JITA

JITA Journal of Information Technology and Applications

Vol. 3 No. 1 (2013): JITA - APEIRON

Željko Eremić, Dragica Radosav

Web page characteristics of educational adaptive web sites

Original scientific paper

DOI: https://doi.org/10.7251/JIT1301020E

Abstract

Educational information about single topic may be found on many different website pages. Those web pages may have different roles, such as the display of information related to teaching, teaching content or routing to other web pages. Educational material can be placed on adaptive websites. Adaptive websites can customize their view and the structure on the basis of previously recorded user behavior. Documents on which visitors often end their navigation are called target documents, and users often visit waypost documents before visiting the target documents. Characteristics of different types of documents are being investigated in this paperwork. Also guidelines related to the design of such educational web sites are being provided.

Keywords: Adaptive website, Waypost, Web design.

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.