[
1. Borissova, D., D. Keremedchiev. Group Decision Making in Evaluation and Ranking of Students by Extended Simple Multi-Attribute Rating Technique. – Cybernetics and Information Technologies, Vol. 19, 2019, No 3, pp.45-56.10.2478/cait-2019-0025
]Search in Google Scholar
[
2. Hussain, S., N. A. Dahan, F. M. Ba-Alwi, N. Ribata. Educational Data Mining and Analysis of Students’ Academic Performance Using WEKA. – Indonesian Journal of Electrical Engineering and Computer Science, Vol. 9, 2018, No 2, pp. 447-459.10.11591/ijeecs.v9.i2.pp447-459
]Search in Google Scholar
[
3. Jecheva, V., D. Orozova. Ontology-Based Electronic Test Result Evaluation, Advances in Intelligent and Soft Computing. – In: Proc. of 3rd International Conference of Software, Services and Semantic Technologies S3T, Springer, 2011, pp. 213-214. ISSN: 1867-5662.10.1007/978-3-642-23163-6_31
]Search in Google Scholar
[
4. Kabakchieva, D. Predicting Student Performance by Using Data Mining Methods for Classification. – Cybernetics and Information Technologies, Vol. 13, 2013, No 1, pp. 61-72.10.2478/cait-2013-0006
]Search in Google Scholar
[
5. Jang, L. C., T. Kim, D.-W. Park, D. Langova-Orozova. Modelling of an Intelligent Training System by a Generalized Net, Issues in Intuitionistic Fuzzy Sets and Generalized Nets. – K. Atanassov, J. Kacprzyk, M. Krawczak, Eds. Wyzsza Szkola Informatyki Stosowanej i Zarzadvania, Warszawa, Vol. 2, 2004, pp. 17-29.
]Search in Google Scholar
[
6. Mardani, A., A. Jusoh, K. M. D. Nor, Z. Khalifah, N. Zakwan, A. Valipour. Multiple Criteria Decision-Making Techniques and Their Applications – A Review of the Literature from 2000 to 2014. – Economic Research-Ekonomska Istraživanja, Vol. 28, 2015, No 1, pp. 516-571.10.1080/1331677X.2015.1075139
]Search in Google Scholar
[
7. Nandeshwar, A., S. Chaudhari. Enrollment Prediction Models Using Data Mining. 2009. http://nandeshwar.info/wp-content/uploads/2008/11/DMWVU_Project.pdf
]Search in Google Scholar
[
8. Orozova, D. Appropriate e-Test System Selection Model. – Compt. Rend Acad. bulg. Sci., Vol. 72, 2019, No 6, pp. 811-820.10.7546/CRABS.2019.06.14
]Search in Google Scholar
[
9. Orozova, D. Generalized Net Model of Tutoring System, Issues in Intuitionistic. – Fuzzy Sets and Generalized Nets, Vol. 5, 2007, pp. 25-34. ISBN 978-83-88311-90-1.
]Search in Google Scholar
[
10. Orozova, D., K. Atanassov. Model of Big Data Map/Reduce Processing. – Compt. Rend. Acad. bulg. Sci., Vol. 72, 2019, No 11, pp.1537-1545. ISSN 1310-133.
]Search in Google Scholar
[
11. Peneva, V., I. Popchev. Multicriteria Decision Making by Fuzzy Relations and Weighting Functions for the Criteria. – Cybernetics and Information Technologies, Vol. 9, 2009, No 4, pp. 58-71.
]Search in Google Scholar
[
12. Peneva, V., I. Popchev. Fuzzy Multicriteria Decision-Making. – Cybernetics and Information Technologies, Vol. 2, 2002, No 1, pp. 16-26.
]Search in Google Scholar
[
13. Popchev, I., V. Peneva. A Fuzzy Multicriteria Decision Making Algorithm. – In: Proc. of 10th International Conference on Multiple Criteria Decision Making, 19-24 July 1992, Taipei, Vol. II, 1992, pp. 11-16.
]Search in Google Scholar
[
14. Popchev, I., D. Orozova. Towards Big Data Analytics in the e-Learning Space. – Cybernetics and Information Technologies, Vol. 19, 2019, No 3, pp. 16-25.10.2478/cait-2019-0023
]Search in Google Scholar
[
15. Popchev, I., I. Radeva. Risk Analysis – An Instrument for Technology Selection. – Engineering Sciences, Vol. 4, 2019, pp. 5-20. ISSN:1312-5702 (Print).10.7546/EngSci.LVI.19.04.01
]Search in Google Scholar
[
16. Radeva, I. Multi-Criteria Models for Clusters Design. – Cybernetics and Information Technologies, Vol. 13, 2013, No 1, pp. 18-33.10.2478/cait-2013-0003
]Search in Google Scholar
[
17. Schwab, K. The Fourth Industrial Revolution. New York, Crown Publishing Group, 2017. ISBN: 978-5247-5886-8, eBook ISBN: 978-1-5247-5887-5.
]Search in Google Scholar
[
18. Stancheva, N., A. Stoyanova-Doycheva, S. Stoyanov, I. Popchev, V. Ivanova. An Environment for Automatic Test Generation. – Cybernetics and Information Technologies, Vol. 17, 2017, No 2, pp. 183-196.10.1515/cait-2017-0025
]Search in Google Scholar
[
19. Stancheva, N., A. Stoyanova-Doycheva, S. Stoyanov, I. Popchev, V. Ivanova. A Model for Generation of Test Questions. – Compt. Rend. Acad. bulg. Sci., Vol. 70, 2017, No 5, pp. 619-630.
]Search in Google Scholar
[
20. Ross, A. The Industries of the Future. Simon & Schuster. Reprint Edition (7 February, 2017). ISBN-10: 1476753660, ISBN-13: 978-1476753669.
]Search in Google Scholar
[
21. Deep Shift Technology Tipping Points and Societal Impact. Survey Report, World Economic Forum, September 2015, p. 44. http://www3.weforum.org/docs/WEF_GAC15_Technological_Tipping_Points_report_2015.pdf
]Search in Google Scholar
[
22. World Economic Forum. The Global Risks Report. 2019. 14th Edition, p. 107. www.weforum.org.
]Search in Google Scholar
[
23. Orange System [Online]: https://orange.biolab.si/training/introduction-to-data-mining/
]Search in Google Scholar
[
24. Summative and Formative Evaluation. https://ieeexploreieee.org/document/7462444
]Search in Google Scholar
[
25. Policy and Investment Recommendations for Trustworthy Artificial Intelligence [Online]. https://ec.europa.eu/digital-single-market/en/news/policy-and-investment-recommendations-trustworthy-artificial-intelligence
]Search in Google Scholar
[
26. Ethics Guidelines for Trustworthy AI [Online]. https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai
]Search in Google Scholar
[
27. Orozova, D., I. Popchev. Cyber-Physical-Social Systems for Big Data. – In: Proc. of 21st International Symposium on Electrical Apparatus and Technologies SIELA 2020, 3-6 June 2020, Bourgas, Bulgaria (in print).10.1109/SIELA49118.2020.9167161
]Search in Google Scholar