JITA

JITA Journal of Information Technology and Applications

Vol. 4 No. 2 (2014): JITA - APEIRON

Zoran Bikicki, Ivan Milenković, Dušan Starčević

Using 3D Models for Improving Face Recognition

Original scientific paper

DOI: https://doi.org/10.7251/JIT1402055B

Abstract

Face recognition algorithm Principal Component Analysis (PCA) has a significant performance drop when comparing photographs taken from different angle. In this paper a 3D model was used for improving that performance. Model enables us to transform the face image which is taken from certain angle to en face. Model has been tested against biometric database formed at the Faculty of Organizational Sciences. Image rotation based on the model was performed before matching with the en face images from the database. Study results show that algorithm precision on biometric verification and identification has been seriously improved.

Keywords: biometrics, face recognition, 3D graphics, PCA.

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.