Method of Detecting a Fictitious Company on the Machine Learning Base

Lipyanina, Hrystyna; Sachenko, Svitlana; Lendyuk, Taras; Brych, Vasyl; Yatskiv, Vasyl; Osolinskyi, Oleksandr

doi:10.1007/978-3-030-80472-5_12

Cited by 5 publications

(5 citation statements)

References 25 publications

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“…In addition to that, comparing the results of the proposed work with similar works [26,32,39,40] shows that the authors' proposed approach to urban waste management has proven to be competitive in terms of forecast accuracy. Using the XGBoost model, we achieved high accuracy in predicting the amount of recycled waste compared to the other models' results.…”

Section: Discussionmentioning

confidence: 70%

“…Compared to the work [32], where a machine learning method was used to identify city sectors with waste accumulation, our approach also proved to be more accurate, which proves its ability to classify and predict waste volumes in more detail.…”

Section: Discussionmentioning

confidence: 84%

“…It is worth noting that the results of this research also demonstrate progress in the use of machine learning for waste management. Similar to works [32,39,40], we used various city attributes and socioeconomic indicators to improve the accuracy of the forecast. However, those authors' approach additionally included waste classification, which contributed to a more complex analysis and improved management efficiency.…”

Section: Discussionmentioning

confidence: 99%

“…Various parameters can be used to classify waste, such as composition, quantity, size, shape, etc. However, for a more accurate classification, machine learning methods can be used [32][33][34][35]. To classify waste, it necessary (Figure 1, Blocks 2,3) to have sufficient data about, for example, its composition, and other parameters that impact its classification.…”

Section: Proposed Methodsmentioning

confidence: 99%

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Intelligent Waste-Volume Management Method in the Smart City Concept

Lipianina-Honcharenko,

Komar,

Osolinskyi

et al. 2023

Smart Cities

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This research paper proposes an innovative approach to urban waste management using intelligent methods of classification, clustering, and forecasting. The application of this approach allows for more efficient waste management and contributes to the sustainable development of the urban environment. The aim of this research is to develop an intelligent method for urban waste management, which includes clustering of waste sources, accurate forecasting of waste volumes, and evaluation of forecast results. To achieve this goal, a real dataset with city characteristics and waste data was used. On account of the war in Ukraine, the authors faced the problem of obtaining open data on waste in Ukraine, so it was decided to use data from another city (Singapore). The results show the high efficiency of the developed method. Comparison of the obtained results with the results of the nearest similar works shows that the main feature of this study is the high accuracy of waste-volume forecasting using the XGBoost model, which reached a level of up to 98%.

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Section: Discussionmentioning

confidence: 70%

Section: Discussionmentioning

confidence: 84%

Section: Discussionmentioning

confidence: 99%

Section: Proposed Methodsmentioning

confidence: 99%

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Intelligent Waste-Volume Management Method in the Smart City Concept

Lipianina-Honcharenko,

Komar,

Osolinskyi

et al. 2023

Smart Cities

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“…As for the economic sphere, a city resident can assess economic stability [17], their financial situation and the employment situation.…”

Section: Information Security Assessmentmentioning

confidence: 99%

Intelligent information system of the city's socio-economic infrastructure

Lipianina-Honcharenko,

Bodyanskiy,

Sachenko

2023

SRIT

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Urban development is an important problem that can be solved with the help of intelligent information systems. Such systems ensure efficient management of the city’s diverse infrastructure. The researchers developed a concept of such an information system based on a conceptual model and using data flow for intelligent decision-making. The system was tested for 1460 days in the city of Ternopil. The modelling results showed that the city’s central area is stable, with 50% of enterprises in the “growing” state and 70% of people in the “satisfactory” state. People often move to the northeastern and western zones due to higher levels of comfort and more affordable housing. However, the total distance of car trips has increased by 249%, negatively impacting the environment. The condition of enterprises in other zones is less stable with lower “growth” indicators, but there are zones with “stable” and “satisfactory” conditions.

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