Data mining в системе управленческих навыков
в приложении к сфере гражданского судостроения
Аннотация
В статье рассмотрены современные методы и инструменты искусственного интеллекта в приложении к решению прикладных задач сферы российского гражданского судостроения. Приведен пример алгоритма конструирования экспериментальной модели искусственного интеллекта и дана ее математическая формализация контекстно к прогнозированию динамики показателей развития предприятий и организаций отечественной судостроительной промышленности.
Литература
1. Severin R. An approach to digitalization at Russian high-tech shipbuilding enterprises. Aktualnuye nauchnuye isledovanuya v sovremennom myre = Actual scientific research in the modern world. 2021; (4-5):207-209. Available at: https://www.elibrary.ru/item.asp?id=46280878 (accessed 27.02.2022). (In Russ., abstract in Eng.)
2. Marchenko S.S., Marzhokhov A.V. Prospects for the development of domestic civil shipbuilding. Nedelja nauki Sankt-Peterburgskogo gosudarstvennogo morskogo tehnicheskogo universiteta = Science Week of the St. Petersburg State Marine Technical University. 2020; 1(3-1):1-51. (In Russ., abstract in Eng.) doi: https://doi.org/10.52899/9785883036070_75
3. Taranov A., Skulyabin M., Alekseev Y. Digitization of KSRC activities: approaches and fields. Trudy Krylovskogo gosudarstvennogo nauchnogo centra = Transactions of the Krylov State Research Center. 2019; Special Edition 2: 233-238. (In Russ., abstract in Eng.) doi: https://doi.org/10.24937/2542-2324-2019-2-S-I-233-238
4. Mayorova K., Mamadzharova T. The relevance of the introduction of digital technologies in the shipbuilding industry of the Russian Federation. Nedelja nauki Sankt-Peterburgskogo gosudarstvennogo morskogo tehnicheskogo universiteta = Science Week of the St. Petersburg State Marine Technical University. 2019; 1(1):1-32. Available at: https://www.elibrary.ru/item.asp?id=42198900 (accessed 27.02.2022). (In Russ., abstract in Eng.)
5. Gorin E.A. Digital technology in the national shipbuilding. Bulyeten nauki i praktitki = Bulletin of Science and Practice. 2017; (11):236‑242. (In Russ., abstract in Eng.) doi: https://doi.org/10.5281/zenodo.1048457
6. Dmitriev N.D. Digital Transformation of Shipbuilding. Strategii biznesa = Business Strategies. 2017; (10):15-18. Available at: https://www.elibrary.ru/item.asp?id=42405077 (accessed 27.02.2022). (In Russ., abstract in Eng.)
7. Hopfield J. Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences of the United States of America. 1982; 79(8):2554-2558. (In Eng.) doi: https://doi.org/10.1073/pnas.79.8.2554
8. Fukushima K. Neocognitron capable of incremental learning. Neural Networks. 2004; 17(1):37-46. (In Eng.) doi: https://doi.org/10.1016/S0893-6080(03)00078-9
9. Fukushima K. Neocognitron for handwritten digit recognition. Neurocomputing. 2003; 51:161-180. (In Eng.) doi: https://doi.org/10.1016/S0925-2312(02)00614-8
10. Specht D. Probabilistic neural networks. Neural Networks. 1990; 3(1):109-118. (In Eng.) doi: https://doi.org/10.1016/0893-6080(90)90049-Q
11. Specht D. A General regression neural network. IEEE Transactions on Neural Networks and Learning Systems. 1991; 2(6):568-576. (In Eng.) doi: https://doi.org/10.1109/72.97934
12. Schløler H., Hartmann U. Mapping Neural Network Derived from the Parzen Window Estimator. Neural Networks. 1992; 5(6):903-909. (In Eng.) doi: https://doi.org/10.1016/S0893-6080(05)80086-3
13. Specht D. The General Regression Neural Network – Rediscovered. Neural Networks. 1993; 6(7):1033-1034. (In Eng.) doi: https://doi.org/10.1016/S0893-6080(09)80013-0
14. Iiduka H., Kobayashi Y. Training Deep Neural Networks Using Conjugate Gradient-like Methods. Electronics. 2020; 9(11):1809. (In Eng.) doi: https://doi.org/10.3390/electronics9111809
15. Podvalny S., Mugatina V., Vasiljev E. Application of Faceted Neural Networks to Solving the Pattern Recognition Problem. In: Kravets A.G., Bolshakov A.A., Shcherbakov M.V. (eds.). Cyber-Physical Systems. Studies in Systems, Decision and Control. Vol. 350. Springer, Cham; 2021. p. 237-247. (In Eng.) doi: https://doi.org/10.1007/978-3-030-67892-0_20
16. Parkhomenko S.S., Ledeneva T.M. Scheduling in volunteer computing networks, based on neural network prediction of the job execution time. International Journal of Parallel, Emergent and Distributed Systems. 2019; 34(4):430-447. (In Eng.) doi: https://doi.org/10.1080/17445760.2018.1496435
17. Cybenko G. Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals and Systems. 1989; 2(4):303-314. (In Eng.) doi: https://doi.org/10.1007/BF02551274
18. Kitova O.V., Kitov V.A. Neural Networks and Algorithms for Modern IT Systems. International Journal of Advanced Trends in Computer Science and Engineering. 2020; 9(4):5796-5801. (In Eng.) doi: https://doi.org/10.30534/ijatcse/2020/237942020
19. Kozhevnikova T., Manzhula I. Simulation Modeling Using Neural Networks to Control Complex Technical Systems. In: Mottaeva A. (ed.) Technological Advancements in Construction. Lecture Notes in Civil Engineering. Vol. 180. Springer, Cham; 2022. p. 149-158. (In Eng.) doi: https://doi.org/10.1007/978-3-030-83917-8_14
20. Lee H., Chen C., Huang T. Learning efficiency improvement of back-propagation algorithm by error saturation prevention method. Neurocomputing. 2001; 41(1-4):125-143. (In Eng.) doi: https://doi.org/10.1016/S0925-2312(00)00352-0
21. Yakushin Y., Bereza A., Dmitrienko N. Neural network model for forecasting statistics of communities of social networks. Modern Science. 2020; (5-1):494-499. Available at: https://elibrary.ru/item.asp?id=42847053 (accessed 27.02.2022). (In Eng.)
22. Yemelyanov V., Yemelyanova N., Chernyi S., Varadarajan V. Аpplication of neural networks to forecast changes in the technical condition of critical production facilities. Computers & Electrical Engineering. 2021; 93(C):107225. (In Eng.) doi: https://doi.org/10.1016/j.compeleceng.2021.107225
23. Otsokov K.A. Using Artificial Neural Networks in assessing the cost of Construction Projects. International Journal of Advanced Trends in Computer Science and Engineering. 2020; 9(3):3191-3195. (In Eng.) doi: https://doi.org/10.30534/ijatcse/2020/109932020
24. Peña-Ayala A. Educational data mining: A survey and a data mining-based analysis of recent works. Expert Systems with Applications. 2014; 41(4-1):1432-1462. (In Eng.) doi: https://doi.org/10.1016/j.eswa.2013.08.042
25. Istratova E., Sin D., Strokin K. A Comparative Analysis of Data Mining Analysis Tools. In: Pattnaik P.K., Sain M., Al-Absi A.A., Kumar P. (eds.). Proceedings of International Conference on Smart Computing and Cyber Security. SMARTCYBER 2020. Lecture Notes in Networks and Systems. Vol. 149. Springer, Singapore; 2021. p. 165-172. (In Eng.) doi: https://doi.org/10.1007/978-981-15-7990-5_16
26. Smyth P., Pregibon D., Faloutsos C. Data-driven evolution of data mining algorithms. Communications of the ACM. 2002; 45(8):33-37. (In Eng.) doi: https://doi.org/10.1145/545151.545175
27. Wang W. Optimization of Intelligent Data Mining Technology in Big Data Environment. Journal of Advanced Computational Intelligence and Intelligent Informatics. 2019; 23(1):129-133. (In Eng.) doi: https://doi.org/10.20965/jaciii.2019.p0129
28. Kotenko I., Saenko I., Branitskiy A. Machine Learning and Big Data Processing for Cybersecurity Data Analysis. In: Sikos L., Choo K.K. (eds.). Data Science in Cybersecurity and Cyberthreat Intelligence. Intelligent Systems Reference Library. Vol. 177. Springer, Cham; 2020. P. 61-85. (In Eng.) doi: https://doi.org/10.1007/978-3-030-38788-4_4
29. Tsyrelchuk I.N., Mamatova N.M., Abdul-Azalova M.Y. Optimization of business processes via Big Data. Proceedings of the VI International Conference on BIG DATA and Advanced Analytics. Bestprint, Minsk; 2020. No. 6-1. p. 96-104. Available at: https://libeldoc.bsuir.by/bitstream/123456789/39047/1/Tsyrelchuk_Optimization.pdf (accessed 27.02.2022). (In Eng.)
30. Bova V.V., Kureichik V.V., Scheglov S.N., Kureichik L.V. Multi-level Ontological Model of Big Data Processing. In: Abraham A., Kovalev S., Tarassov V., Snasel V., Sukhanov A. (eds.). Proceedings of the Third International Scientific Conference "Intelligent Information Technologies for Industry". IITI'18 2018. Advances in Intelligent Systems and Computing. Vol. 874. Springer, Cham; 2019. p. 171-181. (In Eng.) doi: https://doi.org/10.1007/978-3-030-01818-4_17
31. Long C.K., Agrawal R., Trung H.Q., Pham H.V. A big data framework for E-Government in Industry 4.0. Open Computer Science. 2021; 11(1):461-479. (In Eng.) doi: https://doi.org/10.1515/comp-2020-0191
32. Dezi L., Santoro G., Gabteni H., Pellicelli A.C. The role of big data in shaping ambidextrous business process management: Case studies from the service industry. Business Process Management Journal. 2018; 24(5):1163-1175. (In Eng.) doi: https://doi.org/10.1108/BPMJ-07-2017-0215
33. Wang L., Wang G. Big Data in Cyber-Physical Systems, Digital Manufacturing and Industry 4.0. International Journal of Engineering and Manufacturing. 2016; 6(4):1-8. (In Eng.) doi: https://doi.org/10.5815/ijem.2016.04.01
2. Marchenko S.S., Marzhokhov A.V. Prospects for the development of domestic civil shipbuilding. Nedelja nauki Sankt-Peterburgskogo gosudarstvennogo morskogo tehnicheskogo universiteta = Science Week of the St. Petersburg State Marine Technical University. 2020; 1(3-1):1-51. (In Russ., abstract in Eng.) doi: https://doi.org/10.52899/9785883036070_75
3. Taranov A., Skulyabin M., Alekseev Y. Digitization of KSRC activities: approaches and fields. Trudy Krylovskogo gosudarstvennogo nauchnogo centra = Transactions of the Krylov State Research Center. 2019; Special Edition 2: 233-238. (In Russ., abstract in Eng.) doi: https://doi.org/10.24937/2542-2324-2019-2-S-I-233-238
4. Mayorova K., Mamadzharova T. The relevance of the introduction of digital technologies in the shipbuilding industry of the Russian Federation. Nedelja nauki Sankt-Peterburgskogo gosudarstvennogo morskogo tehnicheskogo universiteta = Science Week of the St. Petersburg State Marine Technical University. 2019; 1(1):1-32. Available at: https://www.elibrary.ru/item.asp?id=42198900 (accessed 27.02.2022). (In Russ., abstract in Eng.)
5. Gorin E.A. Digital technology in the national shipbuilding. Bulyeten nauki i praktitki = Bulletin of Science and Practice. 2017; (11):236‑242. (In Russ., abstract in Eng.) doi: https://doi.org/10.5281/zenodo.1048457
6. Dmitriev N.D. Digital Transformation of Shipbuilding. Strategii biznesa = Business Strategies. 2017; (10):15-18. Available at: https://www.elibrary.ru/item.asp?id=42405077 (accessed 27.02.2022). (In Russ., abstract in Eng.)
7. Hopfield J. Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences of the United States of America. 1982; 79(8):2554-2558. (In Eng.) doi: https://doi.org/10.1073/pnas.79.8.2554
8. Fukushima K. Neocognitron capable of incremental learning. Neural Networks. 2004; 17(1):37-46. (In Eng.) doi: https://doi.org/10.1016/S0893-6080(03)00078-9
9. Fukushima K. Neocognitron for handwritten digit recognition. Neurocomputing. 2003; 51:161-180. (In Eng.) doi: https://doi.org/10.1016/S0925-2312(02)00614-8
10. Specht D. Probabilistic neural networks. Neural Networks. 1990; 3(1):109-118. (In Eng.) doi: https://doi.org/10.1016/0893-6080(90)90049-Q
11. Specht D. A General regression neural network. IEEE Transactions on Neural Networks and Learning Systems. 1991; 2(6):568-576. (In Eng.) doi: https://doi.org/10.1109/72.97934
12. Schløler H., Hartmann U. Mapping Neural Network Derived from the Parzen Window Estimator. Neural Networks. 1992; 5(6):903-909. (In Eng.) doi: https://doi.org/10.1016/S0893-6080(05)80086-3
13. Specht D. The General Regression Neural Network – Rediscovered. Neural Networks. 1993; 6(7):1033-1034. (In Eng.) doi: https://doi.org/10.1016/S0893-6080(09)80013-0
14. Iiduka H., Kobayashi Y. Training Deep Neural Networks Using Conjugate Gradient-like Methods. Electronics. 2020; 9(11):1809. (In Eng.) doi: https://doi.org/10.3390/electronics9111809
15. Podvalny S., Mugatina V., Vasiljev E. Application of Faceted Neural Networks to Solving the Pattern Recognition Problem. In: Kravets A.G., Bolshakov A.A., Shcherbakov M.V. (eds.). Cyber-Physical Systems. Studies in Systems, Decision and Control. Vol. 350. Springer, Cham; 2021. p. 237-247. (In Eng.) doi: https://doi.org/10.1007/978-3-030-67892-0_20
16. Parkhomenko S.S., Ledeneva T.M. Scheduling in volunteer computing networks, based on neural network prediction of the job execution time. International Journal of Parallel, Emergent and Distributed Systems. 2019; 34(4):430-447. (In Eng.) doi: https://doi.org/10.1080/17445760.2018.1496435
17. Cybenko G. Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals and Systems. 1989; 2(4):303-314. (In Eng.) doi: https://doi.org/10.1007/BF02551274
18. Kitova O.V., Kitov V.A. Neural Networks and Algorithms for Modern IT Systems. International Journal of Advanced Trends in Computer Science and Engineering. 2020; 9(4):5796-5801. (In Eng.) doi: https://doi.org/10.30534/ijatcse/2020/237942020
19. Kozhevnikova T., Manzhula I. Simulation Modeling Using Neural Networks to Control Complex Technical Systems. In: Mottaeva A. (ed.) Technological Advancements in Construction. Lecture Notes in Civil Engineering. Vol. 180. Springer, Cham; 2022. p. 149-158. (In Eng.) doi: https://doi.org/10.1007/978-3-030-83917-8_14
20. Lee H., Chen C., Huang T. Learning efficiency improvement of back-propagation algorithm by error saturation prevention method. Neurocomputing. 2001; 41(1-4):125-143. (In Eng.) doi: https://doi.org/10.1016/S0925-2312(00)00352-0
21. Yakushin Y., Bereza A., Dmitrienko N. Neural network model for forecasting statistics of communities of social networks. Modern Science. 2020; (5-1):494-499. Available at: https://elibrary.ru/item.asp?id=42847053 (accessed 27.02.2022). (In Eng.)
22. Yemelyanov V., Yemelyanova N., Chernyi S., Varadarajan V. Аpplication of neural networks to forecast changes in the technical condition of critical production facilities. Computers & Electrical Engineering. 2021; 93(C):107225. (In Eng.) doi: https://doi.org/10.1016/j.compeleceng.2021.107225
23. Otsokov K.A. Using Artificial Neural Networks in assessing the cost of Construction Projects. International Journal of Advanced Trends in Computer Science and Engineering. 2020; 9(3):3191-3195. (In Eng.) doi: https://doi.org/10.30534/ijatcse/2020/109932020
24. Peña-Ayala A. Educational data mining: A survey and a data mining-based analysis of recent works. Expert Systems with Applications. 2014; 41(4-1):1432-1462. (In Eng.) doi: https://doi.org/10.1016/j.eswa.2013.08.042
25. Istratova E., Sin D., Strokin K. A Comparative Analysis of Data Mining Analysis Tools. In: Pattnaik P.K., Sain M., Al-Absi A.A., Kumar P. (eds.). Proceedings of International Conference on Smart Computing and Cyber Security. SMARTCYBER 2020. Lecture Notes in Networks and Systems. Vol. 149. Springer, Singapore; 2021. p. 165-172. (In Eng.) doi: https://doi.org/10.1007/978-981-15-7990-5_16
26. Smyth P., Pregibon D., Faloutsos C. Data-driven evolution of data mining algorithms. Communications of the ACM. 2002; 45(8):33-37. (In Eng.) doi: https://doi.org/10.1145/545151.545175
27. Wang W. Optimization of Intelligent Data Mining Technology in Big Data Environment. Journal of Advanced Computational Intelligence and Intelligent Informatics. 2019; 23(1):129-133. (In Eng.) doi: https://doi.org/10.20965/jaciii.2019.p0129
28. Kotenko I., Saenko I., Branitskiy A. Machine Learning and Big Data Processing for Cybersecurity Data Analysis. In: Sikos L., Choo K.K. (eds.). Data Science in Cybersecurity and Cyberthreat Intelligence. Intelligent Systems Reference Library. Vol. 177. Springer, Cham; 2020. P. 61-85. (In Eng.) doi: https://doi.org/10.1007/978-3-030-38788-4_4
29. Tsyrelchuk I.N., Mamatova N.M., Abdul-Azalova M.Y. Optimization of business processes via Big Data. Proceedings of the VI International Conference on BIG DATA and Advanced Analytics. Bestprint, Minsk; 2020. No. 6-1. p. 96-104. Available at: https://libeldoc.bsuir.by/bitstream/123456789/39047/1/Tsyrelchuk_Optimization.pdf (accessed 27.02.2022). (In Eng.)
30. Bova V.V., Kureichik V.V., Scheglov S.N., Kureichik L.V. Multi-level Ontological Model of Big Data Processing. In: Abraham A., Kovalev S., Tarassov V., Snasel V., Sukhanov A. (eds.). Proceedings of the Third International Scientific Conference "Intelligent Information Technologies for Industry". IITI'18 2018. Advances in Intelligent Systems and Computing. Vol. 874. Springer, Cham; 2019. p. 171-181. (In Eng.) doi: https://doi.org/10.1007/978-3-030-01818-4_17
31. Long C.K., Agrawal R., Trung H.Q., Pham H.V. A big data framework for E-Government in Industry 4.0. Open Computer Science. 2021; 11(1):461-479. (In Eng.) doi: https://doi.org/10.1515/comp-2020-0191
32. Dezi L., Santoro G., Gabteni H., Pellicelli A.C. The role of big data in shaping ambidextrous business process management: Case studies from the service industry. Business Process Management Journal. 2018; 24(5):1163-1175. (In Eng.) doi: https://doi.org/10.1108/BPMJ-07-2017-0215
33. Wang L., Wang G. Big Data in Cyber-Physical Systems, Digital Manufacturing and Industry 4.0. International Journal of Engineering and Manufacturing. 2016; 6(4):1-8. (In Eng.) doi: https://doi.org/10.5815/ijem.2016.04.01
Опубликована
2022-03-31
Как цитировать
KIRILCHUK, Svetlana Petrovna; KNYAZEVA, Daria Sergeevna.
Data mining в системе управленческих навыков.
Современные информационные технологии и ИТ-образование, [S.l.], v. 18, n. 1, p. 98-106, mar. 2022.
ISSN 2411-1473. Доступно на: <http://sitito.cs.msu.ru/index.php/SITITO/article/view/836>. Дата доступа: 24 nov. 2024
doi: https://doi.org/10.25559/SITITO.18.202201.98-106.
Раздел
Цифровая трансформация транспорта
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