The aim of this study was to use Artificial Neural Networks, a machine learning algorithm which is a Big Data processing method to create a waste generation forecasting model on Solid waste in Ghana based on data from socio-economic and demographic factors. The processing and integration of data was developed in MATLAB software. Performance assessment indicators such as Regression (R) and Mean Square Error (MSE) were used to access the performance of the models. The results showed that Artificial Neural Networks can be used to create waste prediction models and can be considered as an effective approach to estimating waste generation quantities. The results of this study are expected to represent a general outline for Environmental management stakeholders in Ghana and other countries
Anotace v angličtině
The aim of this study was to use Artificial Neural Networks, a machine learning algorithm which is a Big Data processing method to create a waste generation forecasting model on Solid waste in Ghana based on data from socio-economic and demographic factors. The processing and integration of data was developed in MATLAB software. Performance assessment indicators such as Regression (R) and Mean Square Error (MSE) were used to access the performance of the models. The results showed that Artificial Neural Networks can be used to create waste prediction models and can be considered as an effective approach to estimating waste generation quantities. The results of this study are expected to represent a general outline for Environmental management stakeholders in Ghana and other countries
The aim of this study was to use Artificial Neural Networks, a machine learning algorithm which is a Big Data processing method to create a waste generation forecasting model on Solid waste in Ghana based on data from socio-economic and demographic factors. The processing and integration of data was developed in MATLAB software. Performance assessment indicators such as Regression (R) and Mean Square Error (MSE) were used to access the performance of the models. The results showed that Artificial Neural Networks can be used to create waste prediction models and can be considered as an effective approach to estimating waste generation quantities. The results of this study are expected to represent a general outline for Environmental management stakeholders in Ghana and other countries
Anotace v angličtině
The aim of this study was to use Artificial Neural Networks, a machine learning algorithm which is a Big Data processing method to create a waste generation forecasting model on Solid waste in Ghana based on data from socio-economic and demographic factors. The processing and integration of data was developed in MATLAB software. Performance assessment indicators such as Regression (R) and Mean Square Error (MSE) were used to access the performance of the models. The results showed that Artificial Neural Networks can be used to create waste prediction models and can be considered as an effective approach to estimating waste generation quantities. The results of this study are expected to represent a general outline for Environmental management stakeholders in Ghana and other countries
Software Engineering - Ian Sommerville: Tenth Edition [online], 2015. [cit. 2022-01-13]. Available from: https://iansommerville.com/software-engineering-book/
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The student presented the main goals and results of his master thesis to the committee. The presentation made a good impression, the student came out with the main points of the work. Subsequently, the student was introduced to the opinions of the supervisor and opponent of the master's thesis. The student gradually answered the questions of the thesis opponent.
The Commission raised the following questions for the defence:
1) Doc. Komínková Oplatková: You mention in chapter 5.3 that you also analyzed the neural networks. Have you tried different structures of the neural networks?
The student responded to the questions asked at a good level.