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Pseudo-random number generator based on neural network
P. 568–572.
Gostev I. M.
Aleksei Samarin, Aleksei Toropov, Dzestelova A. et al., , in: Proceedings of the 35th Conference of Open Innovations Association FRUCT, 24-26 April 2024, Tampere, FinlandIssue 1.: FRUCT Oy, 2024. P. 659–664.
This research endeavor is dedicated to the integration of specialized attentional mechanisms within the intricate web of deep neural network architectures aimed at discerning indications of lung carcinoma from monochromatic snapshots derived from computerized axial tomography. Within this exploration, we propose a myriad of adaptations to the traditional non-local blocks, infusing them with bespoke attentional ...
Added: September 19, 2026
Wind Turbines Surface Damage Automatic Detection Using YOLOv8 with Specialized Backbone Modification
Aleksei Samarin, Mamaeva A., Aleksei Toropov et al., , in: Proceedings of the 36th Conference of Open Innovations Association FRUCT, Helsinki, Finland, 30 October - 1 November 2024Vol. 36.: FRUCT Oy, 2024. P. 702–710.
This work is devoted to incorporating specialized self-attention blocks into deep neural network-based models for detecting and quantifying damage across various components of wind turbines using images captured by unmanned aerial vehicle cameras. In our study, we introduce YOLOv8 backbone modification using a specialized self-attention mechanism, tailored to the specific characteristics of the input data. ...
Added: September 19, 2026
Lebedev O. B., Шмелева А. Г., Гежа Н. С., Информатика и автоматизация (Труды СПИИРАН) 2026 Т. 25 № 3 С. 720–750
This paper describes the development of a neural network model for automated analysis of medical data in electrophysiology based on deep learning methods. The relevance of this work stems from the growing need to improve the objectivity, speed, and accuracy of processing complex spatiotemporal signals, such as ECG or EEG. Convolutional neural networks (CNNs), which ...
Added: September 10, 2026
Vyacheslav D. Neverov, Lukyanov A., Andrey V. Krasavin et al., Physical Review B: Condensed Matter and Materials Physics 2026 Vol. 113 No. 2 Article 024515
We develop a neural network approach to solve the self-consistent Bogoliubov-de Gennes equations in strongly disordered s-wave superconductors. The method accurately reproduces inhomogeneous gap distributions and generalizes to system sizes far larger than those used in training. It reduces computational scaling from O(N6 ) to O(N2), enabling quantitative analysis of percolation phenomena and the superconductor-insulator ...
Added: September 5, 2026
Khodadoust J., Kulikova S., Khodadoust F., Biomedical Signal Processing and Control 2027 Vol. 129 Article 111284
Acute ischemic stroke (AIS) analysis from two-dimensional (2D) clinical imaging is hindered by uncontrolled slice tilt and geometric inconsistencies that violate the assumptions of pose-agnostic deep learning (DL) models. This paper proposes a unified geometry-aware, frequency-domain framework for tilted slice localization and ischemic stroke segmentation that explicitly decouples pose estimation from lesion analysis. The method ...
Added: September 2, 2026
Seul: PMLR, 2026.
Added: June 4, 2026
Chertopolokhov V., Mukhamedov A., Bugriy G. et al., IEEE Access 2026 Vol. 14 P. 14369–14392
This study presents on-the-fly identification and multi-step prediction of nonlinear systems with delayed inputs using a dynamic neural network combined with a smooth projection onto ellipsoids. The projection enforces parameter constraints that guarantee stability, while a Lyapunov–Krasovskii analysis yields computable ultimate error bounds. Riccati-type matrix inequalities are derived, providing an efficient vectorization–projection–devectorization implementation suitable for ...
Added: May 22, 2026
Ролинский С. О., Dvoynikova A., В кн.: Альманах научных работ молодых ученых Университета ИТМОТ. 2.: Университет ИТМО, 2022. С. 336–340.
В работе рассмотрены основные существующие подходы к автоматическому распознаванию речи, а также проводится сравнительный анализ открытых компьютерных систем распознавания речи. Для экспериментальных исследований эффективности работы рассматриваемых систем используется речевой корпус LibriSpeech. ...
Added: April 24, 2026
Dvoynikova A., Садикова А. А., В кн.: Сборник трудов X Конгресса молодых ученыхТ. 1.: Университет ИТМО, 2021.
В работе рассматривается применение различных планировщиков обучения (англ. scheduler) нейронных сетей для задачи текстонезависимой верификации дикторов. Для экспериментальных исследований использовалась база данных VoxCeleb1, которая содержит в себе различные речевые высказывания 1211 дикторов. В работе проводился анализ влияния различных планировщиков обучения нейронных сетей, представленных в библиотеке PyTorch языка программирования Python, а также 2 алгоритма планировщика, представленных ...
Added: April 24, 2026
Alexander Demidovskij, Artyom Tugaryov, Igor Salnikov et al., , in: PRICAI 2025: Trends in Artificial Intelligence: 22nd Pacific Rim International Conference on Artificial Intelligence, PRICAI 2025, Wellington, New Zealand, November 17–21, 2025, Proceedings, Part IIIVol. 16453.: Springer, 2026. P. 603–612.
The backpropagation method is the predominant method for pre-training and fine-tuning of Large Language models. At the same time, it is considerably demanding in terms of memory and hardware. Therefore, it makes fine-tuning and pre-training very expensive, harmful for the environment due to the large carbon footprint, and raises the blocks for the development of ...
Added: April 21, 2026
Springer, 2026.
This proceedings contain the papers presented at the 22nd Pacific Rim International Conference on Artificial Intelligence (PRICAI), held on November 17–21, 2025 in Wellington, New Zealand. PRICAI 2025 was co-hosted with the 40th International Conference on Image and Vision Computing New Zealand (IVCNZ 2025) and the annual conference of the New Zealand Artificial Intelligence Researchers ...
Added: April 21, 2026
Efremov A., Portnoy S., Волошин А. Д., Первая миля 2025 № 8 С. 20–28
Выполнен комплексный обзор методов машинного обучения (ML), применяемых для повышения устойчивости сигнала к помехам в каналах связи. Бурное развитие поколений беспроводной связи, активная разработка концепции 6G предъявляют высокие требования к задержке, скорости и надежности передачи данных. Традиционные подходы к защите от помех, основанные на строгих аналитических моделях, зачастую не справляются с хаотичной природой плотных гетерогенных ...
Added: April 4, 2026
Bernadotte A, Elfimov N., Menshikov I., Scientific data 2025 Vol. 13 No. 41
Accurate segmentation of brain vessels in magnetic resonance angiography (MRA) is essential for surgical procedures. Neural networks are powerful tools for medical image segmentation, but their development requires well-annotated datasets. However, publicly available MRA datasets with detailed vessel annotations are scarce. We present a dataset of 100 manually annotated brain MRA images from the IXI ...
Added: February 25, 2026
Antipkina I., Иванущенко А. В., Калабина И. А. et al., Мир психологии. Научно-методический журнал 2025 № 4(123) С. 295–316
Low-quality test items pose significant risks of biased and inaccurate assessment in higher education. In this study, multi-disciplinary test banks were examined, first, using classical test theory and then using a Large Language Model (Grok). Our findings reveal a number of problems in university test items due to methodological shortcomings rather than content inaccuracies. Based ...
Added: January 22, 2026
Пермь: Пермский государственный национальный исследовательский университет, 2024.
This collection presents the proceedings of the Ninth All-Russian Scientific and Practical Conference with International Participation, "Artificial Intelligence in Solving Current Social and Economic Problems of the 21st Century," which was held October 17–18, 2024, in Perm. The collection is intended for researchers and educators, lecturers, postgraduate and master's students, undergraduates, and anyone interested in ...
Added: November 19, 2025
Penskaja E., Имагология и компаративистика 2025 № 23 С. 380–389
The book Artificial Intelligence, Archives and Manuscripts. New Relationships between the Virtual Archive and Its Referent (2025) is presented. This collective monograph discusses both technological and legal, intellectual issues that researchers and archivists face in automated work with manuscript heritage, artificial intelligence and neural networks. ...
Added: October 30, 2025
Surkov A., Sergei Koltcov, Ignatenko V. et al., Physica A: Statistical Mechanics and its Applications 2025 Vol. 681 Article 131085
Neural networks are powerful tools capable of achieving state-of-the-art performance across a wide range of tasks; however, their effectiveness often comes at the cost of extremely large numbers of parameters, which can hinder their deployment in resource-constrained environments. To address this issue, various pruning techniques have been proposed to reduce model size and complexity while ...
Added: October 30, 2025
Yasnitsky L., Голдобин М. А., Прикладная информатика 2025 Т. 20 № 3(117) С. 85–100
Currently, artificial intelligence methods are widely used in the practice of serial production enterprises. They are used to detect defects, classify and eliminate them, identify the causes of defects, predict the quality and properties of the resulting product, select optimal parameters of the production process, and identify and study its patterns. However, outside the field ...
Added: July 10, 2025
Рожин А. К., Бабинцев Л. В., Nefedov S. et al., Наноиндустрия 2024 Т. 17 № S10-2(128) С. 752–755
Распространение систем искусственного интеллекта ведет к повышенному спросу на вычислительные ресурсы, импорт которых стал затруднен. В статье приводится результат исследования отечественной элементной базы, разработан аппаратно-программный комплекс на ее основе. Протестирована скорость вывода и эффективность адаптированных современных нейросетевых алгоритмов. ...
Added: June 20, 2025