A new Approach to Computer Analysis of Queuing Systems Without Programming
Vol. 5 No. 1 (2015): JITA – APEIRON Zoran Ž. Avramović, Radomir Z. Radojičić, Saša D. Mirković A new Approach to Computer Analysis of Queuing Systems Without Programming Original scientific paper DOI: https://doi.org/10.7251/JIT1501025A Download Article PDF Abstract The paper presents original object oriented programming system ARS for modelling and simulation queuing systems. Programming system was developed in programming language C++. It establishes connection with intrinsic, but also with other Windows programming packages, in a simple way, through object oriented environment. Basic characteristics and possibilities of programming system, as well as comparative analysis of simulators: mathematical model (analytical solution) – GPSS/H – ARS, on the example of closed queuing network in the paper is given. The significant application for computer performance evaluation is reported. Keywords: simulation, queuing system, programming system, computer performance evaluation. 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.
Automatic Temperature Regulation System of Locomotive Traction Induction Motors With Power Losses Minimization
Vol. 5 No. 1 (2015): JITA – APEIRON A. S. Kosmodamianskiy, V. I. Vorobiev, A. A. Pugachev Automatic Temperature Regulation System of Locomotive Traction Induction Motors With Power Losses Minimization Original scientific paper DOI: https://doi.org/10.7251/JIT1501013K Download Article PDF Abstract The air cooling systems are shown to be used to provide required temperature condition of traction induction motors on locomotives. The automatic temperature regulation system is developed for its using to solve such a task. Results of experimental investigation showed that the AO63-4 induction motor stator end winding on the side opposite to air supply is the most heated part of the induction motor. Based on the results of the research, it was used an aperiodic second-order transfer function for approximation of the thermal transient curves. The design of an induction motor control system maintaining operating mode with minimum of the stator current are considered. It is shown that the modes of minimum of the stator current and minimum of power losses are quite close to each other. The MatLab simulation results taking typical nonlinearities and iron power losses in an induction motor and conduction and commutation power losses in semiconductors of frequency converter into account are presented. It is shown that as a result of application of the suggested system the power losses reduction may be led up to 20 % relatively to classical scalar control. Keywords: induction motor, locomotive, automatic system, equivalent circuit, power losses minimization 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.
Simulation of Processes in Traction Electric Actuators of Autonomous Vehicles
Vol. 5 No. 1 (2015): JITA – APEIRON Yu M. Inkov, E. V. Sachkova, Ya. A. Korobanova, T. N. Fadeikin Simulation of Processes in Traction Electric Actuators of Autonomous Vehicles Original scientific paper DOI: https://doi.org/10.7251/JIT1501005I Download Article PDF Abstract At the stage of construction of traction electric drives of electric power systems (EPS) the analysis of electromagnetic and energy processes in various operational and emergency modes is needed. The calculation of complex multi-electromechanical systems of modern vehicles is only possible by computer simulation. The programming of such complex systems by traditional methods is practically impossible, or is a time-consuming process. The use of universal modeling systems is the only possible way of modeling of multi-component systems. In this article we deal with the mathematical model of the synchronous generator of autonomous vehicle in computer-aided design (CAD) OrCAD 10.0 (Pspice). The software package OrCAD 10.0 (Pspice) is one of the most versatile in the field of simulation of electrical circuits with a large number of components. OrCAD libraries contain proven by the time mathematical models of practical application of electric power components and it is continuously ever-growing. At the end of the article the characteristics for different modes of operation of a synchronous generator are summarized. Keywords: Traction electric drive, autonomous locomotive, marine engine, mathematical model, energy processes, electromechanical system, synchronous generator, the hysteresis loop. 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 Review on Methods for the Assessment of Information System Projects
Vol. 5 No. 2 (2018): JITA – APEIRON Meltem Ozturan, Birgul Basarir-Ozel, Ezgi Akar A Review on Methods for the Assessment of Information System Projects Original scientific paper DOI: https://doi.org/10.7251/JIT1502117O Download Article PDF Abstract Recently, it is inevitable that businesses invest in many information system (IS) projects in order to gain a competitive advantage within the internal industry and global environment. The important point is the selection of the appropriate IS environment, hence the optimal IS investment methods with respect to changing technological needs. In this respect, both empirical and conceptual studies are reviewed to identify the relevant IS/IT investment methods. After an extensive literature review, 51 relevant articles are identified. The IS/IT investment methods studied in these articles are classified and examined within the three categories: financial, non-financial, and hybrid. The results reveal that most of studies focus on a mixed usage of financial and non-financial methods called hybrid methods, whereas financial methods are used more frequently when compared to non-financial methods during the selected research period. On the other hand, the usage of pure financial methods decreases in recent years, while the usage of hybrid and non-financial methods increases in the same period. Keywords: IS/IT investment methods, IS/IT investment, fi nancial methods, non-fi nancial methods, hybrid methods 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.
Performance Evaluation of Routing Protocols in a Wireless Sensor Network for Targeted Environment
Vol. 5 No. 2 (2018): JITA – APEIRON Viktor Denkovski, Biljana Stojcevska, Toni Dovenski, Veno Pachovski, Adrijan Bozinovski Performance Evaluation of Routing Protocols in a Wireless Sensor Network for Targeted Environment Original scientific paper DOI: https://doi.org/10.7251/JIT1502110D Download Article PDF Abstract This paper investigates the performance of reactive and proactive routing protocols in a wireless sensor network for targeted enviroment. AODV and DSR are chosen as representatives for the reactive routing protocols and DSDV for the proactive. A wireless sensor network application for farm cattle monitoring is created. The proposed solution meets a desired requirement for periodically observing the condition of each individual animal, processing the gathered data and reporting it to the farmer. However, an implementation of a WSN needs to meet particular technical challenges before it can be suitable to be applied in cattle management. For this, multiple scenarios are presented with various situations to evaluate the performance of routing protocols in the WSNs. Finally, the results concerning data transportation from the mounted sensory devices to the mobile nodes are discussed and analyzed. Keywords: wireless sensor network, herd management, cattle health monitoring, routing protocol, cattle monitoring application, mobile nodes 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.
Solving the Chief Executive Officer Selection Problem Using the Fuzzy Decision Support System
Vol. 5 No. 2 (2018): JITA – APEIRON Božo Vukoje, Vjekoslav Bobar Solving the Chief Executive Officer Selection Problem Using the Fuzzy Decision Support System Original scientific paper DOI: https://doi.org/10.7251/JIT1502097V Download Article PDF Abstract Chief Executive Officer (CEO) selection as a subset of personnel selection asks for different characteristic compared to a selection of other personnel. The reason for this is the polymorphic nature of the CEO role. The complexity and importance of the selection problem, call for analytical methods rather than decisions based on intuition. The multi-criteria nature and the presence of both qualitative and quantitative factors make the entire selection more complex. As such, the CEO selection is a multi-criteria decision making problem decision making problem, affected by several qualitative and quantitative, often conflicting criteria which are usually uncertain. This paper proposes a CEO selection approach based on the fuzzy decision support system developed by using JAVA technology and extent analysis method. This system is applied in a real-life case study to evaluate the most suitable person for a CEO position in information and communication (ICT) company dealing with the rating of both qualitative and quantitative criteria, and testing appropriate consistency to ensure quality of selection. Keywords: CEO selection, fuzzy numbers, extent analysis method, decision support system. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.
Simulated Annealing and Evolutionary Algorithm for Base Station Location Problem: a Comparison of Methods
Vol. 5 No. 2 (2018): JITA – APEIRON Evgenii Skakov, Vladimir Malysh Simulated Annealing and Evolutionary Algorithm for Base Station Location Problem: a Comparison of Methods Original scientific paper DOI: https://doi.org/10.7251/JIT1502088S Download Article PDF Abstract A modifications of the evolutionary algorithm and simulated annealing method for solving the base station location problem for creating a wireless data network is introduced in the article. By the way of computer simulation a comparison of speed and accuracy of solutions obtained by the proposed methods and the method of exhaustive search is produced. The study revealed that new simulated annealing method show better results than the modified evolutionary algorithm. Keywords: base station location, evolutionary algorithm, simulated annealing, wireless networks, optimization, SIR. 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.
Hybrid of Hill Climbing and SAT Solving for Air Traffic Controller Shift Scheduling
Vol. 5 No. 2 (2018): JITA – APEIRON Mirko Stojadinović Hybrid of Hill Climbing and SAT Solving for Air Traffic Controller Shift Scheduling Original scientific paper DOI: https://doi.org/10.7251/JIT1502081S Download Article PDF Abstract Modern computers solve many problems by using exact methods, heuristic methods and very often by using their combination. Air Traffic Controller Shift Scheduling Problem has been successfully solved by using SAT technology (reduction to logical formulas) and several models of the problem exist. We present a technique for solving this problem that is a combination of SAT solving and meta-heuristic method hill climbing, and consists of three phases. First, SAT solver is used to generate feasible solution. Then, the hill climbing is used to improve this solution, in terms of number of satisfied wishes of controllers. Finally, SAT solving is used to further improve the found solution by fixing some parts of the solution. Three phases are repeated until optimal solution is found. Usage of exact method (SAT solving) guarantees that the found solution is optimal; usage of meta-heuristic (hill climbing) increases the efficiency in finding good solutions. By using these essentially different ways of solving, we aim to use the best from both worlds. Results indicate that this hybrid technique outperforms previously most efficient developed techniques. Keywords: controller shift schedule, reduction to SAT, hill climbing. 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.
Road Safety Management in Local Communities
Vol. 6 No. 1 (2018): JITA – APEIRON Milenko Čabarkapa, Zoran Ž Avramović Road Safety Management in Local Communities Original scientific paper DOI: https://doi.org/10.7251/JIT16027C Download Article PDF Abstract The research of coordination of activities and responsibility-sharing at the appropriate level of road safety management, conducted by analyzing responses from the prepared Questionnaire, in the period before and after the adoption of the Global Plan for the Decade of action for road safety 2011-2020, showed that the improvement or deterioration of the state of road safety at all levels of management, particularly at the local level within Montenegro, can be directly associated with the achievement of coordination of activities and responsibility sharing for the state of road safety. The aim of the paper is to encourage the development of the road safety system in local communities, basing on a vertical coordination in national and local activities and horizontal coordination in activities at the local level, with the establishment of a responsibility sharing system for the state of road safety in local communities. Keywords: activity, coordination, responsibility, road safety, level of territorial organisation, local community. 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 of the Processing and Selling Insurance Over the Internet
Vol. 6 No. 1 (2018): JITA – APEIRON Dragan Mihić, Branko Latinović, Tomislav Vujinović Model of the Processing and Selling Insurance Over the Internet Original scientific paper DOI: https://doi.org/10.7251/JIT16025M Download Article PDF Abstract The growing demands in providing better services to customers, as well as reducing the cost of the insurance companies; while processing insurance quotes require the use of modern technologies such as the methodology of comparing prices and buying policies through the internet. There is a demand for providing a better customer’s quality of shopping, saving customers time and money and integrate all parameters in insurance companies that are important in calculating and creating insurance price.The current way of exchange – search as integration of data, such as an incident book, would be replaced by a modern automatic search of the database, and use processes that meet all insurance standards. The institutions such as insurance supervisor authorities, state tax office and other institutions will be able to access the data in real-time and receive relevant and accurate information about the insured, the vehicles and the policy.The research and developing model is based on study of regulation laid down by the Agency for supervision of insurance in Bosnia-Herzegovina and the collection of business data from insurance companies. Although tariffs and prices of vehicle insurance are unique for all insurance companies, there are differences in how the businesses are carried in insurance companies. Based on these studies and research the new model is developed and proposed for further development and improvement, integration, processing and sale of insurance policies through the Internet. Keywords: Insurance, insured, bonus, malus, accident, premiums, insurance quote, insurance premium, damage. 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.