doi: 10.17586/1606‑4313‑2020‑19‑4-53-60


Vorobeva A.A., Gerasimov V.V., Li Yu.V.
An algorithm for detecting leaks of insider information of financial markets in investment consulting.



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Article in Russian

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Vorobeva A.A., Gerasimov V.V., Li Yu.V. An algorithm for detecting leaks of insider information of financial markets in investment consulting. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2021, vol. 21, no. 3, pp. 394–400
(in Russian). doi: 10.17586/2226-1494-2021-21-3-394-400


Abstract

Shelf life and quality of raw materials depend on the most favorable conditions for long-term storage. The rational storage time, that is, the longest period at which the loss of quality and nutritional value of the raw material does not exceed the allowable limit, depends on the temperature and humidity conditions. The temperature should be kept as constant as possible throughout the entire storage period, since its fluctuations even within 5 °C can seriously affect the safety of products. On the territory of the Russian Federation, the outdoor air temperature fluctuates during the day by approximately 5 to 15 °C, reaching 30 °C in some regions. The seasonal temperature also changes significantly. The optimal storage limits for starch or pectin-containing raw materials are 0-5 °C. One of the modes of storage of raw materials is storage in a refrigerated state (that is, at the temperature of the raw materials and the surrounding space lowered to –10 °C). Such conditions can be created by equipping storage facilities with facilities for artificial cooling. Using analytical estimates of temperature changes during heat redistribution under the influence of controlled operational factors, we can objectively judge the rational conditions of storage of raw materials and predict storage periods.


Keywords: model, research, heat distribution, starch, pectin, mound of raw materials, controlled, effects.

Acknowledgements. Исследование выполнено при финансовой поддержке Российского фонда фундаментальных исследований в рамках научного проекта (грант № 19-08-00865 А).

References
1. Zhang Z., Lai Z., Xu Y., Shao L., Wu J., Xie G.-S. Discriminative elastic-net regularized linear regression // IEEE Transactions on Image Processing. 2017. V. 26. N 3. P. 1466–1481. doi: 10.1109/TIP.2017.2651396
2. Olive D.J. Linear Regression. Springer, 2017. IX, 494 p. doi: 10.1007/978-3-319-55252-1
3. Xu J., Xu C., Zou B., Tang Y.Y., Peng J., You X. New incremental learning algorithm with support vector machines // IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2019. V. 49. N 11. P. 2230–2241. doi: 10.1109/TSMC.2018.2791511
4. Press S.J., Wilson S. Choosing between logistic regression and discriminant analysis // Journal of the American Statistical Association. 1978. V. 73. N 364. P. 699–705. doi: 10.1080/01621459.1978.10480080
5. Friedman J., Hastie T., Tibshirani R. Additive logistic regression: A statistical view of boosting // Annals of Statistics. 2000. V. 28. N 2. P. 337–407. doi: 10.1214/aos/1016218223
6. Subramaniyaswamy V., Logesh R. Adaptive KNN based recommender system through mining of user preferences // Wireless Personal Communications. 2017. V. 97. N 2. P. 2229–2247. doi: 10.1007/s11277-017-4605-5
7. Cheung D.W., Kao B., Lee J. Discovering user access patterns on the World Wide Web // Knowledge-Based Systems. 1998. V. 10. N 7. P. 463–470. doi: 10.1016/S0950-7051(98)00037-9
8. Liu D.-S., Fan S.-J. A modified decision tree algorithm based on genetic algorithm for mobile user classification problem // The Scientific World Journal. 2014. P. 468324.
9. Santra A., Jayasudha S. Classification of web log data to identify interested users using Naïve Bayesian classification // International Journal of Computer Science Issues (IJCSI). 2012. V. 9. N 1. P. 381.
10. Park S., Suresh N.C., Jeong B.-K. Sequence-based clustering for web usage mining: A new experimental framework and ann-enhanced k-means algorithm // Data & Knowledge Engineering. 2008. V. 65. N 3. P. 512–543. doi: 10.1016/j.datak.2008.01.002
11. Medina-Ortiz D., Contreras S., Quiroz C., Asenjo J.A., Olivera-Nappa Á. DMAKit: A user-friendly web platform for bringing state-of-the-art data analysis techniques to non-specific users // Information Systems. 2020. V. 93. P. 101557. doi: 10.1016/j.is.2020.101557
12. Meroño-Peñuela A. Refining Statistical Data on the Web. CreateSpace Independent Publishing Platform, 2016. 252 p.
13. Nithya P., Sumathi P. Novel pre-processing technique for web log mining by removing global noise and web robots // Proc. of the National Conference on Computing and Communication Systems (NCCCS 2012). 2012. P. 41–45. doi: 10.1109/NCCCS.2012.6412976
14. Kanungo T., Mount D.M., Netanyahu N.S., Piatko C.D., Silverman R., Wu A.Y. An efficient k-means clustering algorithm: Analysis and implementation // IEEE Transactions on Pattern Analysis and Machine Intelligence. 2002. V. 24. N 7. P. 881–892. doi: 10.1109/TPAMI.2002.1017616
15. Yang S.-L., Li Y.-S., Hu X.-X., Pan R.-Y. Optimization study on k value of k-means algorithm // Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice. 2006. V. 26. N 2. P. 97–101. (in Chinese)
16. Syakur M., Khotimah B., Rochman E.M.S., Satoto B.D. Integration k-means clustering method and elbow method for identification of the best customer profile cluster // IOP Conference Series: Materials Science and Engineering. 2018. V. 336. N 1. P. 012017. doi: 10.1088/1757-899X/336/1/012017
17. Thinsungnoen T., Kaoungku N., Durongdumronchai P., Kerdprasop K., Kerdprasop N. The clustering validity with silhouette and sum of squared errors // Proc. 3rd International Conference on Industrial Application Engineering (ICIAE 2015). 2015. P. 44–51. doi: 10.12792/iciae2015.012
18. Menardi G. Density-based Silhouette diagnostics for clustering methods // Statistics and Computing. 2011. V. 21. N 3. P. 295–308. doi: 10.1007/s11222-010-9169-0


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