REDUCTION OF ICT SECURITY RISKS USING LEVEL BASED APPROACH
Vol. 9 No. 2 (2019): JITA – APEIRON Ivo Džakula, Branko Latinović REDUCTION OF ICT SECURITY RISKS USING LEVEL BASED APPROACH Original scientific paper DOI:https://doi.org/ 10.7251/JIT1902099DZ Download Article PDF Abstract Security controls are certainly one of the most preferred ways of controlling the environment in which our system is “alive”. But although they are heavily represented and used in practice, security controls tend to become the same and not change after they are introduced. To try to make the most of the opportunities that this approach provides, this paper will explain the importance of implementing ICT security controls and propose a new approach by adding emergency ICT control. This approach gives us the ability to integrate the entire organization into the development of control by providing a better, more accurate and faster basis for managing the security risks of ICT technology. Keywords: ICT – Information and communications technology, Risk, Security controls. 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.
NEW APPROACH OF STORING AND RETRIEVING LARGE DATA VOLUMES
Vol. 9 No. 2 (2019): JITA – APEIRON Nedeljko Šikanjić, Zoran Ž. Avramović NEW APPROACH OF STORING AND RETRIEVING LARGE DATA VOLUMES Original scientific paper DOI:https://doi.org/10.7251/JIT1902089S Download Article PDF Abstract In today’s world of advanced informational technologies, society is facing a huge amount of data that is just getting impossible to store, process and analyze. In these big data volumes, some of the important information is being lost, that could help us improve the quality of personal and business life. This paper focus is on finding the best possible way of approaching this issue to find a feasible solution in increasing the efficiency and quality of data. Keywords: Data Warehouse, Data Lake, Lambda architecture. 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 POSSIBLE CRYPTOGRAPHIC OPTIMIZATION OF MOBILE HEALTHCARE APPLICATION
Vol. 9 No. 2 (2019): JITA – APEIRON Goran Đorđević, Milan Marković ON POSSIBLE CRYPTOGRAPHIC OPTIMIZATION OF MOBILE HEALTHCARE APPLICATION Original scientific paper DOI:https://doi.org/ 10.7251/JIT1902080DJ Download Article PDF Abstract The paper deals with a possible SOA based m-healthcare online system with secure mobile communication between patients and medical professionals with medical and insurance organizations. An example of an Android-based secure mobile client application is presented which can be used in the described secure m-healthcare model and it is experimentally evaluated. In the paper, we focus on possible optimization of cryptographic algorithms implemented in the secure Android mobile client application. The presented experimental results justify that security operations related to X.509v3 digital certificate generation and XML/WSS digital signature creation/verification are feasible on some current smart phones and justify the use of the proposed optimization techniques for implemented cryptographic algorithms. Keywords: Secure Android Mobile Application, SOA, M-Healthcare, Digital Signature, Encryption. 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.
DDLM – QUALITY STANDARD FOR ELECTRONIC EDUCATION PROGRAMS IN HIGHER EDUCATION OF BOSNIA AND HERZEGOVINA
Vol. 9 No. 2 (2019): JITA – APEIRON Siniša Tomić, Dalibor Drljača DDLM – QUALITY STANDARD FOR ELECTRONIC EDUCATION PROGRAMS IN HIGHER EDUCATION OF BOSNIA AND HERZEGOVINA Original scientific paper DOI:https://doi.org/ 10.7251/JIT1902067T Download Article PDF Abstract The Web-based technological revolution has brought new teaching opportunities and concepts. This expands the range of educational opportunities based on new digital technologies, while certain obstacles and dangers appear that this type of education brings with it at the same time. Electronic education systems should be flexible and it would be ideal if able to meet the specific needs of each student individually. On the other hand, it is extremely important to standardize teaching electronic content, define all vertical and horizontal processes in the electronic education system, and set quality standards that must be respected. Higher education institutions must take an active part in the development and implementation of information technologies in teaching processes. DDLM (Demand-Driven Learning Model) clearly defines the structure of Web-based teaching delivery, so that it essentially defines the quality standard of e-learning programs based on Web technologies. The problem of non-standardization of electronic educational content, poorly defined processes in the system, such as the delivery of electronic content, control activities, personalization or irregular updates, is present everywhere in the world, and so with us. The research conducted in this paper examines the population of students of higher years of study, as well as students of the second and third cycle of study at 5 universities in Bosnia and Herzegovina, in order to get a clear picture of the current state of electronic education in our country. The survey was conducted on 565 students between October 2016 and January 2017. Following the methodology of scientific research, the empirical research was primarily conducted through a survey questionnaire, where primary quantitative data were stored in a database and further analysed, after which we reached the relevant scientific knowledge. Keywords: Electronic education, DDLM standard, LMS. 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.
MISSION CRITICAL ICT
Vol. 9 No. 2 (2019): JITA – APEIRON Goran Đukanović, Dragan Popović MISSION CRITICAL ICT Original scientific paper DOI:https://doi.org/ 10.7251/JIT1902060DJ Download Article PDF Abstract In this paper, three technologies intended to be implemented in Private Mobile Radio systems are analyzed and compared: TETRA (Terrestrial Trunked Radio), LTE (Long Term Evolution) and DMR (Digital Mobile Radio). Characteristics of these networks are collected and compared in one SWOT table. Based on this analysis, appropriate recommendations are made, which should be taken into account when choosing a specific solution for specific uses in Critical Communications systems. Keywords: DMR, ICT, TETRA. 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.
CYBERSECURITY OF RAILWAY COMMAND AND CONTROL SYSTEMS
Vol. 9 No. 2 (2019): JITA – APEIRON Alexey Ozerov CYBERSECURITY OF RAILWAY COMMAND AND CONTROL SYSTEMS Original scientific paper DOI:https://doi.org/10.7251/JIT1902053O Download Article PDF Abstract With the large-scale migration to computer-based and network technology, the threat of unauthorized remote access to railway command and control systems does not appear to be something extraordinary.But external effects shall be considered alongside with internal factorsof signalling software and hardware such errors and undocumented features. Risk mitigation in terms of cybersecurity of signalling installations can onlybe achieved as a combination of means designed within some holistic approach integrating both safety and IT security aspects. Keywords: Cybersecurity, functional safety, signalling, undocumented features, wrong-side failure. 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 ROLE OF POSTPROCESSOR IN THE TRANSLATION OF VECTOR GRAPHICS UNDERSTANDABLE TO THE CNC MACHINE CONTROLLER
Vol. 10 No. 1 (2020): JITA – APEIRON Boris Pauković, Dražen Marinković THE ROLE OF POSTPROCESSOR IN THE TRANSLATION OF VECTOR GRAPHICS UNDERSTANDABLE TO THE CNC MACHINE CONTROLLER Original scientific paper DOI:https://doi.org/ 10.7251/JIT2001058P Download Article PDF Abstract The purpose of this paper is to show the post-processor role and importance in the creation of programming code which CNC machine controller can understand and proceed. Today, the use of CNC technology introduces the Industry 4.0 principles in the production, thus increasing the productivity and precision of the produced parts. It is very important to optimize all the production steps, from choosing the right CAD software for vectors drawing, defining tools and generating tool paths, creating and optimizing post-processor, to translating the tool paths in the programming code which controller of the CNC machine can understand, as well as educating the operators to be able to calibrate the machine and understand and run the CNC programming code properly. When all the abovementioned steps are correctly defined, the production can be optimized and best results are guaranteed. Keywords: CNC technology, CAD/CAM software, postprocessor, CNC programming. 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.
IMPLICATIONS OF IMPLEMENTATION OF ARTIFICIAL INTELLIGENCE IN THE BANKING BUSINESS IN RELATION TO THE HUMAN FACTOR
Vol. 10 No. 1 (2020): JITA – APEIRON Krunoslav Ris, Željko Stanković, Zoran Ž. Avramović IMPLICATIONS OF IMPLEMENTATION OF ARTIFICIAL INTELLIGENCE IN THE BANKING BUSINESS IN RELATION TO THE HUMAN FACTOR Original scientific paper DOI:https://doi.org/10.7251/JIT2001049R Download Article PDF Abstract The banks are known as monetary management institutions because they deal with money. There is a number of customers that a bank daily interacts with. In this technology era, when everything is moving to automation from the beginning step to final product manufacture, medical checkups, medical reports, and evaluation, the banking system is still working on the legacy system. Instead, with the participation and implementation of new Virtual Assistant-powered with AI and Machine Learning technology in the banking sector, the institutions are again using the legacy system or may be bound to use the legacy system. This research will help to elaborate and emphasize the impact of the implementation of automation, using artificial intelligence in the banking business process. This research will be based on the quantitative as well model base prof of system performance using different analytical tools like SPSS. This automation process will help the institutions to enhance profitability, performance, and reduce human dependency. In a nutshell, Virtual Assistants powered with Artificial Intelligence will improve the business process performance in every sector of business, especially the banking sector. Keywords: AI, Machine Learning, Automation, Banking Systems, Virtual Assistants, Chatbots. 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.
INTELLIGENT DISTANCE LEARNING SYSTEMS
Vol. 10 No. 1 (2020): JITA – APEIRON Dragan Vasiljević, Branko Latinović INTELLIGENT DISTANCE LEARNING SYSTEMS Original scientific paper DOI:https://doi.org/10.7251/JIT2001044V Download Article PDF Abstract Models used for creating intelligent systems based on artificial non-chromic networks indicate to the teachers which educational as well as teaching activities should be corrected. Activities that require to be corrected are performed at established distance learning systems and thus can be: lectures, assignments, tests, grading, competitions, directed leisure activities, and case studies. Results regarding data processing in artificial neural networks specifically indicate a specific activity that needs to be maintained, promoted, or changed in order to improve students’ abilities and achievements. The developed models are also very useful to students who can understand their achievements much better as well as to develop their skills for future competencies. These models indicate that students’ abilities are far more developed in those who use some of the mentioned distance learning systems in comparison with the students who learn due to the traditional classes system. Keywords: neural networks, distance learning system, achievements, competencies. 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.
AMBIENT INTELLIGENCE AND E-LEARNING
Vol. 10 No. 1 (2020): JITA – APEIRON Julijana Vasiljević, Željko Stanković AMBIENT INTELLIGENCE AND E-LEARNING Original scientific paper DOI:https://doi.org/ 10.7251/JIT2001035V Download Article PDF Abstract The use of ambient intelligence knowledge inevitably leads to a new education concept particularly in creating an environment towards the implementation of teaching as well as the process of education. The process of teaching and education, besides conventional and physical elements of the environment, will be enriched with elements regarding modern information technology. Ambient intelligence will be presented in this paper as a result of the artificial algorithm neural networks, through the following contexts: e-learning environment, identification, and security. The key role in raising students’ achievements as well as competency levels belongs to modern information technology which works towards creating ambient intelligence. It is also executed through the concept of e-learning onto one of the convenient learning management platforms. Survey results indicate that with the use of ambient intelligence, better results are achieved, especially in mathematics taught at the elementary school level. Furthermore, learned lessons are memorized by students for a long period, which is proved by higher levels of students’ knowledge and skill acquisition in terms of general progress. Keywords: ambient intelligence, e-learning, neural networks. 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.