A Hybrid Methodology of Data Science and Decision Making Techniques: Lessons from COVID-19 Pandemic Management
Today, data mining and machine learning are recognized as tools for extracting knowledge from large datasets with diverse characteristics. With the increasing volume and complexity of information in various fields, decision-making has become more challenging for managers and decision-making units. D...
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| Format: | Article |
| Language: | English |
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Iran University of Science & Technology
2025-03-01
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| Series: | International Journal of Industrial Engineering and Production Research |
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| Online Access: | http://ijiepr.iust.ac.ir/article-1-2095-en.pdf |
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| author | mehdi dadehbeigi ali taherinezhad alireza alinezhad |
| author_facet | mehdi dadehbeigi ali taherinezhad alireza alinezhad |
| author_sort | mehdi dadehbeigi |
| collection | DOAJ |
| description | Today, data mining and machine learning are recognized as tools for extracting knowledge from large datasets with diverse characteristics. With the increasing volume and complexity of information in various fields, decision-making has become more challenging for managers and decision-making units. Data Envelopment Analysis (DEA) is a tool that aids managers in measuring the efficiency of the units under their supervision. Another challenge for managers involves selecting and ranking options based on specific criteria. Choosing an appropriate multi-criteria decision-making (MCDM) technique is crucial in such cases. With the spread of COVID-19 and the significant financial, economic, and human losses it caused, data mining has once again played a role in improving outcomes, predicting trends, and reducing these losses by identifying patterns in the data. This paper aims to assess and predict the efficiency of countries in preventing and treating COVID-19 by combining DEA and MCDM models with machine learning models. By evaluating decision-making units and utilizing available data, decision-makers are better equipped to make effective decisions in this area. Computational results are presented in detail and discussed in depth. |
| format | Article |
| id | doaj-art-a7ade5fee1e84fd4a670c5314c734103 |
| institution | DOAJ |
| issn | 2008-4889 2345-363X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Iran University of Science & Technology |
| record_format | Article |
| series | International Journal of Industrial Engineering and Production Research |
| spelling | doaj-art-a7ade5fee1e84fd4a670c5314c7341032025-08-20T02:46:47ZengIran University of Science & TechnologyInternational Journal of Industrial Engineering and Production Research2008-48892345-363X2025-03-0136199115A Hybrid Methodology of Data Science and Decision Making Techniques: Lessons from COVID-19 Pandemic Managementmehdi dadehbeigi0ali taherinezhad1alireza alinezhad2 MSc Graduate, Department of Industrial Engineering, Faculty of Industrial and MechanicalEngineering, Qazvin branch, Islamic Azad University, Qazvin, Iran PhD Candidate, Department of Industrial Engineering, Faculty of Industrial and MechanicalEngineering, Qazvin branch, Islamic Azad University, Qazvin, Iran Associate Professor, Department of Industrial Engineering, Faculty of Industrial andMechanical Engineering, Qazvin branch, Islamic Azad University, Qazvin, Iran Today, data mining and machine learning are recognized as tools for extracting knowledge from large datasets with diverse characteristics. With the increasing volume and complexity of information in various fields, decision-making has become more challenging for managers and decision-making units. Data Envelopment Analysis (DEA) is a tool that aids managers in measuring the efficiency of the units under their supervision. Another challenge for managers involves selecting and ranking options based on specific criteria. Choosing an appropriate multi-criteria decision-making (MCDM) technique is crucial in such cases. With the spread of COVID-19 and the significant financial, economic, and human losses it caused, data mining has once again played a role in improving outcomes, predicting trends, and reducing these losses by identifying patterns in the data. This paper aims to assess and predict the efficiency of countries in preventing and treating COVID-19 by combining DEA and MCDM models with machine learning models. By evaluating decision-making units and utilizing available data, decision-makers are better equipped to make effective decisions in this area. Computational results are presented in detail and discussed in depth.http://ijiepr.iust.ac.ir/article-1-2095-en.pdfdata miningmachine learningdata envelopment analysismulti-criteria decisionmakingcovid-19. |
| spellingShingle | mehdi dadehbeigi ali taherinezhad alireza alinezhad A Hybrid Methodology of Data Science and Decision Making Techniques: Lessons from COVID-19 Pandemic Management International Journal of Industrial Engineering and Production Research data mining machine learning data envelopment analysis multi-criteria decisionmaking covid-19. |
| title | A Hybrid Methodology of Data Science and Decision Making Techniques: Lessons from COVID-19 Pandemic Management |
| title_full | A Hybrid Methodology of Data Science and Decision Making Techniques: Lessons from COVID-19 Pandemic Management |
| title_fullStr | A Hybrid Methodology of Data Science and Decision Making Techniques: Lessons from COVID-19 Pandemic Management |
| title_full_unstemmed | A Hybrid Methodology of Data Science and Decision Making Techniques: Lessons from COVID-19 Pandemic Management |
| title_short | A Hybrid Methodology of Data Science and Decision Making Techniques: Lessons from COVID-19 Pandemic Management |
| title_sort | hybrid methodology of data science and decision making techniques lessons from covid 19 pandemic management |
| topic | data mining machine learning data envelopment analysis multi-criteria decisionmaking covid-19. |
| url | http://ijiepr.iust.ac.ir/article-1-2095-en.pdf |
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