Harmonization of project configuration of the crop harvesting technological system

Луб, Павло Миронович; Tryhuba, Anatoliy; Chubyk, Roman; Padyuka, Roman

doi:10.1109/csit56902.2022.10000862

Cited by 1 publication

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, they do not provide an assessment of the unevenness of the state of organic waste generation of households in a given community based on the use of machine learning technologies. It is known [46][47][48][49][50][51] that the use of modern machine learning algorithms makes it possible to evaluate the relationships between factors that determine the effectiveness of processes in various spheres of society. At the same time, the substantiation of models, based on the use of methods of intellectual analysis of statistical data, makes it possible to substantiate the trends of changes in individual indicators of the generation and use of household organic waste and perform their qualitative forecasting.…”

Section: Analysis Of the Literature Data And Problem Statementmentioning

confidence: 99%

An Approach to Assessing the State of Organic Waste Generation in Community Households Based on Associative Learning

Tryhuba,

Hutsol,

Tryhuba

et al. 2023

Sustainability

View full text Add to dashboard Cite

The purpose of this work is to substantiate the approach to assessing the state of organic waste generation by households of a given community, which is based on passive production observations and intellectual analysis of statistical data, which ensures consideration of the factors and features of organic waste generation, as well as the development of qualitative models for forecasting their receipt. To achieve the goal, the following tasks were solved: the analysis of the state of organic waste generation by households in the EU countries was performed; an approach to assessing the state of organic waste generation by households of a given community is proposed; based on the use of the proposed approach, and models for assessing the state of organic waste generation of households in a given community were substantiated. The hypothesis of the study is to substantiate and use an approach to assessing the generation of organic waste by households in individual communities, based on the method of association learning and search for association rules, which will identify factors that have a significant impact on the volume of organic waste generated by households, the consideration of which will improve the accuracy of forecasting models and improve the quality of management of the processes of collection and processing of this waste in communities. The research methodology used allows for the use of data mining, probability theory, mathematical statistics, machine learning technology, and the Associative Rule Learning (ARL) method. Based on the use of a reasonable algorithm, they identify key trends and relationships between the factors of organic waste generation in communities in different countries, which is the basis for creating accurate models for predicting the volume of collection and processing of this waste in communities. The study found that the largest number of households produced organic waste per capita in the range of 0.14–0.25 kg/person. At the same time, most households have from two to four residents and are located on the adjoining territory from 350 m2 to 680 m2. Based on the method of learning associative rules, it was found that there are no close correlations between individual factors that determine the daily volume of organic waste generation by households per capita. The highest correlation coefficient between the type of housing and the income level of household residents is 0.13. The number of residents and the occupied area of the adjacent territory have the greatest impact on the daily volume of organic waste generated by households per capita. The substantiated associative rules of relationships, as well as the diagrams of relationships between factors, have helped to identify those factors that have the greatest impact on the volume of organic waste generation. They are the basis for creating accurate models for predicting the volume of collection and planning the processing of this waste in communities. Based on the proposed approach, Python 3.9 software was developed. It makes it possible to quickly carry out calculations and perform a quantitative assessment of the state of organic waste generation by households of a given community according to the specified rules of association between the volumes of organic waste generation and their factors. The results of the study are the basis for the further development of models for accurate forecasting of the collection and planning of the processing of organic waste from households in communities.

show abstract

Section: Analysis Of the Literature Data And Problem Statementmentioning

confidence: 99%