L A Lyutikova
Biographic Data
| ID | 8205047 |
|---|---|
| NAME | L A Lyutikova |
| GIVEN NAMES | L A |
| FAMILY NAME | Lyutikova |
| SIGNATURE | LYUTIKOVA L A |
| AFFILIATIONS | Kabardino-Balkarian Scientific Center |
| ORCID | 0000-0003-4941-7854 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2018 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Predicting the characteristics of sele flows using machine learning
Mudflows are among the most hazardous natural phenomena in mountainous regions, causing significant damage to infrastructure and settlements. Their prediction is traditionally challenging due to the fragmented, incomplete, and heterogeneous nature of source data, which limits the applicability of classical statistical methods. Purpose of the research. To develop and test a set of machine learning models for predicting mudflow characteristics and …
A logical approach to building a machine learning model for assessing the sustainable development of mountain areas
Introduction. There are a significant number of mountainous areas on the territory of Russia. The Russian Federation has extensive mountain systems, including the Altai Territory, the Caucasus region, the Ural Mountains, the Sayans, the Baikal region, Kamchatka, Chukotka and others. The exact number of mountainous areas is difficult to determine, since this concept can vary depending on the classification criteria and the boundaries of mountainou…
System of Autonomous Robot Machine Vision for Agricultural Application in Mountain Territories Based on the Multi-Agent Cognitive Architectures
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System of Autonomous Robot Machine Vision for Agricultural Application in Mountain Territories Based on the Multi-Agent Cognitive Architectures
A logical approach to building a machine learning model for assessing the sustainable development of mountain areas
Introduction. There are a significant number of mountainous areas on the territory of Russia. The Russian Federation has extensive mountain systems, including the Altai Territory, the Caucasus region, the Ural Mountains, the Sayans, the Baikal region, Kamchatka, Chukotka and others. The exact number of mountainous areas is difficult to determine, since this concept can vary depending on the classification criteria and the boundaries of mountainou…
Predicting the characteristics of sele flows using machine learning
Mudflows are among the most hazardous natural phenomena in mountainous regions, causing significant damage to infrastructure and settlements. Their prediction is traditionally challenging due to the fragmented, incomplete, and heterogeneous nature of source data, which limits the applicability of classical statistical methods. Purpose of the research. To develop and test a set of machine learning models for predicting mudflow characteristics and …
Artificial Intelligence (2 works) · Computer Science (2 works) · Geography (2 works) · Advanced Scientific Research Methods (1 work) · Agriculture (1 work) · Archaeology (1 work) · Arctic and Russian Policy Studies (1 work) · Artificial neural network (1 work) · Autonomous robot (1 work) · Climate change and permafrost (1 work)