Gönenç Yücel
Biographic Data
| ID | 4981317 |
|---|---|
| NAME | Gönenç Yücel |
| GIVEN NAMES | Gönenç |
| FAMILY NAME | Yücel |
| SIGNATURE | YÜCEL G |
| AFFILIATIONS | Bogaziçi University |
| ORCID | 0000-0002-0998-075X |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 6 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2014 |
| LATEST PUBLICATION YEAR | 2020 |
| H-INDEX | 1 |
Analysis of an individual‐based influenza epidemic model using random forest metamodels and adaptive sequential sampling
This study proposes a three‐step procedure for the analysis of input–response relationships of dynamic models, which enables the analyst to develop a better understanding about the dynamics of the system. The main building block of the procedure is a random forest metamodel capturing the input–output relationships. We utilize an active learning approach as the second step to improve the accuracy of the metamodel. In the last step, we develop a no…
Automated Analysis of Regularities Between Model Parameters and Output Using Support Vector Regression in Conjunction with Decision Trees
Opening the black-box of nonlinear relationships between model inputs and outputs significantly contributes to the understanding about the dynamic problem being studied. Considering the weaknesses and disadvantages of human-guided and systematic techniques o ered in the literature, this paper presents a model analysis and exploration tool for agent-based models. The tool first approximates the input-output relationship by developing a metamodel, …
Impact of Homophily on Diffusion Dynamics Over Social Networks
The purpose of this study is to analyze the impact of homophily on diffusion over social networks. An agent-based simulation model is developed to serve as the experimental ground for this analysis. Diffusion dynamics of a nonsticky innovation is investigated by varying homophily levels in the social network depicted in the model as the primary control variable. First of all, the results show that homophily is self-reinforcing. Second, starting f…
Impact of Homophily on Diffusion Dynamics Over Social Networks
The purpose of this study is to analyze the impact of homophily on diffusion over social networks. An agent-based simulation model is developed to serve as the experimental ground for this analysis. Diffusion dynamics of a nonsticky innovation is investigated by varying homophily levels in the social network depicted in the model as the primary control variable. First of all, the results show that homophily is self-reinforcing. Second, starting f…
Impact of Homophily on Diffusion Dynamics Over Social Networks
The purpose of this study is to analyze the impact of homophily on diffusion over social networks. An agent-based simulation model is developed to serve as the experimental ground for this analysis. Diffusion dynamics of a nonsticky innovation is investigated by varying homophily levels in the social network depicted in the model as the primary control variable. First of all, the results show that homophily is self-reinforcing. Second, starting f…
Automated Analysis of Regularities Between Model Parameters and Output Using Support Vector Regression in Conjunction with Decision Trees
Opening the black-box of nonlinear relationships between model inputs and outputs significantly contributes to the understanding about the dynamic problem being studied. Considering the weaknesses and disadvantages of human-guided and systematic techniques o ered in the literature, this paper presents a model analysis and exploration tool for agent-based models. The tool first approximates the input-output relationship by developing a metamodel, …
Analysis of an individual‐based influenza epidemic model using random forest metamodels and adaptive sequential sampling
This study proposes a three‐step procedure for the analysis of input–response relationships of dynamic models, which enables the analyst to develop a better understanding about the dynamics of the system. The main building block of the procedure is a random forest metamodel capturing the input–output relationships. We utilize an active learning approach as the second step to improve the accuracy of the metamodel. In the last step, we develop a no…
Computer Science (3 works) · Artificial Intelligence (2 works) · Data mining (2 works) · Machine learning (2 works) · Mathematics (2 works) · Metamodeling (2 works) · Black box (1 work) · Complex Network Analysis Techniques (1 work) · Decision tree (1 work) · Diffusion (1 work)