Özbey, Mehmet Furkan

Profile Picture
Profile URL
https://hdl.handle.net/20.500.14411/6647
Name Variants
M. F. Ozbey
Özbey, Mehmet Furkan
Ö.,Mehmet Furkan
Mehmet Furkan, Ozbey
O., Mehmet Furkan
Ö., Mehmet Furkan
O.,Mehmet Furkan
M.F.Özbey
M.,Özbey
M., Ozbey
Mehmet Furkan, Özbey
Mehmet Furkan Özbey
Özbey,M.F.
Ozbey, Mehmet Furkan
Ozbey,M.F.
M. F. Özbey
Ozbey,Mehmet Furkan
M.F.Ozbey
Job Title
Araştırma Görevlisi
Email Address
furkan.ozbey@atilim.edu.tr
Main Affiliation
Mechanical Engineering
Status
Website
https://www.linkedin.com/in/mfozbey/
ORCID ID
0000-0002-5813-3514
Scopus Author ID
57219871456
Turkish CoHE Profile ID
Google Scholar ID
KIavFZMAAAAJ
WoS Researcher ID
GLU-8252-2022

Sustainable Development Goals

NO POVERTY1
NO POVERTY
0
Research Products
ZERO HUNGER2
ZERO HUNGER
0
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GOOD HEALTH AND WELL-BEING3
GOOD HEALTH AND WELL-BEING
0
Research Products
QUALITY EDUCATION4
QUALITY EDUCATION
3
Research Products
GENDER EQUALITY5
GENDER EQUALITY
1
Research Products
CLEAN WATER AND SANITATION6
CLEAN WATER AND SANITATION
0
Research Products
AFFORDABLE AND CLEAN ENERGY7
AFFORDABLE AND CLEAN ENERGY
1
Research Products
DECENT WORK AND ECONOMIC GROWTH8
DECENT WORK AND ECONOMIC GROWTH
0
Research Products
INDUSTRY, INNOVATION AND INFRASTRUCTURE9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
0
Research Products
REDUCED INEQUALITIES10
REDUCED INEQUALITIES
1
Research Products
SUSTAINABLE CITIES AND COMMUNITIES11
SUSTAINABLE CITIES AND COMMUNITIES
7
Research Products
RESPONSIBLE CONSUMPTION AND PRODUCTION12
RESPONSIBLE CONSUMPTION AND PRODUCTION
1
Research Products
CLIMATE ACTION13
CLIMATE ACTION
0
Research Products
LIFE BELOW WATER14
LIFE BELOW WATER
0
Research Products
LIFE ON LAND15
LIFE ON LAND
0
Research Products
PEACE, JUSTICE AND STRONG INSTITUTIONS16
PEACE, JUSTICE AND STRONG INSTITUTIONS
0
Research Products
PARTNERSHIPS FOR THE GOALS17
PARTNERSHIPS FOR THE GOALS
0
Research Products
Documents

16

Citations

173

h-index

8

Go to Scopus profile
Documents

14

Citations

167

Go to WoS profile
Scholarly Output

18

Articles

15

Views / Downloads

39/32

Supervised MSc Theses

1

Supervised PhD Theses

0

WoS Citation Count

167

Scopus Citation Count

185

Patents

0

Projects

0

WoS Citations per Publication

9.28

Scopus Citations per Publication

10.28

Open Access Source

6

Supervised Theses

1

JournalCount
Buildings2
Energy and Buildings2
E3S Web of Conferences -- 11th International Conference on Indoor Air Quality, Ventilation and Energy Conservation in Buildings, IAQVE C2023 -- 20 May 2023 through 23 May 2023 -- Tokyo -- 1894041
Indoor Air1
Indoor and Built Environment1
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Has files: trueSubject: Adaptive thermal comfort

Scholarly Output Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    Article
    Citation - WoS: 19
    Citation - Scopus: 21
    Gender Inequity in Thermal Sensation Based on Emotional Intensity for Participants in a Warm Mediterranean Climate Zone
    (Elsevier France-editions Scientifiques Medicales Elsevier, 2023) Ceter, Aydin Ege; Ozbey, Mehmet Furkan; Turhan, Cihan
    The deficiencies of the one of the most preferred conventional thermal comfort models, the Predicted Mean Vote/ Percentage of Predicted Dissatisfied (PMV/PPD) method have emerged over time since the model does not take psychological parameters such as personal traits, mood states and adaptation into account. Therefore, re-searchers have focused on Adaptive Thermal Comfort models that integrate human behaviours into the model for better prediction of thermal comfort. In addition to the influence of the behaviours of occupants, thermal comfort may be evaluated as a subjective term, thus, the effect of one of the psychological parameters, current mood state, on thermal sensation cannot be ignored for predictions. Although, the effect of current mood state on thermal sensation is a vital concept, the findings of the studies are not effective and comprehensive in the literature. For this reason, the aim of this study is to examine the relationship between current mood state and thermal sensation in gender difference aspect. Therefore, a series of experiments were conducted in a university study hall between August 16th, 2021 and August 1st, 2022. The current mood states of the participants were evaluated with the Profile of Mood States (POMS) questionnaire and the results were represented by a novel approach called Emotional Intensity Score (EIS). One tailed t-test was applied for investigating the relationship between the EIS and the thermal sensation. Findings of the research showed that a significant association exists between the EIS and thermal sensation for male participants while no relationship was found for female.
  • Thumbnail Image
    Article
    Citation - WoS: 9
    Citation - Scopus: 9
    Modeling the Mood State on Thermal Sensation With a Data Mining Algorithm and Testing the Accuracy of Mood State Correction Factor
    (Pergamon-elsevier Science Ltd, 2025) Yerlikaya-Ozkurt, Fatma; Ozbey, Mehmet Furkan; Turhan, Cihan
    Psychology is proven as an influencing factor on thermal sensation. On the other hand, mood state is one of the significant parameters in psychology field. To this aim, in the literature, mood state correction factor on thermal sensation (Turhan and Ozbey coefficients) is derived utilizing with data-driven black-box model. However, novel models which present analytical form of the mood state correction factor should be derived based on the several descriptive variables on thermal sensation. Moreover, the result of this factor should also be checked with analytical model results. Therefore, this study investigates the modelling of mood state correction factor with a data mining algorithm, called Multivariate Adaptive Regression Splines (MARS). Additionally, the mood state is also taken as a thermal sensation parameter besides environmental parameters in this algorithm. The same data, which are collected from a university study hall in a temperate climate zone, are used and the model results are compared with the thermal sensation results based on mood state correction factor which is driven via black-box model. The results show that coefficient of correlation "r" between the MARS and black-box model is found as 0.9426 and 0.9420 for training and testing. Hence, the mood state is also modelled via a data mining algorithm with a high accuracy, besides the black-box model.