Çakar, Tuna
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Çakar T.
Tuna Çakar
Çakar,Tuna
Çakar Tuna
Çakar, T.
Tuna Çakar
Çakar,Tuna
Çakar Tuna
Çakar, T.
Job Title
Email Address
cakart@mef.edu.tr
Main Affiliation
02.02. Department of Computer Engineering
Status
Current Staff
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Sustainable Development Goals
2
ZERO HUNGER
2
Research Products
16
PEACE, JUSTICE AND STRONG INSTITUTIONS
2
Research Products
1
NO POVERTY
1
Research Products
11
SUSTAINABLE CITIES AND COMMUNITIES
1
Research Products
7
AFFORDABLE AND CLEAN ENERGY
0
Research Products
10
REDUCED INEQUALITIES
0
Research Products
3
GOOD HEALTH AND WELL-BEING
5
Research Products
6
CLEAN WATER AND SANITATION
0
Research Products
9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
1
Research Products
12
RESPONSIBLE CONSUMPTION AND PRODUCTION
1
Research Products
5
GENDER EQUALITY
0
Research Products
14
LIFE BELOW WATER
0
Research Products
13
CLIMATE ACTION
0
Research Products
15
LIFE ON LAND
2
Research Products
8
DECENT WORK AND ECONOMIC GROWTH
4
Research Products
17
PARTNERSHIPS FOR THE GOALS
1
Research Products
4
QUALITY EDUCATION
1
Research Products
This researcher does not have a Scopus ID.
This researcher does not have a WoS ID.
Scholarly Output
106
Articles
17
Views / Downloads
20623/245820
Supervised MSc Theses
33
Supervised PhD Theses
0
WoS Citation Count
78
Scopus Citation Count
150
WoS h-index
3
Scopus h-index
5
Patents
0
Projects
6
WoS Citations per Publication
0.74
Scopus Citations per Publication
1.42
Open Access Source
48
Supervised Theses
33
| Journal | Count |
|---|---|
| 32nd IEEE Signal Processing and Communications Applications Conference (SIU) -- MAY 15-18, 2024 -- Tarsus Univ Campus, Mersin, TURKEY | 9 |
| 2023 31st Signal Processing and Communications Applications Conference (SIU) | 6 |
| 2022 7th International Conference on Computer Science and Engineering (UBMK) | 6 |
| 2023 4th International Informatics and Software Engineering Conference (IISEC) | 5 |
| 30th IEEE Signal Processing and Communications Applications Conference (SIU) -- MAY 15-18, 2022 -- Safranbolu, TURKEY | 4 |
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Scopus Quartile Distribution
Competency Cloud
106 results
Scholarly Output Search Results
Now showing 1 - 10 of 106
Article Determination of Alzheimer's Disease Stages by Artificial Learning Algorithms(Lifescience Global, 2025) Bulut, Nurgül; Çakar, Tuna; Arslan, İlker; Akıncı, Zeynep Karaoğlu; Oner, Kevser SetenayIntroduction: This study aims to determine the stages of Alzheimer's disease (AD) using different machine learning algorithms, and compares the performance of these models. Methods: Demographic, genetic, and neurocognitive inventory data from the National Alzheimer's Coordinating Center (NACC) database as well as brain volume/thickness data from magnetic resonance imaging (MRI) scans were used. Deep Neural Networks, Ordinal Logistic Regression, Random Forest, Gaussian Naive Bayes, XGBoost, and LightGBM models were used to identify four different ordinal stages of AD. Results: Although the performance measures of the developed models were similar, the highest classification rate of AD stages was achieved by the Random Forest model (accuracy: 0.86; F1 score: 0.86; AUC: 0.95). The outputs of the model with the best performance were explained by the SHapley Addictive exPlanations (SHAP) method. Conclusions: This indicates that non-invasive markers and machine learning models can be used effectively in early diagnosis and decision support systems to predict stages of AD. © 2025 Elsevier B.V., All rights reserved.Conference Object Optimizing Collective Building Management Through a Machine Learning-Based Decision Support System(IEEE, 2023) Güvençli, Mert; Dağ, Hasan; Doğan, Erkan; Çakar, Tuna; Özyürüyen, Burcu; Kiran, HalilThis study presents the design, implementation, and evaluation of a Decision Support System (DSS) developed for Collective Building Management. Given the potential advantages of machine learning techniques in this domain, the research explores how these techniques can be used to improve collective building management. The dataset consists of 824,932 records and 15 attributes, after preprocessing the data to fill in missing values with the median. The random forest algorithm was chosen for model training and achieved a performance rate of 71.2%. This model can be used to optimize decision processes in collective building management. The proposed prototype is notable for its ability to automatically generate operational plans. In conclusion, machine learning-based DSSs are effective tools for collective building management.Conference Object Citation - WoS: 2Citation - Scopus: 2Classification of Altruistic Punishment Decisions by Optical Neuroimaging and Machine Learning Methods(IEEE, 2023) Erözden, Ozan; Şahin, Türkay; Akyürek, Güçlü; Filiz, Gözde; Çakar, TunaAltruistic punishment (third-party punishment) is important in terms of maintaining social norms and promoting prosocial behavior. This study examined data obtained using the near infrared spectroscopy (fNIRS) method to predict altruistic punishment decisions. It was found that specific neural activity patterns were significantly related to decisions regarding the punishment of the perpetrator. This research contributes to the development of social decision-making models and helps advance our understanding of the cognitive and neural processes involved in third-party punishments.Master Thesis Convolutional Neural Network for Facial Emotion Recognition With Geometrical Features of Face(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Arslan, İlker; Tuna ÇakarOne of the recent challenging machine learning problems is to make predictions on image datasets. The aim of the project is to construct a convolutional neural network to guess emotions for a face of a human given in an image file considering the face. After the geometrical features are extracted using pretrained models, we construct five models which are convolutional networks fed with handcrafted geometrical features extracted. The last model uses the outputs of other four models to predict more accurately.Conference Object Makine Öğrenimi ve Çok Boyutlu Anket Verileri Kullanılarak Öğrenci Başarısının Tahmini: Eğitim Programı Üzerine Bir Uygulama(Institute of Electrical and Electronics Engineers Inc., 2025) Behsi, Zeynep; Dereli, Serhan; Cakar, Tuna; Patel, Jay; Cicek, Gultekin; Drias, YassineThis study develops a machine learning model integrating survey data and performance metrics to predict student success in the UpSchool education program. Students' personality traits assessed by DISC analysis, financial management, social, and emotional skills were clustered into "Successful,""Unsuccessful,"and "Moderately Successful"groups using K-means clustering. The SMOTE technique addressed data imbalance issues, and algorithms such as Logistic Regression, Random Forest, LightGBM, and AdaBoost were tested. After hyperparameter optimization, AdaBoost and LightGBM achieved the highest predictive performance. Results demonstrated the effectiveness of machine learning models in forecasting student success in educational programs. Future studies are recommended to enhance model performance through expanded datasets and to validate the model's applicability across diverse educational contexts. © 2025 Elsevier B.V., All rights reserved.Conference Object Citation - Scopus: 1Liking Prediction Using fNIRS and Machine Learning: Comparison of Feature Extraction Methods(IEEE, 2022) Koksal, Mehmet Yigit; Çakar, Tuna; Demircioğlu, Esin Tuna; Girisken, YenerThe fMRI method, which is generally used to detect behavioral patterns, draws attention with its expensive and impractical features. On the other hand, near infrared spectroscopy (fNIRS) method is less expensive and portable, but it is as effective as fMRI in creating a good prediction model. With this method, a model has been developed that can predict whether people like a stimulus or not, using machine learning various algorithms. A comparison was made between feature extraction methods, which was the main focus while developing the model.Conference Object Citation - Scopus: 1Toward a Novel Neuroscience-Based System Approach Integrating Cognitive and Implicit Learning in Education(Springer Science and Business Media Deutschland GmbH, 2023) Tsvetkova, Nadezhda; Çakar, Tuna; Veledinskaya, Svetlana; Babanskaya, Olesya; Dorantes-Gonzalez, Dante JorgeEmotional-enhanced learning is a meaningful driver of engagement leading to long-term memory retention in learners, however, traditional approaches such as problem-based learning, and project-based learning, among others, do not consider brain-based learning guidelines concerning learner’s emotional experience design. The Neuroscience-based Learning (NBL) technique is a novel neuro-educational approach that applies the implicit neuro-physiological mechanisms underlying vivid and highly-arousal emotion-al experiences leading to long-term memory retention. The NBL is devised from a cybernetic system point of view, by explaining the novel neuro-physiological learning scheme describing the relation among the environment and the learner’s internal mental processes ranging from perceptions, comparison with previous experiences and memories, immediate sensations and reactions, emotions, desires, intentions, higher order cognitive functions, and controlled actions towards the environment. While explaining biological processes, the scheme also relates the types of memory systems with their non-associative and associative learning mechanisms, and the variables that modulate learning. NBL proposes the triggers for a vivid and highly-arousal emotional learning, which are novelty, unpredictability, sense of low control, threat to ego, avoidance (aversion-mediated learning), and reward (reward-based learning). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.Article A New Benchmark Dataset for P300 Erp-Based Bci Applications(Academic Press Inc Elsevier Science, 2023) Çakar, Tuna; Özkan, Hüseyin; Musellim, Serkan; Arslan, Suayb S.; Yağan, Mehmet; Alp, NihanBecause of its non-invasive nature, one of the most commonly used event-related potentials in brain -computer interface (BCI) system designs is the P300 electroencephalogram (EEG) signal. The fact that the P300 response can easily be stimulated and measured is particularly important for participants with severe motor disabilities. In order to train and test P300-based BCI speller systems in more realistic high-speed settings, there is a pressing need for a large and challenging benchmark dataset. Various datasets already exist in the literature but most of them are not publicly available, and they either have a limited number of participants or utilize relatively long stimulus duration (SD) and inter-stimulus intervals (ISI). They are also typically based on a 36 target (6 x 6) character matrix. The use of long ISI, in particular, not only reduces the speed and the information transfer rates (ITRs) but also oversimplifies the P300 detection. This leaves a limited challenge to state-of-the-art machine learning and signal processing algorithms. In fact, near-perfect P300 classification accuracies are reported with the existing datasets. Therefore, one certainly needs a large-scale dataset with challenging settings to fully exploit the recent advancements in algorithm design (machine learning and signal processing) and achieve high-performance speller results. To this end, in this article we introduce a new freely-and publicly-accessible P300 dataset obtained using 32-channel EEG, in the hope that it will lead to new research findings and eventually more efficient BCI designs. The introduced dataset comprises 18 participants performing a 40 -target (5 x 8) cued-spelling task, with reduced SD (66.6 ms) and ISI (33.3 ms) for fast spelling. We have also processed, analyzed, and character-classified the introduced dataset and we presented the accuracy and ITR results as a benchmark. The introduced dataset and the codes of our experiments are publicly accessible at https://data .mendeley.com /datasets /vyczny2r4w.(c) 2023 Elsevier Inc. All rights reserved.Master Thesis Calculation of the Capacity of a Retail Clothing Store(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Türkoğlu, Murat; Tuna ÇakarThe purpose of this study is to define and improve the capacity calculation for clothing store companies. It is important to know how many products need to be sent to the relevant store in order to sell more products, whether more or less products will be sent to the stores, how much the product can sell and how much capacity the stores will have for the relevant products during the season. Planning and producing more products than necessary may also cause insufficient capacity to consume the stocks of that product in the relevant season. For these reasons, a detailed capacity management system is needed. The capacity of certain product groups in the stores in certain seasons can be determined by calculating the capacities of the relevant units in the stores and the relations of these units with the product groups. The relevant system will produce output for both planning and allocation units. At the same time, converting the capacity of products and store display units into a common unit (LCM) will be one of the factors that facilitate our work in capacity calculation. A short version of the LC Waikiki capacity system platform is used to obtain the data. ASP.Net Core Web API, ADO.NET, T-SQL, C # programming were used as program tools. Azure Microsoft SQL Server was used as a database server. Azure App Services has been used to keep the business codes.Article Citation - WoS: 3Citation - Scopus: 5Unraveling Neural Pathways of Political Engagement: Bridging Neuromarketing and Political Science for Understanding Voter Behavior and Political Leader Perception(2023) Çakar, Tuna; Filiz, GözdePolitical neuromarketing is an interdisciplinary field that combines marketing, neuroscience, and psychology to understand voter behavior and political leader perception. This interdisciplinary field offers novel techniques to understand complex phenomena such as voter engagement, political leadership, and party branding. This study aims to understand the neural activation patterns of voters when they are exposed to political leaders using functional near-infrared spectroscopy (fNIRS) and machine learning methods. We recruited participants and recorded their brain activity using fNIRS when they were exposed to images of different political leaders. This neuroimaging method (fNIRS) reveals brain regions central to brand perception, including the dorsolateral prefrontal cortex (dlPFC), the dorsomedial prefrontal cortex (dmPFC), and the ventromedial prefrontal cortex (vmPFC). Machine learning methods were used to predict the participants' perceptions of leaders based on their brain activity. The study has identified the brain regions that are involved in processing political stimuli and making judgments about political leaders. Within this study, the best-performing machine learning model, LightGBM, achieved a highest accuracy score of 0.78, underscoring its efficacy in predicting voters' perceptions of political leaders based on the brain activity of the former. The findings from this study provide new insights into the neural basis of political decision-making and the development of effective political marketing campaigns while bridging neuromarketing, political science and machine learning, in turn enabling predictive insights into voter preferences and behavior
