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Vol. 2 No. 1 (2012): JITA - APEIRON

Željko Eremić

History-Enriched Digital Objects as a Factor of Improvement of Adaptive Educational Web Site Navigation

Original scientific paper

DOI: https://doi.org/10.7251/JIT1201032J

Abstract

Modern educational websites offer a wealth of information and content intended for both students and teachers. Such facilities often are not grouped in a single location. While students are in need of fast and efficient access to certain content, teachers are in need for an insight into the learning process of students. By using capabilities of Ajax, it is possible to implement a system for mutual support, where teachers and students who have knowledge of the desired resource would share it with students who are in need of such information in real time. History-enriched digital objects can be used to store information about knowledge sharing. In combination with the records of user’s behavior from the log files, this shared knowledge can make a significant contribution to the successful design and navigation of adaptive web sites. Adaptive web sites can change their content and presentation based on the previously recorded user’s behavior.

Keywords: History-enriched digital objects, Web design, Internet technology, Data mining.

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

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

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.