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Start Over Author "Anatoliy Kazak" Topic artificial intelligence
3 results on '"Anatoliy Kazak"'

1. A Solar and Wind Energy Evaluation Methodology Using Artificial Intelligence Technologies

Author

Vladimir Simankov, Pavel Buchatskiy, Anatoliy Kazak, Semen Teploukhov, Stefan Onishchenko, Kirill Kuzmin, and Petr Chetyrbok

Subjects
renewable energy, artificial intelligence, digital twins, forecasting, machine learning, neural networks, Technology
Abstract

The use of renewable energy sources is becoming increasingly widespread around the world due to various factors, the most relevant of which is the high environmental friendliness of these types of energy resources. However, the large-scale involvement of green energy leads to the creation of distributed energy networks that combine several different generation methods, each of which has its own specific features, and as a result, the data collection and processing necessary to optimize the operation of such energy systems become more relevant. Development of new technologies for the more optimal use of RES is one of the main tasks of modern research in the field of energy, where an important place is assigned to the use of technologies based on artificial intelligence, allowing researchers to significantly increase the efficiency of the use of all types of RES within energy systems. This paper proposes to consider the methodology of application of modern approaches to the assessment of the amount of energy obtained from renewable energy sources based on artificial intelligence technologies, approaches used for data processing and for optimization of the control processes for operating energy systems with the integration of renewable energy sources. The relevance of the work lies in the formation of a general approach applied to the evaluation of renewable energy sources such as solar and wind energy based on the use of artificial intelligence technologies. As a verification of the approach considered by the authors, a number of models for predicting the amount of solar power generation using photovoltaic panels have been implemented, for which modern machine-learning methods have been used. As a result of testing for quality and accuracy, the best results were obtained using a hybrid forecasting model, which combines the joint use of a random forest model applied at the stage of the normalization of the input data, exponential smoothing model, and LSTM model.

Published
2024
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2. Evaluation Metrics Research for Explainable Artificial Intelligence Global Methods Using Synthetic Data

Author

Alexandr Oblizanov, Natalya Shevskaya, Anatoliy Kazak, Marina Rudenko, and Anna Dorofeeva

Subjects
Human-Computer Interaction, explainable artificial intelligence, XAI, Artificial Intelligence, Control and Systems Engineering, Applied Mathematics, explanation metrics, synthetic data, Industrial and Manufacturing Engineering, Information Systems
Abstract

In recent years, artificial intelligence technologies have been developing more and more rapidly, and a lot of research is aimed at solving the problem of explainable artificial intelligence. Various XAI methods are being developed to allow the user to understand the logic of how machine learning models work, and in order to compare the methods, it is necessary to evaluate them. The paper analyzes various approaches to the evaluation of XAI methods, defines the requirements for the evaluation system and suggests metrics to determine the various technical characteristics of the methods. A study was conducted, using these metrics, which determined the degradation in the explanation quality of the SHAP and LIME methods with increasing correlation in the input data. Recommendations are also given for further research in the field of practical implementation of metrics, expanding the scope of their use.

Published
2023
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3. The Use of Machine Learning for Comparative Analysis of Amperometric and Chemiluminescent Methods for Determining Antioxidant Activity and Determining the Phenolic Profile of Wines

Author

Anatoliy Kazak, Yurij Plugatar, Joel Johnson, Yurij Grishin, Petr Chetyrbok, Vadim Korzin, Parminder Kaur, and Tatiana Kokodey

Subjects
Human-Computer Interaction, Artificial Intelligence, Control and Systems Engineering, Applied Mathematics, antioxidant activity, amperometric method, chemiluminescent method, phenolic substances, machine learning, Industrial and Manufacturing Engineering, Information Systems
Abstract

This paper presents an analysis of modern methods used to determine antioxidant activity. According to research by the World Health Organization, the deficiency of such important nutrients as antioxidants leads to a decrease in body resistance and the development of chronic diseases. When it comes to diet, the inclusion of foods with a high content of antioxidants helps to increase life expectancy. As a result of this research, the mass concentration of phenolic substances and the antioxidant activity of phenolic antioxidants in young white and red table wine materials were determined using amperometric and chemiluminescent methods in order to determine antioxidant activity. Regression equations reflecting the relationship between the indicator of antioxidant activity and the value of the mass concentration of phenolic substances in young table wine materials were derived. The conversion coefficient for determining the mass concentration of phenolic substances when using Trolox-C and gallic acid as standards was established, which was—3.75. Based on a multiple linear regression model, the total antioxidant activity of the samples (F9.5 = 19.10 and p = 0.0023) can be fairly accurately predicted with an R2 of 0.921 for the calibration data set. A neural network regression model (NNRM) was chosen for the machine-learning regression analysis of the antioxidant activity of the wine samples due to its effectiveness in predicting outcomes in various applications. The implementation was performed using the fitrnet function provided in the Statistics and Machine Learning Toolbox in MATLAB R2021b. The MSE of the calibration model was 0.056; however, the MSE for the three validation samples was much higher, at 0.272.

Published
2022
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