Architecture of GIS Solutions for Detection andDevelopment of Wildfire Database
Vol. 11 No. 2 (2021): JITA – APEIRON Saša Ljubojević, Zoran Ž. Avramović Architecture of GIS Solutions for Detection and Development of Wildfire Database Original scientific paper DOI:https://doi.org/10.7251/JIT2102123L Download Article PDF Abstract This research paper presents organization of the business environment for work with geographic information systems (GIS) which are based on open source. The solution is completely open source: operating system, working environment and supporting apps. The architecture consists of: server, workstations, mobile devices and sensors. Software packages for each architecture segment will be displayed. The goal is to achieve a complete business environment for work with open source GIS, thus minimizing the costs of system development and maintenance. The illustrated example shows the possibility of applying GIS within a forestry company, in the field of wildfire monitoring and data collection and registering the possibility of wildfire occurrence using IoT. Keywords: GIS, open source, IoT, wildfires, wildfire detection. 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.
Accelerated Process of Digital Transformation – TheImpact and Consequences of Covid-19
Vol. 11 No. 2 (2021): JITA – APEIRON Mihajlo Travar, Igor Dugonjić, Saša Ristić Accelerated Process of Digital Transformation – The Impact and Consequences of Covid-19 Original scientific paper DOI:https://doi.org/10.7251/JIT2102116T Download Article PDF Abstract Due to the current pandemic caused by the COVID-19 virus, the world is changing rapidly along with digital technologies that transform every aspect of life, society and the economy. To prevent a complete collapse and suspension of all business processes, companies were forced to organize remote work, i.e. workers perform their daily work activities from their homes. The situation in which the world is currently in clearly indicates that digital transformation is something that should be a priority. Digital transformation is changing the way of doing and developing the business, new opportunities for economic progress in the public and private sectors. It allows companies to survive and focus on innovation, increasing their competitiveness. We can say with certainty that digital transformation means much more than complete integration of digital technologies. It also means digitalization and business processes and models automation, marketing, sales, digital purchase, Big Data, and related processes, and is based on five different areas, which include customers, competition, value, innovation and data. Keywords: Digital transformation, Information technology, Business process, Impact, Pandemic Covid-19. 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.
Software Platforms Based on the Principles of GraphicDesign, Automatic Command Generation and VisualProgramming
Vol. 11 No. 2 (2021): JITA – APEIRON Dražen Marinković, Zoran Ž. Avramović Software Platforms Based on the Principles of Graphic Design, Automatic Command Generation and Visual Programming Original scientific paper DOI:https://doi.org/10.7251/JIT2102110M Download Article PDF Abstract This paper presents a new approach to software application development using a graphical interface. The approach is based on a combination of drag and drop elements and logic based on the model’s own concept. Low code platforms and principles have been developed and are still being developed precisely to enable the rapid creation and use of applications that meet all the special needs and requirements of various organizations. No code platforms allow professionals and laymen to create applications via graphical user interfaces without any prior knowledge or qualifications in programming. However, code platforms are closely related to low code platforms because they are both created with a similar goal, based on a very similar way of working and almost the same principles of operation. Many vendors point out that the future of software development is based on configuration, not program. We believe that eliminating code is one way to bring development to higher standards in application development. One of the biggest advantages of the LC/NC platform is that they allow us to take advantage of innate problem solving and human skills by removing at least a significant number, if not all barriers to implementing software solutions in today’s software world. Keywords: low code, no code, visually integrated development environment, low-skilled people, professional developers. 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-Learning Platform Directions and Future ExpansionWith Case Study
Vol. 11 No. 2 (2021): JITA – APEIRON Nedeljko Šikanjić, Zoran Ž. Avramović E-Learning Platform Directions and Future Expansion With Case Study Original scientific paper DOI:https://doi.org/10.7251/JIT2102104S Download Article PDF Abstract When we look at the current situation in the world we can see that world shifts into digital era. This means, it will also influence the learning and educational section. In this science paper we will analyze e-learning platform architecture, propose architecture based on the teaching process and perform comparative analysis of leading e-learning provides. Keywords: E-learning, Education, Databases. 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.
On the Possibility of Embedding the Mechanism ofLinguistic Anticipation into Speech RecognitionSystems
Vol. 11 No. 2 (2021): JITA – APEIRON Daniel Kurushin, Natalia Nesterova, Olga Soboleva On the Possibility of Embedding the Mechanism of Linguistic Anticipation into Speech Recognition Systems Original scientific paper DOI:https://doi.org/10.7251/JIT2102099K Download Article PDF Abstract The paper deals with the problems of modeling speech recognition systems. The authors proposed to use the mechanism of linguistic anticipation in the speech recognition systems. It is known that anticipation is a kind of phenomenon of anticipatory reflection, which can provide an opportunity for the subject to “look into the future.” Anticipation is believed to be an effective method of improving reading technique in children as it enables to increase the speed of reading [1]. The similarity of the learning processes of the human brain and artificial neural-like algorithms allows to suggest that the inclusion of anticipation mechanisms into the operation of the speech recognition algorithm can improve the quality of the system. The paper presents the experiment carried out with the purpose to study the probability of increasing the quality of modern speech recognition systems provided that linguistic anticipation is embedded into such a system. The obtained results are discussed and possible directions for further work on this topic are considered. Keywords: natural language processing, speech recognition systems, language models, anticipation. 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 Serious Game for Social SkillsTraining
Vol. 11 No. 2 (2021): JITA – APEIRON Filimonas Papadiou, Fotis Lazarinis, Dimitris Kanellopoulos <A day at School>: A Serious Game for Social Skills Training Original scientific paper DOI:https://doi.org/10.7251/JIT2102087P Download Article PDF Abstract Soft skills are the personal characteristics of an individual that enhance his/her interactions, career prospects, and job performance. Soft skills include social skills which incorporate characteristics like empathy, self-control, socialization, and friendliness. The development of soft skills at an early age is vital. Currently, there are few serious games for social skills training aimed at primary school pupils. A serious game does not only provide fun but a player can discover knowledge about himself. This paper presents a serious game named “A Day at School” that helps primary school pupils to develop social skills through an educational scenario. In this scenario, the hero of the game faces various situations during a usual day at school. The scenario deals with the situations of bullying, racism, and social awareness of children. By using the educational application, pupils discover appropriate behavior and get the first stimulus for acquiring their social skills. The serious game helps them to socialize and gain the basis to cultivate empathy, friendliness, and self-control. Primary school pupils and teachers evaluated the serious game. The results showed that teachers found that the game is suitable for teaching purposes and its graphical user interface (GUI) is appealing. Keywords: Serious games; Soft skills; Social skills; Educational games. 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.
Demystification of RNAseq Quality Control
Vol. 11 No. 2 (2021): JITA – APEIRON Dragana Dudić, Bojana Banović Đeri, Vesna Pajić and Gordana Pavlović-Lažetić Demystification of RNAseq Quality Control Original scientific paper DOI:https://doi.org/10.7251/JIT2102073D Download Article PDF Abstract Next Generation Sequencing (NGS) analysis has become a widely used method for studying the structure of DNAand RNA, but complexity of the procedure leads to obtaining error-prone datasets which need to be cleansed in order to avoid misinterpretation of data. We address the usage and proper interpretations of characteristic metrics for RNA sequencing (RNAseq) quality control, implemented in and reported by FastQC, and provide a comprehensive guidance for their assessment in the context of total RNAseq quality control of Illumina raw reads. Additionally, we give recommendations how to adequately perform the quality control preprocessing step of raw total RNAseq Illumina reads according to the obtained results of the quality control evaluation step; the aim is to provide the best dataset to downstream analysis, rather than to get better FastQC results. We also tested effects of different preprocessing approaches to the downstream analysis and recommended the most suitable approach. Keywords: data preprocessing, Illumina sequencing, NGS analysis, quality control, sequence analysis, total RNAseq. 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.
Encapsulation and Functionality of Sensor Systems in theWelding Process
Vol. 12 No. 1 (2022): JITA – APEIRON Barbara Bagarić Encapsulation and Functionality of Sensor Systems in the Welding Process Original scientific paper DOI:https://doi.org/10.7251/JIT2201055B Download Article PDF Abstract This paper shows some aspects of interaction between human and robots and role of the sensors in welding process. Today, use of the robots is very important in modern industry. Sensors are very important in welding process and they increase the productivity and precision of the robot. It is very important to optimize use of the sensor, from choosing the right programming metod, creating good environment such as acceptable amounts of humidity in the air, temperature to educating the operators to be able to work with robot machine and understand and run pre-programmed codes. When all the mentioned steps are correctly defined, sensor will work perfectly and production can be as good as possible. Keywords: robots, online programming, offline programming, welding process. 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.
IoT – Company Approach to IoT Modeling and Applications
Vol. 12 No. 1 (2022): JITA – APEIRON Dražen Marinković, Zoran Ž. Avramović IoT – Company Approach to IoT Modeling and Applications Original scientific paper DOI:https://doi.org/10.7251/JIT2201048M Download Article PDF Abstract Using the available literature, this paper attempts to present the company ‘s approach to IoT modeling and the incorporation of IoT technologies into business processes. Furthermore, a comprehensive overview of IoT technologies and systems of large corporations (Yokogawa, Intel) and commercial access to IoT technologies is provided. In conclusion, based on previous knowledge and scientifically based arguments, the advantages and disadvantages of IoT technologies are presented. Keywords: IoT, IioT, Digital Intelligence, Total Cost of Ownership, Operations Excellence, Cloud, CloudIoT. 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.
Improving the Process of Online Education byIntroducing Innovation
Vol. 12 No. 1 (2022): JITA – APEIRON Boris Kovačić, Branko Latinović Improving the Process of Online Education by Introducing Innovation Original scientific paper DOI:https://doi.org/10.7251/JIT2201040K Download Article PDF Abstract The aim of this paper is to point out the information chain of supply to graduate pharmacists and masters of pharmacy, members of the Pharmaceutical Chamber of the Republic of Srpska. In addition, point to CRM in order to achieve greater satisfaction of members. Point out solutions and innovations that improve data exchange of associations that provide continuous education of graduate pharmacists and masters of pharmacy, members of the Pharmaceutical Chamber of Republika Srpska, as well as online platforms for online education of the Pharmaceutical Chamber of Republika Srpska and local databases. Keywords: Information supply chain, CRM, Online education, Moodle, data exchange, data exchange difficulties. 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.