ANALYSIS OF STAGES OF DEVELOPMENT, CURRENT STATE AND PROSPECTS OF THE EXPERT SYSTEMS
Vol. 10 No. 1 (2020): JITA – APEIRON Nazila A. Rahimova, Vugar H. Abdullayev ANALYSIS OF STAGES OF DEVELOPMENT, CURRENT STATE AND PROSPECTS OF THE EXPERT SYSTEMS Original scientific paper DOI:https://doi.org/10.7251/JIT2001030R Download Article PDF Abstract The objects of the study are stages of development and modern state. In general terms, expert systems are knowledge- based systems. This paper focuses on the components and principles of expert systems. Expert systems are also described. The components of expert systems include knowledge base, logical impact mechanism, user interface and decision-making. In addition, this article describes the capabilities of expert systems. One challenge is to identify the future prospects of expert systems. The research examined the expert system and its significance. It also focuses on generations of expert systems. The first generation of expert systems includes systems created before 1990. This article discusses SAINT, DENDRAL and HEARSAY-1. The features of this expert systems are also discussed here. First-generation expert systems are research prototypes. As a result, the foundations of artificial intelligence were developed. Mostly first-generation expert systems were used as a passive assistant expert. The second generation of expert systems refers to systems created since 1990. Features of second-generation expert systems include dynamism, interactivity, and processing of disparate knowledge. Unlike first-generation expert systems, these systems are able to test the completeness of the knowledge base, to process fuzzy knowledge. Their main difference is the ability to integrate second- generation expert systems with existing systems. At the moment, statistical and dynamic expert systems are distinguished. This article describes the current status of both types. Here are also discussed the tools of statistical and dynamic expert systems. At the end, possible prospects of expert systems are received. Keywords: expert systems, knowledge-based systems, perspective expert systems. 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 SPATIAL DATA QUALITY IN THE GEOGRAPHICAL INFORMATION SYSTEM OF THE TELECOM OPERATOR
Vol. 10 No. 1 (2020): JITA – APEIRON Dragan Čubrilović, Miloš Ljubojević IMPROVING THE SPATIAL DATA QUALITY IN THE GEOGRAPHICAL INFORMATION SYSTEM OF THE TELECOM OPERATOR Original scientific paper DOI:https://doi.org/10.7251/JIT2001017C Download Article PDF Abstract Spatial data about telecommunication infrastructure facilities represent the inevitable resources of each telecom operator. The precision of collected spatial data, used in the geographic information system (GIS) of telecom operators, is very important, especially when it is about urban environments. In this paper, we have presented the possibility of correcting the positions of telecommunication facilities obtained using the Global Positioning System (GPS). Factors affecting the accuracy and quality of spatial data have been analyzed and solutions for quality improvement proposed. We have shown that using permanent stations can achieve the required level of spatial position correction. A fast and efficient position correction allows updating data in GIS of telecom operators, providing correct, accurate, and timely information about telecom infrastructure. Keywords: GPS, GIS, spatial data quality, differential correction, permanent station. 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.
SOME POSSIBILITIES OF COMPUTER LINGUISTICS ON AN EXAMPLE OF ANALYSIS OF NOVELS
Vol. 10 No. 1 (2020): JITA – APEIRON Nedim Smailović, Zoran Ž. Avramović SOME POSSIBILITIES OF COMPUTER LINGUISTICS ON AN EXAMPLE OF ANALYSIS OF NOVELS Original scientific paper DOI:https://doi.org/ 10.7251/JIT2001005S Download Article PDF Abstract This paper shows some aspects of statistical analysis of well-known novels: Death and the Dervish by Meša Selimović (1966), Autobiography by Branislav Nušić (1924) and In the Registrar’s Office by Ante Kovačić (1888). The goal of the analysis is to point to mutual similarities and differences of statistical data in those texts and to compare them with the up to date findings in that field. A part of the analysis relates to comparison of languages of these writers with today’s language, used by column authors in electronic media. These kinds of researches belong to linguistics, as a science on language, but the results may be used in the contemporary development of artificial intelligence. Keywords: computer linguistics, language, text analysis, visualization of data. 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.
Personalization of Teaching in E-learning Systems
Vol. 10 No. 2 (2020): JITA – APEIRON Boris Ribarić, Zoran Ž. Avramović Personalization of Teaching in E-learning Systems Original scientific paper DOI:https://doi.org/ 10.7251/JIT2002120R Download Article PDF Abstract Personalized teaching offers students the opportunity to study independently, with a focus towards fostering anddeveloping their research traits, to intensively develop students’ abilities and competencies. Traditional teaching is a common mode of education through which tutors use the same teaching method, regardless of the differences and complex personalities of students in a single class or group. Such an approach to teaching has the effect of slowing down the progression of talented students on one hand while making it harder for less successful students to follow classes on the other. The consequences of this approach to teaching are a rapid loss of learning motivation and perception of classes and learning as unpleasant obligations. Contemporary e-learning systems offer personalized learning, by tailoring it to the needs and unique traits of each student. Usage of neural networks in data processing for personalized learning will ensure the formation of adequate classes, full understanding, and adoption of the material prescribed by the curriculum, compliance with the general curriculum, and constant insight into the students’ progress. Keywords: personalized learning, e-learning, neural network. 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.
Assessment of Intelligent Solutions for ImprovingElevators’ Performances
Vol. 10 No. 2 (2020): JITA – APEIRON Kristina Jakimovska, Biljana Stojcevska, Anita Vasileva Assessment of Intelligent Solutions for Improving Elevators’ Performances Original scientific paper DOI:https://doi.org/10.7251/JIT2002112J Download Article PDF Abstract In the process of introduction of information as well as data capabilities the first approach is adding technology that can be used in many spheres for buildings and upgrading apparatus and utensils. However the focus of this study is on the deficiency of current elevators associated with efficiency and debugging of the errors or security systems where we concentrate on the introduction of new trends which advise that elevators should be implemented with intelligent devices. Smart elevators easily provide means to predict and prevent errors and bring the chances of an error to a minimum. Needless to say is that a range of negative effects are unavoidable when it comes to the introduction of new technology. This paper will illustrate both the advantages and the disadvantages of using intelligent devices in elevators and through an analysis of the various options using Multi- Criteria Analysis method perform ranking of the presented solutions. Keywords: elevator, intelligent technology, The Multi-Criteria Decision-Making (MCDM). 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.
Nonlinear Prediction Models in Data Analysis
Vol. 10 No. 2 (2020): JITA – APEIRON Željko Račić, Zoran Ž. Avramović, Đuro Mikić Nonlinear Prediction Models in Data Analysis Original scientific paper DOI:https://doi.org/10.7251/JIT2002106R Download Article PDF Abstract The modern entrepreneurial sensibility of the company’s business implies directing the right information to the appropriate parts of the company at the right time. That is why it is necessary to digitalize processes as much as possible and make the organization “intelligent”, and its human resources, to the greatest extent, the knowledge workers. The application of neural networks, i.e. nonlinear prediction models, enables systematic analysis of data in the function of evaluating the behavior of the system. Neural networks are a powerful tool, especially for forecasting trends and forecasting based on historical data. The grouping method, i.e., the k-mean value algorithm, is used as a precursor to neural networks. Keywords: neural networks, Back-propagation neural network, grouping methods, k-mean algorithm. 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.
Multi-criteria analysis of WWW domain efficiency onsocial behavior in cyber space
Vol. 10 No. 2 (2020): JITA – APEIRON Julijana Vasiljević, Dragan Vasiljević, Boris Ribarić Multi-criteria analysis of WWW domain efficiency on social behavior in cyber space Original scientific paper DOI:https://doi.org/ 10.7251/JIT2002096V Download Article PDF Abstract The level of technological development, as well as technology, allows a contemporary individual to put any possible files, photos or multimedia contents on his internet-connected computer. As a result, nowadays we practically have an enormous amount of data, available to almost any possible individual worldwide. People make connections over Web service throughout internet as visible communication. World Wide Web represents the most prominent internet field thus partly influencing internet users in contemporary world. Defining efficiency of World Wide Web domain within cyber space means a lot to social behavior. This paper deals with estimating efficiency of World Wide Web domain on social affairs in cyber space with the use of multicriteria analysis. Based on the criteria chosen, World Wide Web domain efficiency assessment in cyber space has been conducted, with the emphasis on the influences towards efficiency in the domain of fulfilled influences on social affairs. Identification of such World Wide Web fields facilitates the process of technological progress on one hand or facilitates recognition, prevention and protection of human and material resources on the other hand. World Wide Web domain efficiency in cyber space analysis has been performed through the method of Analytic Hierarchy Process (AHP method), while the efficiency expertise of World Wide Web domain on social behavior in cyber space has been performed within a software tool “Super Decision 2.6.0 – RC1“. For the sake of the comparative data analysis, an “on–line“ survey has been made on a representatvie sample of 148 individuals, applying a fivedegree Likert Scale of attitudes as well as the analysis of obtained data within a software tool used for statistical data processing “Statistical Package for the Social Sciences“. Upon a completion of performed analysis based on an influence significance, the following World Wide Web domains were singled out: Facebook, Youtube, Wikipedia and Twitter. Keywords: World Wide Web domain, cyber space, multi-criteria analysis, AHP method. 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.
Development of Awareness and Competences of Employeesin the Processes of Information Security ManagementSystem
Vol. 10 No. 2 (2020): JITA – APEIRON Vitomir T. Miladinović Development of Awareness and Competences of Employees in the Processes of Information Security Management System Original scientific paper DOI:https://doi.org/10.7251/JIT2002087M Download Article PDF Abstract Based on author’s experiencie, in this we will analyze some issues of awareness and competence development of all employees in the organization in the processes of information security management system (ISMS), in accordance with the requirements of the International Standard SRPS ISO/IEC 27001 Information Technology — Security Techniques — Information Security Management Systems — Requirements. Keywords: data, Secsty, information, awareness, competence. 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.
Use of Electronic Modules on Device for TribologicalResearch in the Field of Plastic Deformation of SlimMetal Sheets
Vol. 10 No. 2 (2020): JITA – APEIRON Tomislav Vujinović, Dragan Mihić, Esad Jakupović Use of Electronic Modules on Device for Tribological Research in the Field of Plastic Deformation of Slim Metal Sheets Original scientific paper DOI:https://doi.org/10.7251/JIT2002081V Download Article PDF Abstract Electronic modules are important components of manufacturing and research equipment in the field of plastic deformation of sheet metal fabrication, as well as in other processes. Depending on the type and complexity of the production or research process, different electronic modules are also used. The indispensable electronic modules in production as well as experimental (research) systems are: encoders, signal processing, A/D and D/A converters, required software of all levels, all the way to large packages for numerical process simulation. This scientific paper presents an original computerized device for testing tribological influences in plastic deformation of slim (thin) sheet metal forming (fabrication), whose control base consists of electronic modules. Some results are also shown as dependencies, obtained by testing on this developed device. Keywords: slim (thin) metal sheet, tribology, plastic deformation, electronic modules. 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.
Scaling complexity comparison of an ACO-based routingalgorithm used as an IoT network core
Vol. 10 No. 2 (2020): JITA – APEIRON Goran Djukanovic, Goran Popovic, Dimitris Kanellopoulos Scaling complexity comparison of an ACO-based routing algorithm used as an IoT network core Original scientific paper DOI:https://doi.org/10.7251/JIT2002073DJ Download Article PDF Abstract This paper proposes a routing method that is based on an Ant Colony Algorithm (ACO) for minimizing energy consumption in Wireless Sensor Networks (WSNs). The routing method is used as the backbone of the Internet of Things (IoT) platform. It also considers the critical design issues of a WSN, such as the energy constraint of sensor nodes, network load balancing, and sensor density in the field. Special attention is paid to the impact of network scaling on the performance of the ACO-based routing algorithm. Keywords: ant colony algorithm (ACO), energy consumption, internet of things (IoT), network lifetime, optimal path, wireless sensor network (WSN). 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.