Abstract: Stories, which are the result of a short text, adventure or experience description, reflect real or possible events-situations. Ömer Seyfettin, the representative of event storytelling in Turkish literature, is an important writer with his nationalist and patriotic identity. Ömer Seyfettin, who wrote many works in the genre of stories, generally addressed the social, historical and psychological issues of the founding years of the Republic of Turkey in these stories, as well as themes such as national values, human psychology, moral issues and social changes. The aim of this study is to examine the sentence structure in Ömer Seyfettin's stories called Miracle. The limitations of the study are determined as the entire story of Ömer Seyfettin's Miracle. The method in this study is the analysis of sentence structure, which is a grammar method. Many linguists have done studies on the classification and definition of sentence structure until today. Sentence structures, which express a meaningful whole in linguistics, serve to explain the language and the judgment intended to be expressed. Ömer Seyfettin's stories are suitable for sentence structure studies and are important.
Sentence structure in Omar Sayfuddin's story of Miracle
Keywords: Story
Sentence Structure
Omar Sayfuddin
Turkish Literature
Linguistic Analysis
Publication Date
Sat Dec 30 2023
Journal Name
Journal Of Economics And Administrative Sciences
The Cluster Analysis by Using Nonparametric Cubic B-Spline Modeling for Longitudinal Data
البيانات الطولية
نموذج الشرائح B-spline التكعيبية اللامعلمية
التحليل العنقودي
طريقة الاتجاه المتناوب لخوارزمية المضاعف ADMM.
Longitudinal Data
Nonparametric Cubic B-Spline
Cluster Analysis
The Alternating Direction Method for Multiplier Algorithm ADMM.
Longitudinal data is becoming increasingly common, especially in the medical and economic fields, and various methods have been analyzed and developed to analyze this type of data.
In this research, the focus was on compiling and analyzing this data, as cluster analysis plays an important role in identifying and grouping co-expressed subfiles over time and employing them on the nonparametric smoothing cubic B-spline model, which is characterized by providing continuous first and second derivatives, resulting in a smoother curve with fewer abrupt changes in slope. It is also more flexible and can pick up on more complex patterns and fluctuations in the data.
The longitudinal balanced data profile was compiled into subgroup
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