT1102127B Download Article PDF Abstract In this work, throughout the research which was organized in one high school in Bosnia and Herzegovina, it will be shown the influence of exercises on the final result in the e-learning environment at the final test done by students. The research was conducted from the subject informatics in the I, II and III grade. The type of the questions were of multiple choices, addition and accession. The aim was to see how much influence these online exercises have on the final outcome which is demonstrated through the final informatics test done by students and which is done in a classical way in classroom after the finished teaching materials that were planned according to high school rules. In the research, it was taken account of making all preconditions available for easy experiment conducting with regard to technical securing preconditions for students access to blended system of teaching. Concerning the recent experience, it is noticeable that youth like the use of IT and communication devices. In order to secure all necessary conditions, it was conducted the survey among students about having technical preconditions of online access to testing and about students knowledge of work principle in the Claroline LMS platform. The aim was to increase motivation of high school students with regard to the use of online materials, because in high schools of Bosnia and Herzegovina almost nothing is undertaken when it comes to the implementation of new IKT possibilities. Keywords: knowledge transfer, blended learning, Claroline, e-learning, exercises, motivation and web technology. 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.
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 Download Article PDF Abstract 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 Download Article PDF Abstract 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 Download Article PDF Abstract 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.