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DISADVANTAGES AND ADVANTAGES OF DISTANCE LEARNING
Fazlutdinova, L.R., Gainullin, R.A., Yulmukhametov, A.A. (2023)
The article discusses the nuances of distance learning at the university, highlights the advantages
COLLOBATION FORMS OF LEARNING
Ibragimov, D.R., Aitova, L.R., Timerbulatov, M.V. (2023)
learning. Distance form of education is the receipt of educational services without visiting an educational
IT-TECHNOLOGIES AS A WAY TO TEACH PHYSICAL EDUCATION AND SPORT TO FOREIGN STUDENTS UNDER PANDEMIC CONDITIONS AT BSMU
Zakiev, A.M., Khadiyatov, R.Z. (2023)
In the article methods of distance learning organization with application of IT
DISTANCE EDUCATION FOR RESIDENTS IN THE COVID-19 ERA
Lutfarakhmanov, I.I., Kakaulin, A.G., Gafarova, A.M., Lifanova, A.D., Grazhdankin, A.A. (2023)
-based learning more important. The aim of the re-search was to study a mentor-based curriculum with on
THE ADULT LEARNING THEORY PRINCIPLES
Tselousova, O.S., Ovsyannikova, L.B. (2023)
the andragogy approach of today’s methodology. There are six main assumptions in adult learning theory. They are
DIGITAL TOOLS IN LEARNING STUDENTS OF PEDIATRIC FACULTY
Yakovleva, L.V., Kuchina, E.S., Tikhonova, R.Z. (2023)
DIGITAL TOOLS IN LEARNING STUDENTS OF PEDIATRIC FACULTY
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
Association of the ACTN3 Gene’s Single-Nucleotide Variant Rs1815739 (R577X) with Sports Qualification and Competitive Distance in Caucasian Athletes of the Southern Urals
Balberova, Olga V., Shnayder, Natalia A., Bykov, Evgeny V., Zakaryukin, Yuri E., Petrova, Marina M., Soloveva, Irina A., Narodova, Ekaterina A., Chumakova, Galina A., Al-Zamil, Mustafa, Asadullin, Azat R., Vaiman, Elena E., Trefilova, Vera V., Nasyrova, Regina F. (2023)
gene with qualification and competitive distance in Caucasian athletes of the Southern Urals. Methods
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

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