Yüksel, Mustafa Kerem

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Profile URL
https://hdl.handle.net/20.500.14411/8223
Name Variants
Yuksel, Mustafa Kerem
M.,Yüksel
Y.,Mustafa Kerem
M.K.Yuksel
Y., Mustafa Kerem
M.K.Yüksel
Yuksel,M.K.
Yüksel,M.K.
Yüksel, Mustafa Kerem
Mustafa Kerem, Yüksel
M., Yuksel
Mustafa Kerem, Yuksel
Job Title
Doktor Öğretim Üyesi
Email Address
kerem.yuksel@atilim.edu.tr
Main Affiliation
Department of Business
Status
Former Staff
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID

Sustainable Development Goals

NO POVERTY1
NO POVERTY
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ZERO HUNGER2
ZERO HUNGER
0
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GOOD HEALTH AND WELL-BEING3
GOOD HEALTH AND WELL-BEING
1
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QUALITY EDUCATION4
QUALITY EDUCATION
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GENDER EQUALITY5
GENDER EQUALITY
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CLEAN WATER AND SANITATION6
CLEAN WATER AND SANITATION
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AFFORDABLE AND CLEAN ENERGY7
AFFORDABLE AND CLEAN ENERGY
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DECENT WORK AND ECONOMIC GROWTH8
DECENT WORK AND ECONOMIC GROWTH
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INDUSTRY, INNOVATION AND INFRASTRUCTURE9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
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REDUCED INEQUALITIES10
REDUCED INEQUALITIES
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SUSTAINABLE CITIES AND COMMUNITIES11
SUSTAINABLE CITIES AND COMMUNITIES
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RESPONSIBLE CONSUMPTION AND PRODUCTION12
RESPONSIBLE CONSUMPTION AND PRODUCTION
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CLIMATE ACTION13
CLIMATE ACTION
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LIFE BELOW WATER14
LIFE BELOW WATER
0
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LIFE ON LAND15
LIFE ON LAND
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PEACE, JUSTICE AND STRONG INSTITUTIONS16
PEACE, JUSTICE AND STRONG INSTITUTIONS
0
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PARTNERSHIPS FOR THE GOALS17
PARTNERSHIPS FOR THE GOALS
0
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Scholarly Output

2

Articles

1

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2/39

Supervised MSc Theses

1

Supervised PhD Theses

0

WoS Citation Count

0

Scopus Citation Count

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0.00

Scopus Citations per Publication

0.00

Open Access Source

1

Supervised Theses

1

JournalCount
Journal of the Turkish Operations Management (JTOM)1
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    Article
    Data Driven Approach for Weight Restricted Data Envelopment Analysis Models With Single Output
    (2023) Kurt, Şenol; Yüksel, Mustafa Kerem; Dinçergök, Burcu
    This study aims to explore whether a machine learning algorithm can be used to make improvements in assessing unit efficiencies via a data envelopment analysis (DEA) model. In this study, a DEA model is used to calculate the efficiency scores of Desicion Making Units (DMUs). Then, an ML algorithm is trained that aims to predict the single output using inputs. Ranking of input features based on relative feature importance values obtained from the trained ML model is fed to the DEA model as weight restrictions. As a result, the two DEA models are compared with each other. ML-based insights (feature importance ranking) improve the DEA model in the direction of fewer zero weights. The additional weight restrictions are data depdendent, and hence realistic. As a novel approach, this study proposes the use of machine learning-based feature importance values to overcome a limitation of a DEA model.