Browsing by Author "Sulak, Süleyman Alpaslan"

Now showing 1 - 3 of 3

Citation - WoS: 3
Citation - Scopus: 7
Analysis of Depression, Anxiety, Stress Scale (dass-42) With Methods of Data Mining
(Wiley, 2024) Sulak, Süleyman Alpaslan; Köklu, Nigmet
This study employs advanced data mining techniques to investigate the DASS-42 questionnaire, a widely used psychological assessment tool. Administered to 680 students at Necmettin Erbakan University's Ahmet Kelesoglu Faculty of Education, the DASS-42 comprises three distinct subscales-depression, anxiety and stress-each consisting of 14 items. Departing from traditional statistical methodologies, the study harnesses the power of the WEKA data mining program to analyse the dataset. Employing Naive Bayes (NB), Artificial Neural Network (ANN), Logistic Regression (LR), Support Vector Machine (SVM) and Random Forest (RF) algorithms, the research unveils novel insights. The ANN method emerges as a standout performer, achieving remarkable distinctiveness scores for all subscales: depression (99.26%), anxiety (98.67%) and stress (97.35%). The study highlights the potential of data mining in enhancing psychological assessment and showcases the ANN's prowess in capturing intricate patterns within complex psychological dimensions. By charting a course beyond conventional statistical methods, this research pioneers a new frontier for employing data mining within the realm of social sciences. As a result of the study, it is recommended that teacher candidates in the teacher education process should have knowledge about depression, anxiety and stress, and relevant courses on these topics should be added to the curriculum of teacher education programs.
Predicting Student Dropout Using Machine Learning Algorithms
(2024) Sulak, Süleyman Alpaslan; Köklü, Niğmet
This article comprehensively examines the use of machine learning algorithms to predict and reduce student dropout rates. These methods, developed to monitor and support student achievement in education, also aimedto enhance success rates in education and ensure more effective student engagement in the learning process. Bigdata analysis and machine learning models provide important contributions to the development of strategic solutions to the problem of school dropout by predicting student movements and trends.This study uses a dataset consisting of 4424 student data and has 37 features. The dataset is divided into three classes: "Dropout", "Enrolled" and "Graduate" according to the students' school dropout status. Decision Tree (DT), Random Forest (RF) and Artificial Neural Network (ANN) competitions, which are frequently used in such training studies in the literature, are aimed at this dataset. According to the obtained operations, DT showed moderate performance with an accuracy rate of 70.1%. The RF algorithm showed higher success with an accuracy rate of 75.5%. The highest success was achieved by the ANNalgorithm with an accuracy rate of 77.3%. ANN's flexible structure has produced superior results compared to other algorithms for this dataset, its ability provide successful classification in complex datasets.The article ultimately demonstrates how machine learning-based prediction models can have a significant impact on student achievement and offer a powerful tool for reducing school dropouts.
Using Artificial Intelligence Techniques for the Analysis of Obesity Status According To the Individuals' Social and Physical Activities
(2024) Köklü, Nigmet; Sulak, Süleyman Alpaslan
Obesity is a serious and chronic disease with genetic and environmental interactions. It is defined as an excessive amount of fat tissue in the body that is harmful to health. The main risk factors for obesity include social, psychological, and eating habits. Obesity is a significant health problem for all age groups in the world. Currently, more than 2 billion people worldwide are obese or overweight. Research has shown that obesity can be prevented. In this study, artificial intelligence methods were used to identify individuals at risk of obesity. An online survey was conducted on 1610 individuals to create the obesity dataset. To analyze the survey data, four commonly used artificial intelligence methods in literature, namely Artificial Neural Network, K Nearest Neighbors, Random Forest and Support Vector Machine, were employed after pre-processing. As a result of this analysis, obesity classes were predicted correctly with success rates of 74.96%, 74.03%, 74.03% and 87.82%, respectively. Random Forest was the most successful artificial intelligence method for this dataset and accurately classified obesity with a success rate of 87.82%.