Yerlikaya Özkurt, Fatma
Loading...
Profile URL
Name Variants
Yerlikaya-Özkurt,F.
F., Yerlikaya Ozkurt
Y.,Fatma
Y., Fatma
Yerlikaya Özkurt,F.
Fatma Yerlikaya Özkurt
Yerlikaya Ozkurt,Fatma
Yerlikaya Ozkurt,F.
F., Yerlikaya Özkurt
Yerlikaya Ozkurt, Fatma
Fatma, Yerlikaya Özkurt
F.,Yerlikaya Özkurt
Y.Ö.Fatma
F.,Yerlikaya Ozkurt
Fatma, Yerlikaya Ozkurt
Y. Ö. Fatma
Y. O. Fatma
Yerlikaya Özkurt, Fatma
Yerlikaya-Ozkurt, Fatma
Yerlikaya-Ozkurt, Fatma
Yerlikaya-oezkurt, Fatma
Yerlikaya-Ozkurt,F.
Özkurt, Fatma Yerlikaya
Ozkurt, F. Yerlikaya
F., Yerlikaya Ozkurt
Y.,Fatma
Y., Fatma
Yerlikaya Özkurt,F.
Fatma Yerlikaya Özkurt
Yerlikaya Ozkurt,Fatma
Yerlikaya Ozkurt,F.
F., Yerlikaya Özkurt
Yerlikaya Ozkurt, Fatma
Fatma, Yerlikaya Özkurt
F.,Yerlikaya Özkurt
Y.Ö.Fatma
F.,Yerlikaya Ozkurt
Fatma, Yerlikaya Ozkurt
Y. Ö. Fatma
Y. O. Fatma
Yerlikaya Özkurt, Fatma
Yerlikaya-Ozkurt, Fatma
Yerlikaya-Ozkurt, Fatma
Yerlikaya-oezkurt, Fatma
Yerlikaya-Ozkurt,F.
Özkurt, Fatma Yerlikaya
Ozkurt, F. Yerlikaya
Job Title
Doktor Öğretim Üyesi
Email Address
fatma.yerlikaya@atilim.edu.tr
Main Affiliation
Industrial Engineering
Status
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Sustainable Development Goals
7
AFFORDABLE AND CLEAN ENERGY
2
Research Products
8
DECENT WORK AND ECONOMIC GROWTH
2
Research Products
11
SUSTAINABLE CITIES AND COMMUNITIES
7
Research Products
13
CLIMATE ACTION
2
Research Products
This researcher does not have a Scopus ID.
This researcher does not have a WoS ID.
Scholarly Output
17
Articles
13
Views / Downloads
104/508
Supervised MSc Theses
2
Supervised PhD Theses
0
WoS Citation Count
61
Scopus Citation Count
63
WoS h-index
4
Scopus h-index
5
Patents
0
Projects
3
WoS Citations per Publication
3.59
Scopus Citations per Publication
3.71
Open Access Source
9
Supervised Theses
2
Google Analytics Visitor Traffic
| Journal | Count |
|---|---|
| Natural Hazards | 2 |
| Journal of Industrial and Management Optimization | 2 |
| Journal of Applied Statistics | 2 |
| Journal of Dynamics and Games | 1 |
| Journal of Statistics and Management Systems | 1 |
Current Page: 1 / 3
Scopus Quartile Distribution
Competency Cloud
17 results
Scholarly Output Search Results
Now showing 1 - 10 of 17
Article Citation - WoS: 17Citation - Scopus: 17A New Outlier Detection Method Based on Convex Optimization: Application To Diagnosis of Parkinson's Disease(Taylor & Francis Ltd, 2021) Taylan, Pakize; Yerlikaya-Ozkurt, Fatma; Bilgic Ucak, Burcu; Weber, Gerhard-WilhelmNeuroscience is a combination of different scientific disciplines which investigate the nervous system for understanding of the biological basis. Recently, applications to the diagnosis of neurodegenerative diseases like Parkinson's disease have become very promising by considering different statistical regression models. However, well-known statistical regression models may give misleading results for the diagnosis of the neurodegenerative diseases when experimental data contain outlier observations that lie an abnormal distance from the other observation. The main achievements of this study consist of a novel mathematics-supported approach beside statistical regression models to identify and treat the outlier observations without direct elimination for a great and emerging challenge in humankind, such as neurodegenerative diseases. By this approach, a new method named as CMTMSOM is proposed with the contributions of the powerful convex and continuous optimization techniques referred to as conic quadratic programing. This method, based on the mean-shift outlier regression model, is developed by combining robustness of M-estimation and stability of Tikhonov regularization. We apply our method and other parametric models on Parkinson telemonitoring dataset which is a real-world dataset in Neuroscience. Then, we compare these methods by using well-known method-free performance measures. The results indicate that the CMTMSOM method performs better than current parametric models.Article Citation - WoS: 2Citation - Scopus: 2Enhancing Classification Modeling Through Feature Selection and Smoothness: a Conic-Fused Lasso Approach Integrated With Mean Shift Outlier Modelling(Amer inst Mathematical Sciences-aims, 2025) Yerlikaya-Ozkurt, Fatma; Taylan, PakizeOutlier detection and variable selection are among main objectives of statistical analysis. In our study, we address the outlier problem for classification by using the Mean Shift Outlier Model (CLMSOM). Since the MSOM has more coefficients than the linear regression model, the complexity of the model MSOM is high. Therefore, we consider feature selection for MSOM by using fused Lasso (FLasso), which is beneficial and helpful in the cases where the number of explanatory variables or features is greater than the sample size. FLasso is penalizing both the coefficients and their successive differences by the L-1-norm, and it allows sparsity for both of them, while Lasso only allows the coefficients by considering a nonsmooth optimization problem. In this study, we take into account Iterated Ridge approximation which enables us to use a smooth optimization for FLasso problem. Generated smooth optimization problem is solved by using one of continuous optimization techniques called Conic Quadratic Programming (CQP), which is enabling the utilization of interior point methods. The newly developed method is called Conic FLasso for classification by MSOM (C-FLasso-CLMSOM) and is applied to real world data set to show its performance.Article Citation - WoS: 1Citation - Scopus: 1Modeling of Kappa Factor Using Multivariate Adaptive Regression Splines: Application To the Western Türkiye Ground Motion Dataset(Springer, 2024) Kurtulmus, Tevfik Ozgur; Yerlikaya-Ozkurt, Fatma; Askan, AysegulThe recent seismic activity on Turkiye's west coast, especially in the Aegean Sea region, shows that this region requires further attention. The region has significant seismic hazards because of its location in an active tectonic regime of North-South extension with multiple basin structures on soft soil deposits. Recently, despite being 70 km from the earthquake source, the Samos event (with a moment magnitude of 7.0 on October 30, 2020) caused significant localized damage and collapse in the Izmir city center due to a combination of basin effects and structural susceptibility. Despite this activity, research on site characterization and site response modeling, such as local velocity models and kappa estimates, remains sparse in this region. Kappa values display regional characteristics, necessitating the use of local kappa estimations from previous earthquake data in region-specific applications. Kappa estimates are multivariate and incorporate several characteristics such as magnitude and distance. In this study, we assess and predict the trend in mean kappa values using three-component strong-ground motion data from accelerometer sites with known VS30 values throughout western Turkiye. Multiple linear regression (MLR) and multivariate adaptive regression splines (MARS) were used to build the prediction models. The effects of epicentral distance Repi, magnitude Mw, and site class (VS30) were investigated, and the contributions of each parameter were examined using a large dataset containing recent seismic activity. The models were evaluated using well-known statistical accuracy criteria for kappa assessment. In all performance measures, the MARS model outperforms the MLR model across the selected sites.Article Citation - WoS: 2A new approach to adaptive spline threshold autoregression by using Tikhonov regularization and continuous optimization(Taru Publications, 2019) Yalaz, S.; Taylan, P.; Ozkurt, F. YerlikayaIn this study adaptive spline threshold autoregression and conic quadratic programming is used to develope conic adaptive spline threshold autoregression. With the introduced approach the second stepwise algorithm of adaptive spline threshold autoregression model turned to the Tikhonov regularization problem which was transformed into conic quadratic programming problem. The aim is to attain an optimum solution chosen in many solutions obtained by determining the bounds of the optimization problem using multiobjective optimization approach. Furthermore, in application part we used two different data set to compare performances of linear regression, adaptive spline threshold autoregression and conic adaptive spline threshold autoregression approaches.Article Citation - WoS: 4Citation - Scopus: 5Strategic Electricity Production Planning of Turkey Via Mixed Integer Programming Based on Time Series Forecasting(Mdpi, 2023) Yoruk, Gokay; Bac, Ugur; Yerlikaya-Ozkurt, Fatma; Unlu, Kamil DemirberkThis study examines Turkey's energy planning in terms of strategic planning, energy policy, electricity production planning, technology selection, and environmental policies. A mixed integer optimization model is proposed for strategic electricity planning in Turkey. A set of energy resources is considered simultaneously in this research, and in addition to cost minimization, different strategic level policies, such as CO2 emission reduction policies, energy resource import/export restriction policies, and renewable energy promotion policies, are also considered. To forecast electricity demand over the planning horizon, a variety of forecasting techniques, including regression methods, exponential smoothing, Winter's method, and Autoregressive Integrated Moving Average methods, are used, and the best method is chosen using various error measures. The optimization model constructed for Turkey's Strategic Electricity Planning is obtained for two different planning intervals. The findings indicate that the use of renewable energy generation options, such as solar, wind, and hydroelectric alternatives, will increase significantly, while the use of fossil fuels in energy generation will decrease sharply. The findings of this study suggest a gradual increase in investments in renewable energy-based electricity production strategies are required to eventually replace fossil fuel alternatives. This change not only reduces investment, operation, and maintenance costs, but also reduces emissions in the long term.Article Citation - WoS: 3Citation - Scopus: 4Cmars: a Powerful Predictive Data Mining Package in R(Elsevier, 2023) Yerlikaya-oezkurt, Fatma; Yazici, Ceyda; Batmaz, InciConic Multivariate Adaptive Regression Splines (CMARS) is a very successful method for modeling nonlinear structures in high-dimensional data. It is based on MARS algorithm and utilizes Tikhonov regularization and Conic Quadratic Optimization (CQO). In this paper, the open-source R package, cmaRs, built to construct CMARS models for prediction and binary classification is presented with illustrative applications. Also, the CMARS algorithm is provided in both pseudo and R code. Note here that cmaRs package provides a good example for a challenging implementation of CQO based on MOSEK solver in R environment by linking R MOSEK through the package Rmosek.Conference Object Comparison of Field Survey-Based Macroseismic Intensity Map and Numerical Macroseismic Intensity Maps Using Mars and Multiple Regression Methods for 6th of February 2023 Kahramanmaraş Earthquakes(Springer Science and Business Media Deutschland GmbH, 2024) Albayrak, Kubilay; Askan, Aysegul; Yerlikaya-Ozkurt, FatmaEarthquakes are natural events that cause damage to built environments by the instant seismic energy release. This energy is measured by instrumental devices to obtain the peak ground motion parameters such as Peak Ground Acceleration (PGA) and Peak Ground Velocity (PGV). Additional measurements based on the questionnaires after the earthquakes are required to identify the felt or macro seismic intensity levels. These measurements are crucial to identify the total effects of earthquakes over not only an area but also for the spatial distribution of ground motion parameters. For this purpose, it is important to study the multi-variable criteria correlations between ground motion parameters and Modified Mercalli Intensity (MMI) levels based on linear relationships of predictor variable couples. In this regard, the Multivariate Adaptive Regression Splines (MARS) Method and the Multiple Linear Regression Method are used. The entire dataset is composed of 69 earthquakes between 2005 and 2022 with 2171 ground motion parameters coupled with MMI levels. For MMI-based correlations, the MARS method is used to identify the non-linearities between predictor variables by piecewise linear functions, but for the Multiple Linear Regression Method, the least correlated variables of PGA-Epicentral Distance and PGV-Epicentral Distance are used to obtain the relationship between MMI and PGM parameters. The resulting equations obtained for the entire Turkiye database are performed to identify MMI maps of the 6th of February 2023, Kahramanmaras Earthquakes, and these maps are used to check the accuracy of the results by the comparison of field survey-based MMI maps. Finally, the numerical MMI maps are found to be consistent with the field survey-based MMI maps.Article Citation - WoS: 1Citation - Scopus: 1Spline Based Sparseness and Smoothness for Partially Nonlinear Model Via C-Fused Lasso(American Institute of Mathematical Sciences, 2025) Taylan, P.; Yerlikaya-¨Ozkurt, F.; Tez, M.One of the most beneficial and widely used models for data analysis are partially nonlinear models (PNLRM), which consists of parametric and nonparametric components. Since the model includes the coefficients of both the parametric and nonparametric parts, the complexity of the model will be high and its interpretation will be very difficult. In this study, we propose a procedure that not only achieves sparseness, but also smoothness for PNLRM to obtain a simpler model that better explains the relationship between the response and covariates. Thus, the fused Lasso problem is taken into account where nonparametric components are expressed as a spline basis function, and then the Fused Lasso estimation problem is built and expressed in terms of conic quadratic programming. Applications are conducted to evaluate the performance of the proposed method by considering commonly utilized measures. Promising results are obtained, especially in the data with nonlinearly correlated variables. © (2025), (American Institute of Mathematical Sciences). All rights reserved.Master Thesis Elektrik Talep Tahminine Dayalı Karma Tam Sayılı Programlama ile Türkiye'nin Stratejik Enerji Üretimi Planlaması(2021) Yörük, Gökay; Baç, Uğur; Özkurt, Fatma YerlikayaBu tezde, Türkiye için enerji planlama problemi, stratejik planlama, enerji politikası, enerji güç kapasite planlaması, teknoloji seçimi ve çevre politikaları açısından ele alınmaktadır. Stratejik elektrik planlaması kapsamında; fosil yakıtlar, yenilenebilir enerji, nükleer enerji gibi alternatif teknolojileri göz önünde bulunduran karışık tam sayılı matematiksel programlama modeli önerilmiştir. Çalışmada maliyetin (yatırım, operasyon ve bakım) en küçüklenmesine ek olarak, CO2 emisyonunun sınırlandırılması, enerji kaynak paylaşımı kısıtlama politikaları ve yenilenebilir enerji teşvik politikaları gibi hususlar önerilen modelde ele alınmıştır. Planlama sürecinde elektrik talebini tahmin etmek için regresyon metotları, üstel düzeltme, Winter ve Otoregresif Entegre Hareketli Ortalama (ARIMA) yöntemleri gibi bir dizi tahmin tekniği kullanılmış ve farklı hata ölçüm kriterleri kullanılarak en iyi yöntem seçilmiştir. Modelin bir uygulaması olarak Türkiye'nin Stratejik Elektrik Planlama Problemi ele alınmış ve iki farklı (2021-2030 ve 2021-2040) planlama aralığı için çözülmüştür. Sonuçlar, yenilenebilir enerji üretim seçenekleri olan güneş, rüzgâr ve hidroelektrik alternatiflerinin kullanımının önemli ölçüde artacağını, enerji üretiminde fosil yakıtların kullanımının ise belirgin bir şekilde azalacağını göstermektedir. Sonuç olarak, bu araştırma yenilenebilir enerji yatırımlarının kademeli olarak artırılmasını ve uzun vadede fosil yakıt alternatiflerinin yerini almasını önermektedir. Bu değişiklik yalnızca yatırım, işletme ve bakım maliyetlerini düşürmekle kalmayacak, aynı zamanda emisyon seviyesini de önemli ölçüde düşürecektir.Article Citation - WoS: 2Citation - Scopus: 2Estimation in the Partially Nonlinear Model by Continuous Optimization(Taylor & Francis Ltd, 2021) Yerlikaya-Ozkurt, Fatma; Taylan, Pakize; Tez, MujganA useful model for data analysis is the partially nonlinear model where response variable is represented as the sum of a nonparametric and a parametric component. In this study, we propose a new procedure for estimating the parameters in the partially nonlinear models. Therefore, we consider penalized profile nonlinear least square problem where nonparametric components are expressed as a B-spline basis function, and then estimation problem is expressed in terms of conic quadratic programming which is a continuous optimization problem and solved interior point method. An application study is conducted to evaluate the performance of the proposed method by considering some well-known performance measures. The results are compared against parametric nonlinear model.
