Work Experience

  • Todate 2012

    Asst. Prof. Dr.

    Near East University, Mathematics

Education & Training

  • Ph.D. 2018

    Mathematics

    Near East University

  • Master2012

    Mathematics

    Near East University

  • Bachelor2010

    Applied Mathematics and Computer Science

    Eastern Meditarrenean University

Honors, Awards and Grants

  • 2017
    Young Scientist Award, NEU
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  • 2016
    Young Scientist Award, NEU
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Research Projects

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Development of an Intelligent model to Estimate the probability of having metabolic syndrome

Conference Paper Procedia Computer Science , Volume -, Issue -, 2016, Pages -

Abstract

Logistic regression has now become an essential part of medical data analysis that uses a binary-response model. The model is frequently used by epidemiologists as a model for the probability (interpreted as the risk) that an individual will acquire a disease during a specified time period, during which he or she is exposed to a condition (called a risk factor) known to be or suspected of being associated with the disease. The objective is to establish a model using a minimum number of variables, and is also able to identify the relationship between the dependent variable and independent variable. Additionally, the study will determine the risk factors that can lead to the development of metabolic syndrome (MetSyn) and will establish an intelligent and biologically acceptable model for estimating the probability of having the condition, based on the NCEP ATP III criteria. In this study, binary logistic regression analysis has been employed in order to specify the risk factors that affect metabolic syndrome. Metabolic syndrome (MetSyn) is a common metabolic disorder that is increasingly caused by the pervasiveness of obesity in society and diagnosed according to the National Cholesterol Education Program (NCEP) Adult Treatment Panel III (ATP III) Identification1 . The data has been obtained from the laboratory test results of 321 adult individuals who had consecutively been treated by the Near East University Internal Medicine Department. For this intelligent model, binary logistic regression analysis has been used. The sensitivity, specificity and accuracy rates have been detected as 94.7%, 96.0% and 95.5%, respectively. As a result, homeostatic model assessment (HOMA-IR), uric acid, body mass index (BMI), low-density lipoprotein (LDL) cholesterol, age, smoking, education level (EL) are defined as metabolic syndrome risk factors, the model has been estimated by using those variables in the acquired intelligent model. As a consequence of the research, it has been determined that the key elements that can have an impact are the changeable risk factors, meaning that the illness could be destroyed before it actually occurs, and lifestyle change, that can also prevent the illness.


Özet

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Prediction of Body Mass Index: A comparative study of multiple linear regression, ANN and ANFIS models

Conference Paper Procedia Computer Science, Volume -, Issue -, 2017, Pages -

Abstract

A report by the National Cholesterol Education Program’s Adult Treatment Panel III recognized metabolic syndrome as a multiplex risk factor for cardiovascular hearth disease (CHD) as well as type 2 diabetes; therefore, it is important to give these factors further clinical attention. Early diagnosis and prediction of disease, particularly the diseases related to metabolic syndrome, are increasing dramatically. On the other hand, most patients with metabolic syndrome are obese or overweight. In this regard, an accurate BMI estimation model based on metabolic syndrome components and risk factors provides facilities to control these modifiable components through various lifestyle changes. Thus, this study investigates if the people who have metabolic syndrome components and risk factors are expected to be obese. The central issue is selecting the appropriate model from a potentially large class of candidate models. Multiple Linear Regression (MLR) and two soft computing techniques, namely: Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) by considering Metabolic Syndrome components as input variables, were chosen and applied in this study. ANFIS is a particular form of ANN with a hybrid intelligent system. ANFIS benefits from the ANN’s superior learning algorithms and Fuzzy Inference Systems’ excellent estimation functions. Obviously, all three developed models are capable of predicting BMI value. The performance of these three estimation models (MLR, ANN and ANFIS) were compared based on RMSE, MAPE and R2. Consequently, the results indicate that the ANFIS model is more feasible than the other two models in predicting BMI.


Özet

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Modelling the impact of energy consumption and environmental sanity in Turkey: A STIRPAT framework

Conference Paper Procedia Computer Science, Volume -, Issue -, 2017, Pages -

Abstract

This study examines the causal relationship between energy consumption and the economic growth of Turkey. The analysis was conducted within the Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) framework. This is a salient improvement from the previous studies because this unique method permits synergetic modelling of energy dynamics vis-à-vis its environmental impact. The study applied the structural break unit root test, bound testing cointegration and vector error correction causality analysis. The study found that the importation of energy is the major challenge to the Turkish conservative policy. When controlling for C02 emissions in the energy import model, the study found that an approximate 0.14% increase emission emanates from imported energy for Turkey in the short-run. Similarly, 1.1% of emissions results from electricity production and other related activities over the short-run. Even though, evidence of long-run relations was only presence in the C02 emission as well as the financial development models, the correcting process from the short-run deviation to the long-run equilibrium is higher in the electricity consumption model, in that 51% short-run disequilibrium is corrected toward the long-run equilibrium per year compared with 3.3% and 4.8% in the energy import and C02 emission models, respectively. One positive aspect observed is the robustness of Turkey’s financial institution to support the energy sector. However, financial institutional support for conservational policy is pathetically weak due to the low potential return


Özet

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Body Mass Index Estimation by Using an Adaptive Neuro Fuzzy Inference System

Conference Paper Procedia Computer Science, Volume -, Issue -, 2017, Pages -

Abstract

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Özet

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Prediction of Mechanical Lower Back Pain for Healthcare Workers Using ANN and Logistic Regression Models

Conference Paper In International Conference on Theory and Applications of Fuzzy Systems and Soft Computing, Volume -, Issue -, 2018, Pages -

Abstract

The aim of this study is the comparison of predictive capabilities of logistic regression (LR) model and artificial neural network (ANN) to predict chronic mechanical lower back pain (MLBP) for healthcare workers in North Cyprus. For this purpose, the dataset has been obtained from Near East University (NEU) Hospital healthcare employees after obtaining approval from the ethics committee. Since this work was defined as exploratory, stepwise regression methods were considered to be the most appropriate and therefore, the Forward Selection and Backward Elimination methods were compared to find the proper binary LR model by using Likelihood ratio test. In order to obtain accurate results, two ANN models (ANN_1 and ANN_2) were used in this study. The main different of these two models was the number of processing elements. In the both models the Levenberg–Marquardt (LM) algorithm, as one of the most common and fastest back-propagation training algorithms was used in this study. The predictive capabilities of the binary logistic regression and ANNs was evaluated by specificity, sensitivity, accuracy rates and area under the ROC curve. The comparison results show that ANN performs better than the logistic regression model for prediction of chronic MLBP for healthcare workers. However, two models are biologically acceptable, too.


Özet

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Statistical modelling and forecasting of female breast cancer cases in Northern Cyprus

Original Article Far East Journal of Mathematical Sciences , Volume -, Issue -, 2017, Pages -

Abstract

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Özet

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Prevalence of metabolic syndrome in routine check-up program in Cyprus

Original Article The West Indian Medical journal, Volume -, Issue -, 2017, Pages -

Abstract

Objective: Metabolic syndrome (MetS) represents a conglomeration of various metabolic abnormalities, including glucose intolerance, hypertension, increased triglycerides, and decreased high-density lipoprotein cholesterol (HDL-C). This study evaluated the prevalence of MetS in a health check-up subjects. Methods: In a cross-sectional observational study designed between October 2015 and July 2016, using a simple random sampling method, 324 adults aged more than 20 years who were invited for health check-up examinations to Internal Medicine Department of Near East University Hospital. The revised National Cholesterol Education Program–Adult Treatment Panel III was used for the diagnosis of MetS. Results: A total of 324 participants included 163 men and 161 women with respective mean age of 42.77?±?16.16. The prevalence of MetS was found 37.04% (120 of 324). The prevalence was higher in men (51.5%) than in women (22.4%). The prevalence of high waist circumference (>102?cm men; >88?cm women), having three or more risk factors for MetS and low HDL-C levels were significantly higher in males compared with females (p<0.001). High blood pressure, high fasting glucose and triglycerides levels were most frequent components of the males. Conclusion: The prevalence of MetS in our population is very high, especially in men. Preventive strategies should be developed to decrease prevalence of MetS in our country.


Özet

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Cancer Incidence 2010-2014 Among the North Cyprus Population of Adults Aged 15 and Over

Original Article Turkısh Journal Of Oncology, Volume -, Issue -, 2017, Pages -

Abstract

OBJECTIVE This study is an analysis of cancer incidence among people aged 15 and over in North Cyprus (NC) with aim of helping to establish a cancer control plan based on the results. METHODS Data from 2010 to 2014 were obtained from Near East University (NEU) Hospital. Crude incidence rate (CIR) and age-standardized rate (ASR) per 100,000 were calculated for 28 different cancers for males and females. In line with GLOBOCAN 2012, ASR values for 2012 in NC were compared with South Cyprus (SC) and Southern Europe (SE). RESULTS Total of 1,782 cancer cases were diagnosed. ASR value was 180.61 and 192.75 per 100,000 person-years for males and females, respectively. Most prevalent male (M) cancers were lung, prostate, colorectal, thyroid, and bladder. Similarly, for females (F), they were breast, thyroid, colorectal, lung, and non-melanoma skin cancer. Thyroid cancer in females in NC was more prevalent than in SE. Breast (F), prostate, colorectal (F), bladder (M), and ovary and corpus were less frequent than in SE and SC; cervix, larynx (M), brain (M) and colorectal (M) only were less frequent than in SE, and the remainder were statistically similar. CONCLUSION Possible risk factors for the most common cancers in NC have been discussed; however, etiopathogenetic scientific results are still needed. Risk factor studies should be performed to raise public awareness and to plan future cancer prevention measures. The development of active cancer registry centers and combining data under a single cancer organization is recommended.


Özet

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A novel method as a diagnostic tool for the detection of influential observations in the Cox proportional hazards model

Original Article Quantity and Quality, Volume -, Issue -, 2018, Pages -

Abstract

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Özet

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Currrent Teaching

  • 2019 GÜZ

    STATISTICS I

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  • 2019 GÜZ

    STATISTICS II

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  • 2019 GÜZ

    PROBABILITY AND STATISTICS I

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Teaching History

  • 2017 GÜZ

    LINEAR ALGEBRA I

    ...

  • 2015 BAHAR

    Statistics 1

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  • 2018 AKADEMİK YILI

    STATISTICS I

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  • 2015 BAHAR

    STATISTICS I

  • 2016 BAHAR

    STATISTICS I

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  • 2017 BAHAR

    STATISTICS I

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  • 2018 BAHAR

    STATISTICS I

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  • 2015 GÜZ

    STATISTICS I

  • 2016 GÜZ

    STATISTICS I

  • 2016 GÜZ

    STATISTICS I

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  • 2017 GÜZ

    STATISTICS I

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  • 2018 GÜZ

    STATISTICS I

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  • 2017 YAZ

    STATISTICS I

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  • 2016 GÜZ

    STATISTICS II

  • 2014 BAHAR

    STATISTICS FOR ACCOUNTANTS II

  • 2018 AKADEMİK YILI

    STATISTICS II

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  • 2014 BAHAR

    STATISTICS II

  • 2015 BAHAR

    STATISTICS II

  • 2016 BAHAR

    STATISTICS II

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  • 2017 BAHAR

    STATISTICS II

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  • 2018 BAHAR

    STATISTICS II

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  • 2015 GÜZ

    STATISTICS II

  • 2016 GÜZ

    STATISTICS II

  • 2017 GÜZ

    STATISTICS II

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  • 2018 GÜZ

    STATISTICS II

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  • 2015 YAZ

    STATISTICS II

  • 2016 YAZ

    STATISTICS II

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  • 2017 YAZ

    STATISTICS II

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  • 2017 YAZ

    STATISTICS II

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  • 2016 GÜZ

    PROBABILITY AND RANDOM VARIABLES

  • 2015 YAZ

    PROBABILITY AND RANDOM VARIABLES

  • 2015 YAZ

    STATISTICAL METHODS FOR CE

  • 2018 GÜZ

    STATISTIC AND ITS APPLICATIONS IN SOCIAL SCIENCES

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  • 2015 GÜZ

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  • 2016 GÜZ

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  • 2015 BAHAR

    MATHEMATICS FOR SOCIAL SCIENCES I

  • 2014 BAHAR

    MATHEMATICS I

  • 2015 BAHAR

    MATHEMATICS I

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  • 2015 GÜZ

    MATHEMATICS I

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  • 2016 GÜZ

    MATHEMATICS I

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  • 2016 BAHAR

    MATHS. FOR BUS. & ECONOMICS I

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  • 2017 BAHAR

    MATHS. FOR BUS. & ECONOMICS I

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  • 2017 GÜZ

    MATHS. FOR BUS. & ECONOMICS I

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  • 2016 GÜZ

    MATHEMATICS FOR SOCIAL SCIENCES II

  • 2014 BAHAR

    MATHEMATICS OF BUS. & ECON. II

  • 2015 BAHAR

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  • 2015 GÜZ

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  • 2016 GÜZ

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  • 2015 BAHAR

    MATHEMATICS I

  • 2014 BAHAR

    MATHS FOR BUS AND ECONOMICS I

  • 2015 BAHAR

    MATHS FOR BUS AND ECONOMICS I

  • 2015 GÜZ

    MATHS FOR BUS AND ECONOMICS I

  • 2016 GÜZ

    MATHS FOR BUS AND ECONOMICS I

  • 2014 BAHAR

    MATHS. FOR BUS. & ECONOMCIS I

  • 2015 BAHAR

    MATHS. FOR BUS. & ECONOMCIS I

  • 2015 GÜZ

    MATHS. FOR BUS. & ECONOMCIS I

  • 2016 GÜZ

    MATHS. FOR BUS. & ECONOMCIS I

  • 2014 BAHAR

    MATHS. FOR BUS. & ECONOMICS I

  • 2014 BAHAR

    MATHS. FOR BUS. & ECONOMICS I

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  • 2015 BAHAR

    MATHS. FOR BUS. & ECONOMICS I

  • 2015 BAHAR

    MATHS. FOR BUS. & ECONOMICS I

  • 2015 BAHAR

    MATHS. FOR BUS. & ECONOMICS I

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  • 2016 BAHAR

    MATHS. FOR BUS. & ECONOMICS I

  • 2016 BAHAR

    MATHS. FOR BUS. & ECONOMICS I

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  • 2015 GÜZ

    MATHS. FOR BUS. & ECONOMICS I

  • 2015 GÜZ

    MATHS. FOR BUS. & ECONOMICS I

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  • 2016 GÜZ

    MATHS. FOR BUS. & ECONOMICS I

  • 2015 BAHAR

    MATHS. FOR BUS.& ECONOMICS I

  • 2015 GÜZ

    MATHS. FOR BUS.& ECONOMICS I

  • 2016 GÜZ

    MATHS. FOR BUS.& ECONOMICS I

  • 2014 BAHAR

    MATHEMATICS II

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  • 2016 GÜZ

    MATHEMATICS II

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  • 2014 BAHAR

    MATHS FOR BUS AND ECONOMICS II

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  • 2014 YAZ

    MATHS FOR BUS AND ECONOMICS II

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  • 2014 BAHAR

    MATHS. FOR BUS. & ECONOMCIS II

  • 2014 BAHAR

    MATHS. FOR BUS. & ECONOMCIS II

  • 2014 BAHAR

    MATHS. FOR BUS. & ECONOMCIS II

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  • 2014 BAHAR

    MATHS. FOR BUS. & ECONOMCIS II

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  • 2016 GÜZ

    MATHS. FOR BUS. & ECONOMCIS II

  • 2016 GÜZ

    MATHS. FOR BUS. & ECONOMCIS II

  • 2016 GÜZ

    MATHS. FOR BUS. & ECONOMCIS II

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  • 2014 YAZ

    MATHS. FOR BUS. & ECONOMCIS II

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  • 2014 BAHAR

    MATHS. FOR BUS. & ECONOMICS II

  • 2014 BAHAR

    MATHS. FOR BUS. & ECONOMICS II

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  • 2014 YAZ

    MATHS. FOR BUS. & ECONOMICS II

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  • 2015 GÜZ

    STATISTICS I

  • 2015 GÜZ

    STATISTICS II

  • 2015 BAHAR

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  • 2015 GÜZ

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  • 2016 GÜZ

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  • 2014 BAHAR

    MATHEMATICS I

  • 2015 BAHAR

    MATHEMATICS I

  • 2015 GÜZ

    MATHEMATICS I

  • 2016 GÜZ

    MATHEMATICS I

  • 2014 BAHAR

    MATHEMATICS II

  • 2016 BAHAR

    MATHEMATICS II

  • 2016 GÜZ

    MATHEMATICS II

  • 2017 GÜZ

    MATHEMATICS II

  • 2014 YAZ

    MATHEMATICS II

  • 2017 BAHAR

    MATHEMATICS AND GEOMETRY I

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  • 2017 GÜZ

    MATHEMATICS AND GEOMETRY I

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  • 2015 YAZ

    PROBABILITY AND STATISTICS

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  • 2015 YAZ

    PROBABILITY AND STATISTICS

    Örnekleme yöntemiyle elde edilen verileri derleme, özetleme, çözümleme, sonuçları yorumlama ve genelleme yapma. Örnek uzaylar, örnek noktalar ve olayları tanımlayarak bir olayın olasılığı ve bazı olasılık kurallarını öğrenme. Kesikli ve sürekli rasgele değişkenleri ve dağılımlarını, rasgele değişkenin beklenen değerinin özelliklerini, varyansının özelliklerini inceleme; olasılık problemlerini çözebilme. Bazı önemli kesikli ve sürekli olasılık dağılımlarını inceleme.

  • 2015 BAHAR

    PROBABILITY AND STATISTICS I

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  • 2018 BAHAR

    PROBABILITY AND STATISTICS I

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  • 2018 GÜZ

    PROBABILITY AND STATISTICS I

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  • 2016 YAZ

    PROBABILITY AND STATISTICS I

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  • 2018 YAZ

    PROBABILITY AND STATISTICS I

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  • 2018 GÜZ

    SOSYAL BİLİMLERDE İSTATİSTİK

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  • 2018 GÜZ

    MATHEMATICS FOR TOURISM AND HOTEL MANAGEMENT STUDENTS

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