Profile URL: https://hdl.handle.net/20.500.13091/11977
Job Title:Dr. Öğr. Gör.
Email Address:nkoklu@ktun.edu.tr
Main Affiliation:07. 07. Department of Construction Technology
Status: Current Staff
ORCID:
0000-0001-9563-3473
0000-0001-9563-3473Scopus ID:
57221725261
57221725261YÖK Akademik: FD1A5556866A2C70
Google Scholar:
5vfM9VoAAAAJ
5vfM9VoAAAAJWeb of Science ID:
KXB-0589-2024
KXB-0589-2024Name Variants:
Köklü, N. Koklu, Nigmet
12 results
Scholarly Output Search Results
Now showing 1 - 10 of 12
Article Citation - WoS: 2Citation - Scopus: 3Assessment of University Students' Earthquake Coping Strategies Using Artificial Intelligence Methods(Nature Portfolio, 2025) Sulak, Suleyman Alpaslan; Koklu, NigmetEarthquakes are one of the most destructive natural disasters that pose a serious threat to human life and infrastructure worldwide. The aim of this study is to evaluate the coping strategies of adult individuals in Turkey regarding earthquake stress using artificial intelligence-based methods. The data was collected from 858 university students living in Turkey during January, February, and March 2024. A dataset was created using the 'Coping Scale for Earthquake Stress.' Prediction models were established using artificial intelligence algorithms such as Logistic Regression (LR), Bagging, and Random Forest (RF) based on information from 24 variables. The cross-validation method was applied during model training. The Logistic Regression algorithm achieved the highest accuracy rate of 98.60%, while the Bagging algorithm demonstrated the lowest performance with an accuracy rate of 79.95%. The Random Forest algorithm showed moderate performance with an accuracy rate of 85.89%. The findings provide important insights into the coping strategies of the community regarding earthquake stress. This study is expected to contribute significantly to areas such as disaster management, psychology, public health, and community resilience.Article Analysis of the Xylenol Isomers by Femtosecond Laser Time of Flight Mass Spectrometry(2018) Kepçeoğlu, Abdullah; Dereli, Ö.; Gündoğdu, Yasemin; Köklü, Niğmet; Kilic, H.S.Xylenol is a phenolic chemical substance having two methyl groups and one hydroxyl group attached to a benzene ring and has six isomers. 2,4-xylenol is the only isomer of the xylenol molecule that is in the liquid phase while the remaining isomers are all in crystal form at room temperature. In the scope of this study, we have experimentally investigated ionization and dissociation properties of xylenol isomers. All experiments were carried out by using a time of flight mass spectrometry (TOF-MS) system coupled with a femtosecond laser system. The laser pulse power-dependent multiphoton ionization of xylenol isomers was investigated by using IR (800 nm) femtosecond laser pulses having a pulse width of ∼90 fs in duration and laser intensities changing from 2.6 × 10 13 to 2.6 × 10 14 W/cm 2 . Theoretically, molecular orbitals (LUMO+1, LUMO, HOMO, HOMO–1), vertical and adiabatic ionization energies were calculated using density functional theory (DFT) with B3LYP functional and 6–311++G(d,p) basis set by following geometry optimization and performing conformational analysis.Article A Multidimensional Analysis of the 21st Century Competencies Scale Through AI-Driven Data Mining Techniques(Nature Portfolio, 2025) Koklu, NigmetIn recent years, evaluating competencies such as knowledge, practical skills, character traits, and meta-learning capabilities has gained increasing importance in educational research. As educational datasets grow larger and more complex, machine learning offers promising tools for analyzing student responses and identifying patterns that support assessment processes. This study aims to classify student responses collected through the 21st Century Competencies Scale using a variety of machine learning algorithms, including SVM, ANN, k-NN, RF, LR, DT, AdaBoost, Gradient Boosting, and XGBoost. The dataset contains responses from 616 participants and covers four key sub-dimensions. Model performance was measured using accuracy, precision, recall, and F1-score. Grid search optimization was also applied to improve performance. The highest classification accuracy was achieved by LR in the "Character" sub-dimension (78.73%), followed by SVM in the "Skills" (78.58%) and overall scale (74.51%). Gradient Boosting and k-NN models also showed competitive results across multiple dimensions. These findings emphasize the effectiveness of machine learning, particularly when combined with parameter optimization, in supporting data-driven educational assessments.Article Citation - WoS: 3Citation - Scopus: 7Analysis of Depression, Anxiety, Stress Scale (dass-42) With Methods of Data Mining(Wiley, 2024-09-19) Sulak, Süleyman Alpaslan; Köklu, NigmetThis 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.Article Predicting Student Dropout Using Machine Learning Algorithms(2025-01-01) Sulak, Süleyman Alpaslan; Köklü, NiğmetThis 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.Other Citation - WoS: 5Citation - Scopus: 7The Systematic Analysis of Adults' Environmental Sensory Tendencies Dataset(Elsevier Inc., 2024-08-01) Koklu, N.; Sulak, S.A.This study was conducted to investigate the profound impact of human activities on the environment, based on scientific data, recognizing the potential of environmental problems to turn into devastating crises if appropriate measures are not taken. It emphasizes the important role of education in developing environmental awareness, knowledge and sensitivity to counter adverse environmental consequences. For this purpose, a dataset was created for the emotional tendencies of university students, who represent a demographic that has the potential to influence the sustainable future of the world. A survey data including 34 different variables was collected from 388 university students in Turkey. Environmental Sensory Tendencies Dataset is intended to provide valuable guidance for the development of effective environmental education programs and policies aimed at increasing university students' awareness and participation in environmental issues. Our research underlines the vital importance of developing responsible attitudes and behaviors to effectively address environmental challenges and thereby contribute to a healthier and more sustainable global ecosystem. This study will make a significant contribution to the literature and highlight the interconnection between human actions and environmental well-being. © 2024 The AuthorsArticle Using Artificial Intelligence Techniques for the Analysis of Obesity Status According To the Individuals' Social and Physical Activities(2024) Köklü, Nigmet; Sulak, Süleyman AlpaslanObesity 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%.Article Citation - Scopus: 4Analysis of the Studies Done on Laboratories in Turkey(Ekip Buro Makineleri A., 2020-06-30) Yener, Dündar; Köklü, Niğmet; Yamaç, Ramazan Ziya; Yalçın, SeherThe aim of this study is to determine the trend of studies in the laboratory and put the current situation in Turkey. For this purpose, document analysis technique, one of the qualitative research methods, was used in the research. The data group of the research consists of thesis studies on laboratories in our country between 1999-2017. Theses in the fields of science, physics, chemistry, and biology have been determined and themes and sub-themes have been created through the keywords of these theses. Then, frequency tables were created according to the themes and sub-themes created. According to the findings obtained, it was seen that the traditional laboratory approach and inquiry-based laboratory approaches are compared in the studies. It was determined that the studies were done on physics subjects and it was determined that complementary measurement and evaluation studies performed for performance evaluation were used in very few numbers. In addition, it was concluded that the keywords did not give enough information about the studies. In this context, it can be suggested to examine the effectiveness of these approaches according to each other and experiment types by examining the approaches in which students can be more active in laboratories. © 2020. All Rights Reserved.Article Classification of Environmental Attitudes With Artificial Intelligence Algorithms(2024-06-30) Köklü, Niğmet; Sulak Süleyman AlpaslanThe study aims to examine people's attitudes towards the environment. Environmental education provides the necessary awareness to effectively address environmental issues. It is stated that attitudes towards the environment are very important and negative attitudes can worsen environmental problems. For this purpose, a dataset was obtained by using a scale consisting of 37 variables to a participant group consisting of 384 people. With this dataset, attitudes towards the environment have been analyzed usingvarious classification algorithms. Logistic Regression (LR), Support Vector Machine (SVM) and Decision Tree (DT) models were used in the research. The LR, SVM, and DT models achieved 94.53%, 92.96%, and 82.55% classification success, respectively It is seen that the classification achievements of the models are at an acceptable level compared to the literature. As a result, the research sheds light on people's attitudes towards the environment through classification processes. Despite the acceptable classification achievements, alternative artificial Intelligence approaches can also be used to improve performance.Article Analysis of the Studies Done on Laboratories in Turkey(2020) Yener Dündar; Köklü, Niğmet; Yamaç Ramazan Ziya; Yalçın SeherThe aim of this study is to determine the trend of studies in the laboratory and put the current situation in Turkey. For this purpose, document analysis technique, one of the qualitative research methods, was used in the research. The data group of the research consists of thesis studies on laboratories in our country between 1999-2017. Theses in the fields of science, physics, chemistry, and biology have been determined and themes and sub-themes have been created through the keywords of these theses. Then, frequency tables were created according to the themes and sub-themes created. According to the findings obtained, it was seen that the traditional laboratory approach and inquiry-based laboratory approaches are compared in the studies. It was determined that the studies were done on physics subjects and it was determined that complementary measurement and evaluation studies performed for performance evaluation were used in very fewnumbers. In addition, it was concluded that the keywords did not give enough information about the studies. In this context, it can be suggested to examine the effectiveness of these approaches according to each other and experiment types by examining theapproaches in which students can be more active in laboratories.
Research Topics
Domains
Social SciencesPhysical Sciences
Fields
Social SciencesArts and HumanitiesComputer ScienceEngineeringPhysics and Astronomy
Subfields
EducationPhilosophyComputer Science ApplicationsMedia TechnologyStatistical and Nonlinear Physics
Specific Research Areas
Innovative Teaching Methods
Education Practices and Challenges
Online Learning and Analytics
Experimental Learning in Engineering
Experimental and Theoretical Physics Studies
Sustainable Development Goals
4QUALITY EDUCATION
4
Research Products
9INDUSTRY, INNOVATION AND INFRASTRUCTURE
3
Research Products
11SUSTAINABLE CITIES AND COMMUNITIES
2
Research Products
12RESPONSIBLE CONSUMPTION AND PRODUCTION
2
Research Products
3GOOD HEALTH AND WELL-BEING
1
Research Products
14LIFE BELOW WATER
1
Research Products
17PARTNERSHIPS FOR THE GOALS
1
Research Products
Documents
5
Citations
21
h-index
3
Documents
0
Citations
0
Publication Collaboration
| Affiliation Name | Count |
|---|---|
| Konya Technical University | 10 |
| Necmettin Erbakan University | 7 |
| Selçuk University | 5 |
| Bolu Abant İzzet Baysal University | 1 |
1 / 1
Data obtained from OpenAlex
| Journal | Count |
|---|---|
| Scientific Reports | 3 |
| Journal of Turkish Science Education | 2 |
| European Journal of Education | 1 |
| Intelligent Methods in Engineering Sciences | 1 |
| Intelligent Methods In Engineering Sciences | 1 |
Current Page: 1 / 2
Scholarly Output
12
Articles
11
Views / Downloads
19/70
Supervised MSc Theses
0
Supervised PhD Theses
0
WoS Citation Count
10
Scopus Citation Count
21
Patents
0
Projects
0
WoS Citations per Publication
0.83
Scopus Citations per Publication
1.75
Open Access Source
11
Supervised Theses
0
Scopus Quartile Distribution
Competency Cloud
