Kirgizov G.V., Nikitin N.O., Pinchuk M., Iamshchikova L.A., Deeva I., Shakhkyan K., Borisov I.I., Zharkov K.D., Kaluzhnaya, A.V. . Automated Design of Graph-based Models and Structures using Modular Evolutionary Framework. AAAI-24. 2024. pp. in press.
Revin I., Potemkin V., Balabanov N., Nikitin N.O. Automated machine learning approach for time series classification pipelines using evolutionary optimization. Knowledge-Based Systems. 2023. Vol. 268. pp. 110483.
Stebenkov A.S., Nikitin N.O. Automated Generation of Ensemble Pipelines using Policy-Based Reinforcement Learning method. Procedia Computer Science. 2023. Vol. 229. pp. 70-79.
Nikitin N., Pinchuk M., Pokrovskii V., Shevchenko P., Getmanov A., Aksenkin Y., Revin I., Stebenkov A., Poslavskaya E., Kalyuzhnaya A. Integration Of Evolutionary Automated Machine Learning With Structural Sensitivity Analysis For Composite Pipelines. не указано. 2023.
Nikitin N.O., Teryoshkin S., Pokrovskii V., Pakulin S., Nasonov D. Improvement of Computational Performance of Evolutionary AutoML in a Heterogeneous Environment. IEEE Congress on Evolutionary Computation, CEC 2023. 2023. pp. 1-8.
Klimova A., Nasonov D., Hvatov A., Nikitin N.O., Ivanov S.V., Kalyuzhnaya A.V., Boukhanovsky A. Strategic Trends in Artificial Intelligence Through Impact of Computational Science: What Young Scientists Should Expect. Procedia Computer Science. 2023. Vol. 229. pp. 1-7.
Starodubcev N., Nikitin N., Andronova E., Gavaza K., Sidorenko D., Kalyuzhnaya A.V. Generative design of physical objects using modular framework. Engineering Applications of Artificial Intelligence. 2023. Vol. 119. pp. 105715.
Современные методы оптимизации с примерами на Python
Sarafanov M., Pokrovskii V., Nikitin N.O. Evolutionary Automated Machine Learning for Multi-Scale Decomposition and Forecasting of Sensor Time Series. IEEE Congress on Evolutionary Computation, CEC 2022. 2022. pp. 1-8.
Sarafanov M., Nikitin N.O., Kalyuzhnaya A.V. Automated Data-Driven Approach for Gap Filling in the Time Series Using Evolutionary Learning. Advances in Intelligent Systems and Computing. 2022. Vol. 1401. pp. 633-642.
Nikitin N.O., Revin I., Hvatov A., Vychuzhanin P., Kalyuzhnaya A.V. Hybrid and Automated Machine Learning Approaches for Oil Fields Development: the Case Study of Volve Field, North Sea. Computers and Geosciences. 2022. Vol. 161. pp. 105061.
Nikitin N.O., Vychuzhanin P., Sarafanov M., Polonskaia I.S., Revin I., Barabanova I.V., Kaluzhnaya A.V., Boukhanovsky A. Automated Evolutionary Approach for the Design of Composite Machine Learning Pipelines. Future Generation Computer Systems. 2022. Vol. 127. pp. 109-125.
Starodubcev N., Nikitin N.O., Kalyuzhnaya A.V. Surrogate-Assisted Evolutionary Generative Design Of Breakwaters Using Deep Convolutional Networks. IEEE Congress on Evolutionary Computation, CEC 2022. 2022. pp. 1-8.
Sarafanov M.I., Borisova Y., Maslyaev M., Revin I., Maximov G., Nikitin N.O. Short-Term River Flood Forecasting Using Composite Models and Automated Machine Learning: The Case Study of Lena River. Water. 2021. Vol. 13. No. 24. pp. 3482.
Платформа интерактивного построения композитных моделей на основе автоматического машинного обучения
Nikitin N.O., Polonskaia I.S., Kalyuzhnaya A.V., Boukhanovsky A.V. The multi-objective optimisation of breakwaters using evolutionary approach. Proceedings of the 5th International Conference on Maritime Technology and Engineering, MARTECH 2020. 2021. Vol. 2. pp. 767-774.
Borisova J., Aladina A., Nikitin N.O. Hybrid Modelling of Environmental Processes using Composite Models. Procedia Computer Science. 2021. Vol. 193. pp. 256-265.
Barabanova I.V., Vychuzhanin P., Nikitin N.O. Sensitivity Analysis of the Composite Data-Driven Pipelines in the Automated Machine Learning. Procedia Computer Science. 2021. Vol. 193. pp. 484-493.
Nikitin N.O., Vychuzhanin P., Sarafanov M., Polonskaia I.S., Kaluzhnaya A.V. Multi-Purpose Evolutionary AutoML for the Generative Design of Composite Modelling Pipelines. KDD-AutoML Workshop 2021. 2021. pp. 1-6.
Polonskaia I.S., Nikitin N.O., Revin I., Vychuzhanin P., Kaluzhnaya A.V. Multi-Objective Evolutionary Design of Composite Data-Driven Models. IEEE Congress on Evolutionary Computation, CEC 2021. 2021. pp. 926-933.
Hvatov A., Maslyaev M., Polonskaia I.S., Sarafanov M.I., Merezhnikov M., Nikitin N.O. Model-Agnostic Multi-objective Approach for the Evolutionary Discovery of Mathematical Models. Communications in Computer and Information Science. 2021. Vol. 1488. pp. 72-85.
Kalyuzhnaya A.V., Nikitin N.O., Hvatov A., Maslyaev M., Yachmenkov M., Boukhanovsky A.V. Towards generative design of computationally efficient mathematical models with evolutionary learning. Entropy. 2021. Vol. 23. No. 1. pp. 28.
Оценка чувствительности композитных моделей в рамках фреймворка автоматического машинного обучения
Интерактивный анализ и визуализация процессов идентификации композитных моделей
Deeva I., Bubnova A., Andriushchenko P.D., Voskresenskiy A., Bukhanov N.V., Nikitin N.O., Kalyuzhnaya A.V. Oil and Gas Reservoirs Parameters Analysis Using Mixed Learning of Bayesian Networks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2021. Vol. 12742. pp. 394-407.
Nikitin N.O., Hvatov A., Polonskaia I.S., Kalyuzhnaya A.V., Grigorev G., Wang X., Qian X. Generative design of microfluidic channel geometry using evolutionary approach. GECCO 2021 - Proceedings of the Genetic and Evolutionary Computation Conference Companion. 2021. pp. 59-60.
Polonskaia I.S., Aliev I.R., Nikitin N.O. Automated Evolutionary Design of CNN Classifiers for Object Recognition on Satellite Images. Procedia Computer Science. 2021. Vol. 193. pp. 210-219.
Применение методов автоматического машинного обучения для прогнозирования временных рядов
Sarafanov M., Kazakov E.E., Nikitin N.O., Kalyuzhnaya A.V. A Machine Learning Approach for Remote Sensing Data Gap-Filling with Open-Source Implementation: An Example Regarding Land Surface Temperature, Surface Albedo and NDVI. Remote Sensing. 2020. Vol. 12. No. 23. pp. 3865.
Никитин Н.О., Полонская Я.С., Калюжная А.В. Интеллектуальное проектирование защитных сооружений на шельфе с применением моделей морской среды и методов оптимизации. Комплексные исследования Мирового океана: материалы V Всероссийской научной конференции молодых ученых (Калининград, 18-22мая 2020г.). 2020. С. 141-142.
Калюжная А.В., Никитин Н.О., Вычужанин П.В., Хватов А.А. Технологии прикладного искусcтвенного интеллекта в задачах численного моделирования процессов в океане. Комплексные исследования Мирового океана: материалы V Всероссийской научной конференции молодых ученых (Калининград, 18-22мая 2020г.). 2020. С. 81-82.
Nikitin N.O., Polonskaia I.S., Vychuzhanin P., Barabanova I.V., Kaluzhnaya A.V. Structural Evolutionary Learning for Composite Classification Models. Procedia Computer Science. 2020. Vol. 178. pp. 414-423.
Kaluzhnaya A.V., Nikitin N.O., Vychuzhanin P., Hvatov A., Boukhanovsky A.V. Automatic Evolutionary Learning of Composite Models With Knowledge Enrichment. GECCO 2020 - Proceedings of the Genetic and Evolutionary Computation Conference Companion. 2020. pp. 43-44.
Применение методов машинного обучения для заполнения пропусков в данных дистанционного зондирования
Nikitin N.O., Deeva I., Vychuzhanin P., Kalyuzhnaya A.V., Hvatov A., Kovalchuk S.V. Deadline-driven approach for multi-fidelity surrogate-assisted environmental model calibration: SWAN wind wave model case study. GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion. 2019. pp. 1583-1591.
Deeva I., Nikitin N.O., Kalyuzhnaya A.V. Pattern Recognition in Non-Stationary Environmental Time Series Using Sparse Regression. Procedia Computer Science. 2019. Vol. 156. pp. 357-366.
Vychuzhanin P., Nikitin N.O., Kalyuzhnaya A.V. Robust Ensemble-Based Evolutionary Calibration of the Numerical Wind Wave Model. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2019. Vol. 11536. pp. 614-627.
Khvatov A.A., Nikitin N., Kaluzhnaya A.V., Kosukhin S.S. Adaptation of NEMO-LIM3 model for multigrid high-resolution Arctic simulation. Ocean Modelling. 2019. Vol. 141. pp. 101427.
Kovalchuk S.V., Kisliakovskii I.O., Metsker O.G., Nikitin N.O., Funkner A.A., Kalyuzhnaya A.V., Vaganov D.A., Bochenina K.O. Towards management of complex modeling through a hybrid evolutionary identification. GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion. 2018. pp. 255-256.
Kovalchuk S.V. ., Metsker O.G., Funkner A.A., Kisliakovskii I.O., Nikitin N.O., Kalyuzhnaya A.V., Vaganov D.A., Bochenina K.O. A Conceptual Approach to Complex Model Management with Generalized Modelling Patterns and Evolutionary Identification. Complexity. 2018. pp. 5870987.
Nikitin N.O., Kalyuzhnaya A.V., Bochenina K., Kudryashov A., Uteuov A., Derevitskii I., Boukhanovsky A.V. Evolutionary ensemble approach for behavioral credit scoring. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2018. Vol. 10862. pp. 825-831.
Araya-Lopez J., Nikitin N.O., Kalyuzhnaya A.V. Case-adaptive ensemble technique for met-ocean data restoration. Procedia Computer Science. 2018. Vol. 136. pp. 311-320.
Эволюционный подход к управлению качеством ансамблевых моделей
Kalyuzhnaya A.V., Nikitin N.O., Butakov N.A., Nasonov D.A. Precedent-based approach for the identification of deviant behavior in social media. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2018. Vol. 10862. pp. 846-852.
Никитин Н.О. Программный комплекс для моделирования синтетических циклонов. Свидетельство о регистрации программы для ЭВМ. 2017. Т. 2017616937. № от 20.06.2017.
Noymanee J., Nikitin N.O., Kaluzhnaya A.V. Urban Pluvial Flood Forecasting using Open Data with Machine Learning Techniques in Pattani Basin. Procedia Computer Science. 2017. Vol. 119. pp. 288-297.
Nikitin N.O., Spirin D.S., Visheratin A.A., Kalyuzhnaya A.V. Statistics-based models of flood-causing cyclones for the Baltic Sea region. Procedia Computer Science. 2016. Vol. 101. pp. 272–281.
Использование индексирования для аннотирования документов
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Греция, Ираклион
Российская Федерация, Санкт-Петербург