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Öğe A study on the factors affecting the academic performance of distance education students and formal students(Hitit Üniversitesi, 2016) Kör, Hakan; Erbay, Hasan; Demir, Emre; Akmeşe, Ömer FarukRecently the numbers of distance education programs and students enrolling in them have increased significantly. This increase also carries the question of what factors may have an effect on academic success. The demographic features, motivation levels, personal development status and basic computer literacy of students have been taken into account and their effects on the academic success of students have been analyzed. With this purpose, surveys prepared with the help of professional opinion have been applied to certain groups. The sample group of this study consists students of the Kırıkkale University Distance Education Center and the formal education students of the Kırıkkale Vocational School. The results have been analyzed with the help of the SPSS data analysis program (version 22.0) and have been turned into figures. The academic success of distance learning students and formal education students have been compared and this data has been used to identify whether there are any significant connections between academic success and the factors determined. In the conclusion of this study, the figures have been explained separately, and suggestions have been made regarding both distance education and formal education.Öğe An interactive web application for propensity score matching with R shiny; example of thrombophilia(2020) Demir, Emre; Köse, Serdal Kenan; Akmeşe, Ömer Faruk; Yıldırım, EnginAim: The aim of this study was to develop a new web-based R Shiny package that calculates propensity score using many algorithms such as logistic regression, machine learning, and performs matching analysis with balance evaluation. In addition, it was aimed to explain the process of matching analysis on a real data set by comparing the number of live births between those with methylenetetrahydrofolate reductase (MTHFR) homozygous mutations and those without mutations in women hospitalized due to abortion in the gynecology and obstetrics clinic. Material and Methods: The web-based application was developed using R shiny. The “matchIt” library was used for matching analysis and PS prediction. The “cobalt” library was used to evaluate balance and generate plots. Results: The abortion variable, which was statistically significantly different in the groups before matching (p=0.010), was similar in the groups after matching (p=0.743). In addition, when the descriptive statistics and p values of the other variables were examined, it was seen that almost full balance was achieved after matching and the confounder variables were similar distributed in groups. After matching analysis, it was determined that the result variable “livebirths” did not show statistically significant difference in the groups (p=0.864). Conclusion: In this study, we developed an interactive web application for matching analysis based on propensity score. It is thought that this application will facilitate the studies of the researchers.Öğe Bibliometric analysis of publications on house dust mites during 1980-2018(Elsevier Espana Slu, 2020) Demir, Emre; Akmeşe, Ömer Faruk; Erbay, Hasan; Taylan Özkan, Hikmet Ayşegül; Mumcuoğlu, Kosta Y.Background: The global prevalence of allergic diseases has increased dramatically in recent years and are now recognized as significant chronic diseases worldwide. One of the most important allergens that causes allergic diseases is house dust mites. Objective: This study aims to present a bibliometric overview of research published on dust mites between 1980 and 2018. Methods: Articles published from 1980 to 2018 were analyzed using bibliometric methods. The keywords ?Dust mite*,? and ? Dermatophagoides ? were used in the Web of Science (WoS). Simple linear regression analysis was used to estimate the number of future publications on this subject. Results: A total of 4742 publications were found, 2552 (53.8%) of them were articles. Most of the articles were on subjects related to immunology (1274; 49.9%) and allergy (1229; 48.1%). Clinical and Experimental Allergy (222; 8.7%) was the journal with the most publications. The USA was the country that most contributed to the literature with 461 (18.1%) articles. The countries producing the most publications on this subject were developed countries. The most active author was W.R. Thomas (66; 2.5%). The most productive institution was the University of Western Australia (91; 3.6%). The most cited article was published in the New England Journal of Medicine .Conclusion: According to the findings, developed countries were the most productive in publishing on house dust mites. By planning multinational research rather than regional studies, it may be suggested that researchers in underdeveloped or developing countries could also-conduct more research on this subject.(C) 2019 SEICAP. Published by Elsevier Espana, S.L.U. All rights reserved.Öğe Course content development platform in distance education(Hitit Üniversitesi, 2017) Akmeşe, Ömer Faruk; Erbay, Hasan; Emiroğlu, Bülent Gürsel; Kör, HakanIt is seen that distance education method which is a rationalist, contemporary and innovative education system, is started to be used widely with transportation of education and training activities to internet area fast, in recent years. Also, distance education contributes to human’s lifelong learning through giving education opportunities to working people whose financial situation and time are limited. With the development of information technologies in the world and Turkey, distance education methods and techniques also developed. Also the more the number of students taking part in distance education rises, the more the number of institutions giving distance education rises. Needs for preparing contents and managing these contents for lessons given by distance education method appeared with this raise. On this study, a platform has been developed for lecturers to create lesson contents and the efficiency of developed implementation has been analyzed statistically. It is aimed on distance education to design an efficient content development system through thinking the roles such as lecturers, managers and studentsÖğe Data privacy-aware machine learning approach in pancreatic cancer diagnosis(BMC, 2024) Akmeşe, Ömer FarukProblem Pancreatic ductal adenocarcinoma (PDAC) is considered a highly lethal cancer due to its advanced stage diagnosis. The fve-year survival rate after diagnosis is less than 10%. However, if diagnosed early, the fve-year survival rate can reach up to 70%. Early diagnosis of PDAC can aid treatment and improve survival rates by taking necessary precautions. The challenge is to develop a reliable, data privacy-aware machine learning approach that can accurately diagnose pancreatic cancer with biomarkers. Aim The study aims to diagnose a patient’s pancreatic cancer while ensuring the confdentiality of patient records. In addition, the study aims to guide researchers and clinicians in developing innovative methods for diagnosing pancreatic cancer. Methods Machine learning, a branch of artifcial intelligence, can identify patterns by analyzing large datasets. The study pre-processed a dataset containing urine biomarkers with operations such as flling in missing values, cleaning outliers, and feature selection. The data was encrypted using the Fernet encryption algorithm to ensure confdentiality. Ten separate machine learning models were applied to predict individuals with PDAC. Performance metrics such as F1 score, recall, precision, and accuracy were used in the modeling process. Results Among the 590 clinical records analyzed, 199 (33.7%) belonged to patients with pancreatic cancer, 208 (35.3%) to patients with non-cancerous pancreatic disorders (such as benign hepatobiliary disease), and 183 (31%) to healthy individuals. The LGBM algorithm showed the highest efciency by achieving an accuracy of 98.8%. The accuracy of the other algorithms ranged from 98 to 86%. In order to understand which features are more critical and which data the model is based on, the analysis found that the features “plasma_CA19_9”, REG1A, TFF1, and LYVE1 have high importance levels. The LIME analysis also analyzed which features of the model are important in the decision-making process. Conclusions This research outlines a data privacy-aware machine learning tool for predicting PDAC. The results show that a promising approach can be presented for clinical application. Future research should expand the dataset and focus on validation by applying it to various populations.Öğe Student perceptions for distance education and efficiency analysis of the system(Hitit Üniversitesi, 2016) Akmeşe, Ömer Faruk; Demir, Emre; Dünder, EmreWith the increase in the number of universities and students requesting distance education as well as the development of Information Technologies, new institutions providing distance education have come up and this situation has made the research of the efficiency of distance education a more important issue. Considering the social and economic structure, population growth rate and young population as well as the number of students and academicians in our country, it is realized that distance education is of high importance for our country. The aim of this study is to evaluate the distance education used in the mutual courses in Hitit University in 2013-2014 educational term (Turkish Language, Ataturk’s Principles and History of Turkish Revolution) by the students, to analyze the efficiency and to develop distance education system in accordance with these findings. Survey method has been used to measure the perceptions of the students for distance learning and to evaluate the system of distance learning. The sampling of the research has been conducted with 321 students chosen randomly and homogeneously among 4000 students actively studying in the faculties, graduated from schools and vocational schools of Hitit University.Öğe The Use of Machine Learning Approaches for the Diagnosis of Acute Appendicitis(Hindawi Ltd, 2020) Akmeşe, Ömer Faruk; Doğan, Gül; Kör, Hakan; Erbay, Hasan; Demir, EmreAcute appendicitis is one of the most common emergency diseases in general surgery clinics. It is more common, especially between the ages of 10 and 30 years. Additionally, approximately 7% of the entire population is diagnosed with acute appendicitis at some time in their lives and requires surgery. The study aims to develop an easy, fast, and accurate estimation method for early acute appendicitis diagnosis using machine learning algorithms. Retrospective clinical records were analyzed with predictive data mining models. The predictive success of the models obtained by various machine learning algorithms was compared. A total of 595 clinical records were used in the study, including 348 males (58.49%) and 247 females (41.51%). It was found that the gradient boosted trees algorithm achieves the best success with an accurate prediction success of 95.31%. In this study, an estimation method based on machine learning was developed to identify individuals with acute appendicitis. It is thought that this method will benefit patients with signs of appendicitis, especially in emergency departments in hospitals.Öğe Web tabanlı ders yerleştirme programı(Atatürk Üniversitesi Kazım Karabekir Eğitim Fakültesi, 2013-06-08) Akmeşe, Ömer Faruk; Küce, Sergen TolgaGünümüzde web tabanlı uygulamaların önemi sürekli olarak artmaktadır. İnternet üzerinden yapılan herhangi bir işlemde zaman ve mekân kavramı ortadan kalkmaktadır. İnsanlar buluştukları sanal ortamda zaman ve mekâna bağımlı kalmaksızın çalışabilmektedirler. Birçok alanda kullanılan web teknolojileri kendisini eğitim öğretim alanında da göstermiştir. Web tabanlı sistemlerin eğitim alanında kullanılması; zamandan, mekândan, iş gücünden ve maliyetten kazanç olarak geri dönmektedir. Bu çalışmada, basit bir arayüz aracılığı ile web tabanlı bir yazılım kullanılarak; öğretim elemanı görevlendirmelerinin yapılması, ders programlarının hazırlanması ve bu faaliyetlere ayrılan zamanın düşürülmesi amaçlanmıştır
