Research of Methods of Control and Management of the Quality of Butter on the Basis of the Neural Network

Kostiuk, Yuliia; Kryvoruchko, Olena; Tsiutsiura, Mykola; Yerukaiev, Andrii; Rusan, Nadiia

doi:10.1109/sist54437.2022.9945764

Cited by 4 publications

(1 citation statement)

References 6 publications

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“…As part of the performed studies of identification of parameters of influence on the processes of data collection and accumulation, scientific results based on ideas and working hypotheses are expected [14]:…”

Section: Main Materialsmentioning

confidence: 99%

Data collection and accumulation of media resources using artificial intelligence

Tsiutsiura,

Yerukaiev,

Khorolska

et al. 2023

MDCS

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In this difficult time in which our country found itself, information technology has become the force that is successfully used against the aggressive actions of the enemy. The armed forces, in addition to the necessary physical assistance, are quite significantly helped in the information sphere, in particular, from the side of the scientific community. Different directions and branches of computer science are combined to get an effective way to achieve the desired victory and the long-awaited peace and prosperity. In this article, the authors offer their vision in the implementation of technology for evaluating information coming from various media sources. For this, one of the most widely used areas of information technology – artificial intelligence – was involved in the research. As you know, AI includes a sufficiently wide and complete set of models and methods for working out various practical problems. In particular, the authors focused on "soft" calculations, that is, the use of neural networks, fuzzy sets, and genetic algorithms in combination with methods from another area of AI – intelligent data analysis. All the above-mentioned ideas are suggested to be considered in the rationale before creating a scientific project. In this article, foreign and domestic sources will be analyzed in detail in order to identify common and distinctive features with the author's ideas. Particular attention will be paid to those components that are not disclosed in other works and are proposed for development in this project. The object, the subject of research, the goal will also be indicated, the scientific and practical novelty of the implementation of the ideas disclosed in this work will be revealed. In addition, the advantages of using the proposed methods for solving not only the task of processing and evaluating information from various media sources will be described, but also the benefit of their application in other critical military tasks will be shown. Thus, the authors set themselves the goal of helping our defenders implement an approach based on the application of artificial intelligence. This article covers only proposals with their detailed scientific justification. So, summarizing all of the above, the work reveals a theoretical-applied approach at the level of revealing the basic ideas for creating a project. In the following works by the authors, after the presentation of the research base, more attention will be paid to already mathematical methods of description and coverage of the conducted experimental studies.

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Section: Main Materialsmentioning

confidence: 99%

Data collection and accumulation of media resources using artificial intelligence

Tsiutsiura,

Yerukaiev,

Khorolska

et al. 2023

MDCS

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Evaluation of water quality based on artificial intelligence: performance of multilayer perceptron neural networks and multiple linear regression versus water quality indexes

Palabıyık,

Akkan

2024

Environ Dev Sustain

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A significant problem in the sustainable management of water resources is the lack of funding and long-term monitoring. Today, this problem has been greatly reduced by innovative, adaptive, and sustainable learning methods. Therefore, in this study, a sample river was selected and 14 variables observed at 5 different points for 12 months, traditionally reference values, were calculated by multivariate statistical analysis methods to obtain the water quality index (WQI). The WQI index was estimated using different algorithms including the innovatively used multiple linear regression (MLR), multilayer perceptron artificial neural networks (MLP-ANN) and various machine learning estimation algorithms including neural networks (NN), support vector machine (SVM), gaussian process regression (GPR), ensemble and decision tree approach. By comparing the results, the most appropriate method was selected. The determination of water quality was best estimated by the multiple linear regression (MLR) model. As a result of this MLR modeling, high prediction performance was obtained with accuracy values of R2 = 1.0, RMSE = 0.0025, and MAPE = 0.0296. The root mean square error (RMSE), percent mean absolute error (MAE), and coefficient of determination (R2) were used to determine the accuracy of the models. These results confirm that both MLR model can be used to predict WQI with very high accuracy. It seems that it can contribute to strengthening water quality management. As a result, as with the powerful results of the innovative approaches (MLR and MLP-ANN) and other assessments, it was found that the presence of intense anthropogenic pressure in the study area and the current situation needs immediate remediation.

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The Working Principle of Artificial Intelligence in Video Games

Levytskyi,

Tsiutsiura,

Yerukaiev

et al. 2023

2023 IEEE International Conference on Smart Information Systems and Technologies (SIST)

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Research of Methods of Control and Management of the Quality of Butter on the Basis of the Neural Network

Cited by 4 publications

References 6 publications

Data collection and accumulation of media resources using artificial intelligence

Data collection and accumulation of media resources using artificial intelligence

Evaluation of water quality based on artificial intelligence: performance of multilayer perceptron neural networks and multiple linear regression versus water quality indexes

The Working Principle of Artificial Intelligence in Video Games

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