Larisa Lyutikova
Biographic Data
| ID | 9424976 |
|---|---|
| NAME | Larisa Lyutikova |
| GIVEN NAMES | Larisa |
| FAMILY NAME | Lyutikova |
| SIGNATURE | LYUTIKOVA L |
| AFFILIATIONS | Institute of Applied Mathematics and Automation – a branch of the Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences |
| VERIFIED | No |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 5 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2018 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Logical and mathematical interpretation of intelligent traffic light management strategies in the city
Introduction. This article examines the application of explainable reinforcement learning (XRL) methods to intelligent traffic light control in an urban environment, using Vladikavkaz as an example. Purpose of the research. The primary objective of the study is to develop an adaptive, efficient, and interpretable traffic flow management system that overcomes the shortcomings of traditional fixed - cycle schemes. Materials and Methods. The propose…
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 …
Construction of a predictive model of pedestrian crossing traffic in a mountainous region taking into account weather and time factors
Pedestrian traffic in mountainous regions is significantly affected by weather conditions and time factors. Optimization of urban infrastructure and ensuring pedestrian safety require forecasting traffic changes depending on climate and time of day. The purpose of this study is to develop a predictive model that takes into account key factors affecting pedestrian traffic intensity. The study collected data on pedestrian traffic in the mountainous…
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
No prominent works on this page.
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…
Construction of a predictive model of pedestrian crossing traffic in a mountainous region taking into account weather and time factors
Pedestrian traffic in mountainous regions is significantly affected by weather conditions and time factors. Optimization of urban infrastructure and ensuring pedestrian safety require forecasting traffic changes depending on climate and time of day. The purpose of this study is to develop a predictive model that takes into account key factors affecting pedestrian traffic intensity. The study collected data on pedestrian traffic in the mountainous…
Logical and mathematical interpretation of intelligent traffic light management strategies in the city
Introduction. This article examines the application of explainable reinforcement learning (XRL) methods to intelligent traffic light control in an urban environment, using Vladikavkaz as an example. Purpose of the research. The primary objective of the study is to develop an adaptive, efficient, and interpretable traffic flow management system that overcomes the shortcomings of traditional fixed - cycle schemes. Materials and Methods. The propose…
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 …
Computer Science (3 works) · Geography (3 works) · Artificial Intelligence (2 works) · Transportation Systems and Logistics (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)