Yöetim Bilişim Sistemleri Bölümüne ait koleksiyonlar bu alt bölümde listelenir. https://hdl.handle.net/20.500.12294/3979 2026-11-10T23:31:11Z 2026-11-10T23:31:11Z Atas, Pinar Karadayi https://hdl.handle.net/20.500.12294/3963 2023-11-10T12:20:13Z 2023-01-01T00:00:00Z

A novel hybrid model to predict concomitant diseases for Hashimoto’s thyroiditis Atas, Pinar Karadayi Hashimoto’s thyroiditis is an autoimmune disorder characterized by the destruction of thyroid cells through immune-mediated mechanisms involving cells and antibodies. The condition can trigger disturbances in metabolism, leading to the development of other autoimmune diseases, known as concomitant diseases. Multiple concomitant diseases may coexist in a single individual, making it challenging to diagnose and manage them effectively. This study aims to propose a novel hybrid algorithm that classifies concomitant diseases associated with Hashimoto’s thyroiditis based on sequences. The approach involves building distinct prediction models for each class and using the output of one model as input for the subsequent one, resulting in a dynamic decision-making process. Genes associated with concomitant diseases were collected alongside those related to Hashimoto’s thyroiditis, and their sequences were obtained from the NCBI site in fasta format. The hybrid algorithm was evaluated against common machine learning algorithms and their various combinations. The experimental results demonstrate that the proposed hybrid model outperforms existing classification methods in terms of performance metrics. The significance of this study lies in its two distinctive aspects. Firstly, it presents a new benchmarking dataset that has not been previously developed in this field, using diverse methods. Secondly, it proposes a more effective and efficient solution that accounts for the dynamic nature of the dataset. The hybrid approach holds promise in investigating the genetic heterogeneity of complex diseases such as Hashimoto’s thyroiditis and identifying new autoimmune disease genes. Additionally, the results of this study may aid in the development of genetic screening tools and laboratory experiments targeting Hashimoto’s thyroiditis genetic risk factors. New software, models, and techniques for computing, including systems biology, machine learning, and artificial intelligence, are used in our study. © 2023, BioMed Central Ltd., part of Springer Nature.

2023-01-01T00:00:00Z Atas, Pinar Karadayi https://hdl.handle.net/20.500.12294/3954 2023-11-10T12:22:44Z 2023-01-01T00:00:00Z

Evaluate student achievement by classifying brain structure and its functionality with novel hybrid method Atas, Pinar Karadayi In a labor market that demands a workforce well-trained in science, technology, engineering, and mathematics (STEM) subjects, it is required of children to successfully develop their mathematical skills in order to become highly productive adults. Recent developments in computer vision, artificial intelligence, machine learning, and medical imaging techniques give us new opportunities for building intelligent support tools to help us learn more about the neural underpinnings of how children learn math and how that knowledge relates to individual differences in skill. This study examines the brain activities of students during problem-solving by checking brain structure and its functionality. By using powerful techniques in the light of machine learning and image processing, the relationship between success and the background of a child was researched. The aim is to make a solid prediction of the possible future success of the children by observing their brain activities. The children we investigated were asked different questions to get information about their intelligence. In our study, we have tried to find how those questions and answers may affect the future success of a child. For this purpose, a novel hybrid classification model that utilizes cluster analysis, Random Forest, Logistic Regression, and ensemble learning is intended for classification tasks. Our study includes two main stages. Firstly, the image processing techniques were applied to create unique features of brain images. Then, machine learning tecnniques were used to select a set of features, and for getting prediction results our hybrid classification model was applied. In the end, we obtained useful results indicating that there is a complicated connection between the success rate and the history of a child. This novel approach to classification, which combines multiple methods by using a hybrid model, has the potential to be implemented in computational tools for strategic decision support systems.

2023-01-01T00:00:00Z Göv, Abdullah Yılancı, Veli https://hdl.handle.net/20.500.12294/3938 2023-11-10T12:24:31Z 2023-01-01T00:00:00Z

Ekonomik Büyüme, Enerji Tüketimi, Dış Ticaret Dengesi ve Finansal Gelişme İlişkisine Grafiksel Nedensellik Testi Yaklaşımı Göv, Abdullah; Yılancı, Veli Bu çalışmanın amacı, gelişmekte olan 30 ülke özelinde ekonomik büyüme ile enerji tüketimi, finansal gelişme ve dış ticareti dengesi arasındaki ilişkiyi incelemektir. Analizde LASSO ile Kesik LASSO(KLASSO) Grafiksel Granger Nedensellik(GGN) yaklaşımlarından yararlanılmıştır. Elde edilen sonuçlarda, KLASSO GGN testi ekonomik büyümeden dış ticaret dengesine doğru tek yönlü, LASSO GGN testi ise bu iki değişken arasında çift yönlü bir nedensellik ilişkisinin olduğu sonucunu vermiştir. Bununla birlikte iki testin bulgularında, nedensellik ilişkisinin finansal gelişme ile enerji tüketiminden ekonomik büyümeye doğru tek yönlü olduğu tespit edilmiştir. Ampirik bulgular enerji tüketimi-ekonomik büyüme ilişkisinde büyüme hipotezini, finansal gelişme-ekonomik büyüme ilişkisinde ise talep takipli hipotezi desteklemektedir. Buna ilaveten incelenen ülke grubu ve dönemde (1990-2019) ekonomik büyümenin dış ticaret dengesinin değişimi üzerinde etkisi olan önemli bir faktör olduğuna dair kanıtlar elde edilmiştir.

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