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  Surname Name Title Thesis status   Supervisors Reviewers Type of thesis Date of def. Title
Student Type of thesis - - - - - - - - - -
Item shown in detail Danquah Includes the selected person into the timetable overlap calculation. George Amoako Big Data Processing Methods for Environmental Management Big Data Processing Methods for Environmental Management Thesis finished and defended successfully (DUO).   Jašek Roman Sedláček Michal Master's thesis 1686780000000 15.06.2023 Big Data Processing Methods for Environmental Management Thesis finished and defended successfully (DUO).
George Amoako Danquah Master's thesis 0XX 0XX 0XX 0XX 0XX 0XX 0XX 0XX 0XX 0XX

Thesis info Big Data processing Methods for Environmental Management

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Name Danquah George Amoako Includes the selected person into the timetable overlap calculation.
Acad. Yr. 2022/2023
Assigning department AUIUI
Date of defence Jun 15, 2023
Type of thesis Master's thesis
Thesis status Thesis finished and defended successfully (DUO). Thesis finished and defended successfully (DUO).
Completeness of mandatory entries - All mandatory fields for this Thesis are filled in.
Main topic Metody zpracování velkých dat pro environmentální management
Main topic in English Big Data Processing Methods for Environmental Management
Title according to student Big Data processing Methods for Environmental Management
English title as given by the student Big Data Processing Methods for Environmental Management
Parallel name -
Subtitle -
Thesis supervisor Jašek Roman, prof. Mgr. Ph.D., DBA
External examiner Sedláček Michal, Ing. Mgr. Ph.D.
Annotation 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
Annotation in English 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
Keywords Artificial Neural Networks, waste generation, environmental management.
Keywords in English Artificial Neural Networks, waste generation, environmental management.
Length of the covering note 66 pages
Language AN
Annotation
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
Annotation in English
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
Keywords
Artificial Neural Networks, waste generation, environmental management.
Keywords in English
Artificial Neural Networks, waste generation, environmental management.
Research Plan
  1. Analyze the literary and professional sources of the topic.
  2. Define the main goal of the work.
  3. Design an engineering way to solve the assignment.
  4. Implement your solution in accordance with the defined goal.
  5. Evaluate the success of your project.
Research Plan
  1. Analyze the literary and professional sources of the topic.
  2. Define the main goal of the work.
  3. Design an engineering way to solve the assignment.
  4. Implement your solution in accordance with the defined goal.
  5. Evaluate the success of your project.
Recommended resources
  1. BLANCHARD, Benjamin S. and John BLYLER. System engineering management. Fifth edition. Hoboken, New Jersey: Wiley, 2016. ISBN 9781119047827.
  2. Managementmania.com: Professional social network for business [online], Copyright © 2011 - 2016. Wilmington, New Castle County Delaware, USA: MANAGEMENTMANIA.COM [cit. 2022-01-13]. Available from: https://managementmania.com
  3. Software Engineering - Ian Sommerville: Tenth Edition [online], 2015. [cit. 2022-01-13]. Available from: https://iansommerville.com/software-engineering-book/
Recommended resources
  1. BLANCHARD, Benjamin S. and John BLYLER. System engineering management. Fifth edition. Hoboken, New Jersey: Wiley, 2016. ISBN 9781119047827.
  2. Managementmania.com: Professional social network for business [online], Copyright © 2011 - 2016. Wilmington, New Castle County Delaware, USA: MANAGEMENTMANIA.COM [cit. 2022-01-13]. Available from: https://managementmania.com
  3. Software Engineering - Ian Sommerville: Tenth Edition [online], 2015. [cit. 2022-01-13]. Available from: https://iansommerville.com/software-engineering-book/
Týká se praxe No
Enclosed appendices CD-ROM
Appendices bound in thesis illustrations, graphs, schemes, tables
Taken from the library No
Full text of the thesis
Appendices
Reviewer's report
Supervisor's report
Defence procedure record file