Annals of Computer Science and Information Systems, Volume 21

DOI: http://dx.doi.org/10.15439/2020F202

Citation: Proceedings of the 2020 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 21, pages 119–128 (2020)

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Abstract. This paper presents a method of generating linguistic summaries of women's menstrual cycles based on the set of concepts describing various aspects of the cycles. These concepts enable description of menstrual cycles that are readable for humans, but they also provide high-level information that can be used as control input for other data processing actions such as e.g. anomaly detection. The labels signifying these concepts are assigned to cycles by means of multivariate time series analysis. The corresponding algorithm is a subsystem of a bigger solution created as a part of an R\&D project.

References

1. L. Bablok, W. Dziadecki, I. Szymusik, and et al., “Patterns of infertility in Poland - multicenter study,” Neuro Endocrinol Lett., vol. 32, no. 6, pp. 799–804, 2011.
2. A. Chadzynska-Krasowska, P. Betlinski, and D. Slezak, “Scalable machine learning with granulated data summaries: A case of feature selection,” in Proceedings of ISMIS 2017, Warsaw, Poland, ser. Lecture Notes in Computer Science, vol. 10352. Springer, 2017, pp. 519–529. [Online]. Available: https://doi.org/10.1007/978-3-319-60438-1_51
3. W. Damian, S. Iwona, W. Miroslaw, and P. Bronislawa, “The impact of endometriosis on the quality of life and the incidence of depression-a cohort study,” Int. J. Environ. Res Public Health, vol. 17, no. 10, p. 3641, 2020.
4. J. Fedorowicz, L. Sosnowski, D. Slezak, I. Szymusik, and et al., “Multivariate ovulation window detection at OvuFriend,” in Proceedings of IJCRS 2019, Debrecen, Hungary, ser. Lecture Notes in Computer Science, vol. 11499. Springer, 2019, pp. 395–408. [Online]. Available: https://doi.org/10.1007/978-3-030-22815-6_31
5. A. Jain, M. Popescu, J. M. Keller, M. Rantz, and B. Markway, “Linguistic summarization of in-home sensor data,” J. Biomed. Informatics, vol. 96, 2019. [Online]. Available: https://doi.org/10.1016/ j.jbi.2019.103247
6. A. Janusz, M. Przyborowski, P. Biczyk, and D. Ślęzak, “Network device workload prediction: A data mining challenge at knowledge pit.” in Proceedings FedCSIS 2020, Sofia, Bulgaria, 2020.
7. J. Kacprzyk and R. R. Yager, “Linguistic summaries of data using fuzzy logic,” International Journal of General Systems, vol. 30, no. 2, pp. 133–154, 2001. [Online]. Available: https://doi.org/10.1080/03081070108960702
8. J. Kacprzyk and S. Zadrozny, “Fuzzy logic-based linguistic summaries of time series: a powerful tool for discovering knowledge on time varying processes and systems under imprecision,” Wiley Interdiscip. Rev. Data Min. Knowl. Discov., vol. 6, no. 1, pp. 37–46, 2016. [Online]. Available: https://doi.org/10.1002/widm.1175
9. W. Kosiński and A. Chwastyk, “Ordered fuzzy numbers in financial stock and accounting problems,” in 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), 2013, pp. 546–551.
10. L. Polkowski and P. Artiemjew, Granular Computing in Decision Approximation - An Application of Rough Mereology, ser. Intelligent Systems Reference Library. Springer, 2015, vol. 77. [Online]. Available: http://dx.doi.org/10.1007/978-3-319-12880-1
11. M. Romaniuk and P. Nowak, “Monte carlo methods : Theory, algorithms and applications to selected financial problems,” Warszawa, 2015.
12. D. Slezak, J. Borkowski, and A. Chadzynska-Krasowska, “Ranking mutual information dependencies in a summary-based approximate analytics framework,” in 2018 International Conference on High Performance Computing & Simulation, HPCS 2018, Orleans, France, July 16-20, 2018. IEEE, 2018, pp. 852–859. [Online]. Available: https://doi.org/10.1109/HPCS.2018.00137
13. D. Slezak, R. Glick, P. Betlinski, and P. Synak, “A new approximate query engine based on intelligent capture and fast transformations of granulated data summaries,” J. Intell. Inf. Syst., vol. 50, no. 2, pp. 385–414, 2018. [Online]. Available: https://doi.org/10.1007/s10844-017-0471-6
14. L. Sosnowski, “Compound objects comparators in application to similarity detection and object recognition,” Trans. Rough Sets, vol. 21, pp. 169–300, 2019. [Online]. Available: https://doi.org/10.1007/978-3-662-58768-3_6
15. L. Sosnowski, I. Szymusik, and T. Penza, “Network of fuzzy comparators for ovulation window prediction,” in Proceedings of IPMU 2020, ser. Communications in Computer and Information Science, vol. 1239. Springer, 2020, pp. 800–813. [Online]. Available: https://doi.org/10.1007/978-3-030-50153-2_59
16. S. Staab and A. Maedche, “Knowledge Portals: Ontologies at Work,” AI Magazine, vol. 22, no. 2, pp. 63–75, 2001.
17. J. Stepaniuk and A. Skowron, “Ontological framework for approximation,” in Proceedings of RSFDGrC 2005, ser. Lecture Notes in Computer Science, vol. 3641. Springer, 2005, pp. 718–727. [Online]. Available: https://doi.org/10.1007/11548669_74
18. M. Świechowski and D. Ślęzak, “Introducing LogDL - Log Description Language for Insights from Complex Data,” in Proceedings FedCSIS 2020, Sofia, Bulgaria, 2020.