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The Effect of Splitting of Raw Data into Training and Test Subsets on the Accuracy of Predicting Spatial Distribution by a Multilayer Perceptron

Mathematical geosciences, 2020-01, Vol.52 (1), p.111-121

2020

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Combining spatial autocorrelation with machine learning increases prediction accuracy of soil heavy metals

Catena (Giessen), 2019-03, Vol.174, p.425-435

2019

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The extraction of the training subset for the spatial distribution modelling of the heavy metals in topsoil

Catena (Giessen), 2021-12, Vol.207, p.105699, Article 105699

2021

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MODELING OF THE SPATIAL DISTRIBUTION OF CHROME AND MANGANESE IN SOIL: SELECTION OF A TRAINING SUBSET

Геоэкология. Инженерная геология. Гидрогеология. Геокриология, 2023-09 (5), p.88-96

2023

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Game-Theoretic Model for the Interaction of the Online Course Design Process Participants within the University

Universitetskoe upravlenie: praktika i analiz (Online), 2023-02, Vol.26 (4), p.71-83

2023

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Atmospheric Air Dust Concentration, Composition And Size Distribution Data At Breathing Heights In Yekaterinburg

Geography, environment, sustainability, 2024-01, Vol.16 (4), p.193-199

2024

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Prediction of the chrome distribution in subarctic Noyabrsk using co-kriging, generalized regression neural network, multilayer perceptron, and hybrid technics

Геоэкология. Инженерная геология. Гидрогеология. Геокриология, 2019-05 (2), p.77-86

2019

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COMPARATIVE ANALYSIS OF DATA SPLITTING ALGORITHMS FOR TRAINING ARTIFICIAL NEURAL NETWORKS IN MODELING THE SPATIAL DISTRIBUTION OF HEAVY METALS IN THE TOPSOIL

Траектория исследований – человек, природа, технологии, 2022 (3), p.73-88

2022

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VERTICAL DISTRIBUTION OF DUST CHARACTERISTICS OF THE ATMOSPHERIC SURFACE LAYER IN EKATERINBURG

Траектория исследований – человек, природа, технологии, 2022 (3), p.60-72

2022

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Two-step combined algorithm for improving the accuracy of predicting methane concentration in atmospheric air based on the NARX neural network and subsequent prediction of residuals

Arktika, ėkologii͡a︡ i ėkonomika, 2020-06 (2(38)), p.59-67

2020

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Magnetic phase transitions, metastable states, and magnetic hysteresis in the antiferromagnetic compounds Fe0.5TiS2−ySey

Physical review. B, 2019-07, Vol.100 (2), p.1

2019

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Hierarchical analytical and simulation modelling of human-machine systems with interference

Journal of physics. Conference series, 2017-01, Vol.803 (1), p.12026

2017

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Magnetic phase transitions, metastable states, and magnetic hysteresis in the antiferromagnetic compounds Fe 0.5 TiS 2 − y Se y

Physical review. B, 2019-07, Vol.100 (2), Article 024430

2019

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The Effect of Splitting of Raw Data into Training and Test Subsets on the Accuracy of Predicting Spatial Distribution by a Multilayer Perceptron

Combining spatial autocorrelation with machine learning increases prediction accuracy of soil heavy metals

The extraction of the training subset for the spatial distribution modelling of the heavy metals in topsoil

MODELING OF THE SPATIAL DISTRIBUTION OF CHROME AND MANGANESE IN SOIL: SELECTION OF A TRAINING SUBSET

Game-Theoretic Model for the Interaction of the Online Course Design Process Participants within the University

Atmospheric Air Dust Concentration, Composition And Size Distribution Data At Breathing Heights In Yekaterinburg

Prediction of the chrome distribution in subarctic Noyabrsk using co-kriging, generalized regression neural network, multilayer perceptron, and hybrid technics

COMPARATIVE ANALYSIS OF DATA SPLITTING ALGORITHMS FOR TRAINING ARTIFICIAL NEURAL NETWORKS IN MODELING THE SPATIAL DISTRIBUTION OF HEAVY METALS IN THE TOPSOIL

VERTICAL DISTRIBUTION OF DUST CHARACTERISTICS OF THE ATMOSPHERIC SURFACE LAYER IN EKATERINBURG

Two-step combined algorithm for improving the accuracy of predicting methane concentration in atmospheric air based on the NARX neural network and subsequent prediction of residuals

Magnetic phase transitions, metastable states, and magnetic hysteresis in the antiferromagnetic compounds Fe0.5TiS2−ySey

Hierarchical analytical and simulation modelling of human-machine systems with interference

Magnetic phase transitions, metastable states, and magnetic hysteresis in the antiferromagnetic compounds Fe 0.5 TiS 2 − y Se y

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