По вашему запросу найдено документов: 119

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Cloud technologies and machine learning in malignant tumors identification via Raman spectroscopy
KOVTUNENKO, A.S., BILYALOV, A.R., V.N. Pavlov (2019)
the technical aspects of solving the problem of malignant tumors diagnostic using machine learning
Identification of Pathological Formations in the Lungs Based on Machine Learning Methods
Shakhmametova, G.R., Vakkazov, N.O., Zulkarneev, R.KH. (2020)
is proposed and a model for finding pathological formations based on machine learning methods is developed
Joint decision-making of parallel machine scheduling restricted in job-machine release time and preventive maintenance with remaining useful life constraints
He, X., Wang, Z., Li, Y., Khazhina, S., Du, W., Wang, J., Wang, W. (2022)
The machine remaining useful life (RUL), the job-machine release time and the correlation between
Predicting Threat Degree for Onset of Type 2 Diabetes Mellitus Based on Machine Learning Methods
Shakhmametova, G., Vakkazov, N., Klimets, S. (2023)
This article discusses the use of machine learning methods to predict the degree of threat
Machine Learning Algorithms for the Analysis of Age-Related Macular Degeneration Based on Optical Coherence Tomography: a Systematic Review
Lopukhova, Ekaterina A., Ibragimova, Rada R., Idrisova, Gulnaz M., Lakman, Irina A., Mukhamadeev, Timur R., Grakhova, Elizaveta P., Bilyalov, Azat R., Kutluyarov, Ruslan V. (2023)
is expressed in the application of various machine learning algorithms, metrics for evaluating
Machine Learning Methods and Models for Recognizing Lung Inhomogeneity from Computed Tomography Scans
Shakhmametova, G. R., Stepanova, E. S., Akhmetshin, A. A., Zulkarneev, R. Kh. (2023)
-platform. For machine learning algorithms, PyTorch 1.9.0 packages were used. An analysis of the effectiveness
Использование методов машинного обучения в диагностике и прогнозировании клинических особенностей шизофрении: нарративный обзор литературы
Machine Learning Techniques in Diagnostics and Prediction of the Clinical Features of Schizophrenia: A Narrative Review
Gashkarimov, Vadim Rimovich, Sultanova, Renata Ildarovna, Efremov, Ilya Segeevich, Asadullin, Azat (2023)
and patient’s quality of life. In this context, machine learning techniques open up new opportunities for a
Применение машинного обучения при диагностике глаукомы: систематический обзор
Application of Machine Learning in Glaucoma Diagnosis: A Systematic Review
Bayboldina, A., Lakman, I., Mukhamadeev, T., Zagidullina, A., Arslanova, A., Mezentseva, A., Байболдина, А.А., Лакман, И.А., Мухамадеев, Т.Р, Загидуллина, А.Ш., Арсланова, А.И., Мезенцева, А.И. (2024)
литературы был осуществлен по базам PubMed/MEDLINE и eLibrary по группам ключевых словосочетаний: machine
ИСКУССТВЕННЫЙ ИНТЕЛЛЕКТ В ОНКОЛОГИИ: ВЗГЛЯД В БУДУЩЕЕ
ARTIFICIAL INTELLIGENCE IN ONCOLOGY: A LOOK INTO THE FUTURE
ГАНЦЕВ, Ш.Х., ФРАНЦ, М.В., Gantsev, Sh.Kh., Frants, M.V. (2019)
MACHINE LEARNING
Современные возможности применения технологий искусственного интеллекта в сердечно-сосудистой визуализации
Modern capabilities of artificial intelligence technologies in cardiovascular imaging
Islamgulov, Almaz Kh., Bogdanova, Alina S., Sufiiarov, Damir I., Chernyavskaya, Alina V., Bairakaeva, Elena R., Maksimova, Anastasia A., Nemychnikov, Nikita V., Bikieva, Dina R., Shakhmaeva, Alsu I., Burdina, Lyubov A., Bolekhan, Aleksandr V., Akimov, Egor I., Shurakova, Zilya Z., Исламгулов, А.Х., Богданова, А.С., Суфияров, Д.И., Чернявская, А.В., Байракаева, Е.Р., Максимова, А.А., Немычников, Н.В., Бикиева, Д.Р., Шахмаева, А.И., Бурдина, Л.А., Болехан, А.В., Акимов, Е.И., Шуракова, З.З. (2025)
machine learning

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