Deep Multilayer Neural Network for Predicting the Winner of Football Matches

Anfilets, Sergei; Bezobrazov, Sergei; Golovko, Vladimir; Sachenko, Anatoliy; Komar, Myroslav; Dolny, Raman; Kasyanik, Valery; Bykovyy, Pavlo; Mikhno, Egor; Osolinskyi, Oleksandr

doi:10.31891/1727-6209/2020/19/1-70-77

Cited by 14 publications

(1 citation statement)

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“…Models' description: Step 3. Forecasting (Figure 1, Block 5) modeling [43][44][45]:Different forecasting model such as ARIMA, DNN, XGBoost, are used for each cluster to predict the amount o waste in the future.…”

Section: Proposed Methodsmentioning

confidence: 99%

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: Proposed Methodsmentioning

confidence: 99%