Bilgisayar Mühendisliği Bölümü Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1940
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Conference Object Citation - WoS: 1Neural Language Generation for a Turkish Task-Oriented Dialogue System(PROCEEDINGS OF THE WORKSHOP ON INTELLIGENT INFORMATION PROCESSING AND NATURAL LANGUAGE GENERATION, 2020) Çakar, Tuna; Demir, Şeniz; Bilgin, Batuhan; Çakar, Tuna; Demir, Şeniz; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityRapidly growing language and speech-enabled technologies contribute to the development of task-oriented dialogue systems. The demand for better user engagement has been increasing at an accelerating pace and this brings new remarkable challenges including the generation of informative and natural system utterances. In this work, our ultimate goal is to develop a Turkish task-oriented dialogue system that enables users to navigate over a map in order to get informed about dining venues that best match their preferences and make reservations based on received recommendations. This paper presents the pipeline architecture of our dialogue system with a particular focus on the language generator. We utilize an open source framework for building the components of our system and develop a sequence-to-sequence (Seq2Seq) neural model for language generation. This pioneering work is the first that proposes the use of a neural generation model in a Turkish conversational system. Our evaluations suggest that Turkish neural generation from meaning representations given in the form of dialogue acts is effective, but still in need of further improvements.Conference Object Rag Based Interactive Chatbot for Video Streaming Services(Institute of Electrical and Electronics Engineers Inc., 2025) Çakar, Tuna; Özlem, Şirin; Kara E.; Yildiz A.; Köseoǧlu O.; Makaroǧlu D.; Çakar T.; Gözükara, Hamza; Patel, Jay; Makaroǧlu, Didem; Kara, Erkan; Yildiz, Ayşenur; Çakar, Tuna; Köseoǧlu, Ozan; 02.01. Department of Industrial Engineering; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityThe proliferation of content within video streaming services presents a significant challenge for users seeking personalized recommendations and specific information. This research addresses this challenge by developing a Retrieval-Augmented Generation (RAG) chatbotn designed to enhance user experience through conversational AI. The primary contribution of this work is a novel Retrieval-Augmented Generation (RAG) architecture featuring a dual-retrieval system that combines semantic search for descriptive requests and structured queries for fact based inquiries. This approach grounds the Large Language Model (LLM) in a factual knowledge base, mitigating the risk of hallucinations. The system is engineered to handle empty data retrieval scenarios by dynamically relaxing search filters, ensuring a robust user experience. The effectiveness of this RAG approach was validated through a comprehensive set of automated evaluations. The system demonstrates high precision in ranked list retrieval with questions like "Recommend me the top 5 action movies with highest IMDb scores", achieving an average NDCG@k of 0.837. While the chatbot shows strong semantic understanding by achieving 91% accuracy with contextual clues such as "Which Batman movies are directed by Christopher Nolan?", its performance with more ambiguous, plot-only queries (59.5% accuracy) indicates clear opportunities for future refinement. These results confirm that the dual-tool architecture successfully combines the flexibility of semantic search with the precision of structured queries, paving the way for more intuitive and efficient content discovery on streaming platforms. © 2025 IEEE.Conference Object Combining Similar Trajectories and XGBoost via Residual Learning for Traffic Flow Forecasting(Institute of Electrical and Electronics Engineers Inc., 2025) Çakar, Tuna; Yilmaz E.; Arslan I.; Çakar T.; Çakar, Tuna; Işlak, Ümit; Yilmaz, Elif; Arslan, Ilker; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityIn this study, we propose novel hybrid forecasting models that integrate the method of similar trajectories with machine learning techniques, particularly the XGBoost algorithm, for traffic flow prediction. Traditional statistical models, such as ARIMA, often struggle to accurately capture the complex, non-linear patterns characteristic of traffic flow data. To address these limitations, we develop an additive hybrid forecasting framework that combines the strengths of linear models (similar trajectories method) and non-linear models (XGBoost). Our proposed methods are evaluated on two different stations from the California PEMS dataset. Experimental results demonstrate that the proposed hybrid models consistently outperform individual benchmark models, including ARIMA, standalone similar trajectories, and XGBoost. The superiority of the hybrid approach, particularly the XGBST model, is further validated through the Diebold-Mariano statistical test, confirming significant predictive improvements at various significance levels. Additionally, using weighted Euclidean distance within the similar trajectories method further enhanced forecasting accuracy. The interpretability and flexibility of our hybrid framework make it especially suitable for practical implementation in traffic management systems. These findings underline the effectiveness of hybrid modeling strategies in traffic flow forecasting and suggest future research directions, such as comprehensive hyperparameter optimization and broader validation across diverse datasets. © 2025 IEEE.Conference Object Citation - WoS: 1Citation - Scopus: 2Determination of Alzheimer's Disease Levels by Ordinal Logistic Regression and Artificial Learning Algorithms(Ieee, 2024) Çakar, Tuna; Çakar, Tuna; Arslan, Ilker; Akinci, Zeynep Karaoglu; Oner, Kevser Setenay; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityThis study compares artificial learning algorithms and logistic regression models in determining different levels of Alzheimer's disease (AD). The research uses demographic, genetic, and neurocognitive inventory results obtained from the National Alzheimer's Coordination Center (NACC) database, along with brain volume/thickness measurements derived from MRI scanners. Deep Neural Networks, Ordinal Logistic Regression, Random Forest, Gaussian Naive Bayes, XGBoost, and LightGBM models were employed to determine the 4 different ordinal levels of AD. Although there were similarities between the accuracy rate, F1 score, AUC value, and sensitivity, specificity, and precision performance measures of each class, the highest classification rate was achieved by the Random Forest model where the oversampling was not applied. (F1 score: 0.86; accuracy: 0.86 and AUC: 0.95). The outputs of the model with the best performance were explained with the SHAP (SHapley Additive exPlanations) method. These findings indicate that non-invasive markers and artificial learning models can be used effectively in early diagnosis and decision support systems to predict different levels of Alzheimer's disease.Conference Object Feature Enrichment Via Similar Trajectories for Xgboost Based Time Series Forecasting(Ieee, 2024) Yilmaz, Elif; Çakar, Tuna; Çakar, Tuna; Arslan, Ilker; Matematik, ; Bilimi, Bilgisayar; Mühendisliği, Makine; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityIn this study, new time series forecasting models are developed based on XGBoost, and the similar trajectories method (ST), which can be interpreted as a regression based on nearest neighbors. Both the similar trajectories method and XGBoost model are known to have successful applications in traffic flow prediction. In our case, the focus is on similar trajectories used in the former method, and features based on these trajectories are used in the training of XGBoost. The success of the proposed models is confirmed through metrics such as the mean absolute error. Also, statistical tests are performed among the compared benchmark models. The study is concluded with discussions and questions about how these models can be further developed.Conference Object Reliability Study of Psychometric Tests in a Credit Scoring Model(Ieee, 2024) Nicat, Sahin; Filiz, Gozde; Çakar, Tuna; Çakar, Tuna; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityThis study investigates the effectiveness and reliability of using psychometric tests in the credit decision-making processes within the finance sector. Psychometric tests, by measuring individuals' cognitive and psychological traits, hold the potential to broaden access to credit and identify high credit risk. However, after the literature review, it was seen that there was a need for more studies on the reliability and validity of these tests in finance. This study is designed to measure the test-retest reliability of a machine learning model and its inputs that utilize psychometric test results. Within the scope of the research, 115 participants were re-subjected to the same psychometric tests after an average of 6 months. Findings showed that psychometric tests and the machine learning model were generally consistent over time. This work has the potential to fill the gaps in the literature regarding the use of psychometric tests in the finance sector and lays a foundation for future research.Conference Object Citation - Scopus: 1Distinguishing Cognitive Processes: a Machine Learning Approach To Decode Fnirs Data for Third-Party Punishment and Credit Decision-Making(Ieee, 2024) Filiz, Gozde; Çakar, Tuna; Son Turan, Semen; Ertugrul, Seyit; Sahin, Turkay; Akyurek, Guclu; Çakar, Tuna; 02.02. Department of Computer Engineering; 04.03. Department of Business Administration; 02. Faculty of Engineering; 04. Faculty of Economics, Administrative and Social Sciences; 01. MEF UniversityFunctional near-infrared spectroscopy (fNIRS) has seen increasingly widespread use in examining brain activity and cognitive processes. However, the existing literature provides insufficient information on distinguishing between different decision-making mechanisms. This study explores the application of fNIRS in differentiating between two distinct decision-making processes: third-party punishment decisions and credit decisions. The research includes analyzing fNIRS data collected during these processes and classifying the associated neural patterns using machine learning. The findings reveal that fNIRS, in conjunction with ML, holds substantial potential to enhance the depth of understanding of decision-making processes in neuroscience research.Article Citation - WoS: 2Citation - Scopus: 4Unlocking the Neural Mechanisms of Consumer Loan Evaluations: an Fnirs and Mlbased Consumer Neuroscience Study(Frontiers Media SA, 2024) Girişken, Yener; Son Turan, Semen; Çakar, Tuna; Filiz, Gözde; Çakar, Tuna; Ertuğrul, Seyit; Sayar, Alperen; Tuna, Esin; Son-Turan, Semen; 02.02. Department of Computer Engineering; 04.03. Department of Business Administration; 02. Faculty of Engineering; 04. Faculty of Economics, Administrative and Social Sciences; 01. MEF UniversityThis study conducted a comprehensive exploration of the neurocognitive processes underlying consumer credit decision-making using cutting-edge techniques from neuroscience and artificial intelligence (AI). Employing functional Near-Infrared Spectroscopy (fNIRS), the research examines the hemodynamic responses of participants while evaluating diverse credit offers. The study integrates fNIRS data with advanced AI algorithms, specifically Extreme Gradient Boosting, CatBoost, and Light Gradient Boosted Machine, to predict participants' credit decisions based on prefrontal cortex (PFC) activation patterns. Findings reveal distinctive PFC regions correlating with credit behaviors, including the dorsolateral prefrontal cortex (dlPFC) associated with strategic decision-making, the orbitofrontal cortex (OFC) linked to emotional valuations, and the ventromedial prefrontal cortex (vmPFC) reflecting brand integration and reward processing. Notably, the right dorsomedial prefrontal cortex (dmPFC) and the right vmPFC contribute to positive credit preferences. This interdisciplinary approach bridges neuroscience and finance, offering unprecedented insights into the neural mechanisms guiding financial choices. The study's predictive model holds promise for refining financial services and illuminating human financial behavior within the burgeoning field of neurofinance. The work exemplifies the potential of interdisciplinary research to enhance our understanding of human financial decision-making.Conference Object Differential Effect of Young Adults and Students Metacognitive Skills in Mathematics Problem Solving Process(eScholarship, 2023) Birgili, Bengi; Can, Rümeysa; Birgili, Bengi; Çakar, Tuna; 02.02. Department of Computer Engineering; 06.02. Department of Mathematics and Science Education; 02. Faculty of Engineering; 01. MEF University; 06. Faculty of EducationThe purpose of this study is to examine how young adults and pupils use their metacognitive abilities such as cognitive strategies and self-checking during the mathematics problem-solving process. The study group consisted of 12 young adults selected from three different faculties in a foundation university and 32 pupils from public and privateK-12 schools, Istanbul, Turkey. Multimodal mixed-methods design was employed, where participants were asked to think out loud while solving ten mathematical problems. The experimental process was recorded with the use of eye-tracking, which was utilized to evaluate the active use of metacognitive sub-skills. The findings from the experimental process revealed that there is a significant difference between the amount of reflection of young adults’ and pupils' cognitive strategy and self-checking skill levels on their responses to mathematics problem solving process in favor of pupils.Book Part Citation - Scopus: 1Consumer Neuroscience Perspective for Brands: How Do Brands Influence Our Brains?(IGI Global, 2020) Çakar, Tuna; Çakar, Tuna; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityNeuroscientific tools have increasingly been used by marketing practitioners and researchers to understand and explain several different questions that have been issued for a specific company or a general understanding. In this respect, the neuroscientific approach has been evaluated as a potential tool for understanding the neural mechanisms directly related to marketing with its contribution to providing novel perspectives. The chapter addresses one of the most relevant subjects, brands, for issuing the strategic role of applied neuroscience in marketing and consumer behavior. The first section of this chapter focuses on a novel definition of brand, and the next section covers the brand image, brand perception, and brand loyalty. The second section summarizes the main findings regarding the neuroscience of brands. In the final section, the findings from a related experiment have been provided for the potential roles of neuromarketing for developing marketing strategies for brands.