Bilgisayar Mühendisliği Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1940

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Citation - Scopus: 2
Alternative Data Sources and Psychometric Scales Supported Credit Scoring Models
(IEEE, 2023) Şahin, Türkay; Filiz, Gözde; Çakar, Tuna; Özvural, Özden Gebizlioğlu; Nicat, Şahin
This study aims to evaluate individuals with limited access to banking services and enhance credit scoring models with alternative data sources. A psychometric-based credit scoring model was developed and tested. Despite limited data, significant potential findings were obtained. However, clarification of the distinction between credit payment intention and ability and validation of the results with more data are necessary.
An Exploratory Study on the Effect of Contour Types on Decision Making Via Optic Brain Imaging Method (fnirs)
(eScholarship, 2023) Demircioglu, Esin Tuna; Girişken, Yener; Çakar, Tuna
Decision-making is a combination of our positive anticipations from the future with the contribution of our past experiences, emotions, and what we perceive at the moment. Therefore, the cues perceived from the environment play an important role in shaping the decisions. Contours, which are the hidden identity of the objects, are among these cues. Aesthetic evaluation, on the other hand, has been shown to have a profound impact on decision-making, both as a subjective experience of beauty and as having an evolutionary background. The aim of this empirical study is to explain the effect of contour types on preference decisions in the prefrontal cortex through risk-taking and aesthetic appraisal. The obtained findings indicated a relation between preference decision, contour type, and PFC subregion. The results of the current study suggest that contour type is an effective cue in decision-making, furthermore, left OFC and right dlPFC respond differently to contour types.
Citation - WoS: 52
Citation - Scopus: 66
An Investigation of the Neural Correlates of Purchase Behavior Through Fnirs
(2018) Cakir, Murat Perit; Yurdakul, Dicle; Girisken, Yener; Çakar, Tuna
Purpose This study aims to explore the plausibility of the functional near-infrared spectroscopy (fNIRS) methodology for neuromarketing applications and develop a neurophysiologically-informed model of purchasing behavior based on fNIRS measurements. Design/methodology/approach The oxygenation signals extracted from the purchase trials of each subject were temporally averaged to obtain average signals for buy and pass decisions. The obtained data were analyzed via both linear mixed models for each of the 16 optodes to explore their separate role in the purchasing decision process and a discriminant analysis to construct a classifier for buy/pass decisions based on oxygenation measures from multiple optodes. Findings Positive purchasing decisions significantly increase the neural activity through fronto-polar regions, which are closely related to OFC and vmPFC that modulate the computation of subjective values. The results showed that neural activations can be used to decode the buy or pass decisions with 85 per cent accuracy provided that sensitivity to the budget constraint is provided as an additional factor. Research limitations/implications The study shows that the fNIRS measures can provide useful biomarkers for improving the classification accuracy of purchasing tendencies and might be used as a main or complementary method together with traditional research methods in marketing. Future studies might focus on real-time purchasing processes in a more ecologically valid setting such as shopping in supermarkets. Originality/value This paper uses an emerging neuroimaging method in consumer neuroscience, namely, fNIRS. The decoding accuracy of the model is 85 per cent which presents an improvement over the accuracy levels reported in previous studies. The research also contributes to existing knowledge by providing insights in understanding individual differences and heterogeneity in consumer behavior through neural activities.
Citation - Scopus: 1
Analytical Approaches in Customer Relationship Management
(IEEE, 2023) Akata, Mustafa Aşkım; Ergin, Kaan; Kaya, Büşra; Kızılay, Ayşe; Çakar, Tuna; Şahin, Zeynep
This study examines the impact of analytical customer relationship management (aCRM) strategies, specifically the segmentation approach using RFM analysis and artificial learning methods, on customer satisfaction, revenue performance, and loyalty in businesses. The research adopts an approach that integrates data from both online and offline channels onto a single platform, providing a holistic view of customer behaviors. Combining the segmentation obtained through RFM analysis and artificial learning methods with timely campaigns has enhanced shopping opportunities for customers and increased customer satisfaction and loyalty. The use of aCRM as a strategic marketing and sales tool has enabled businesses to manage customer relationships more effectively. This paper contributes to the literature in this field by presenting in detail the advantages offered by aCRM, its application methods, and the results obtained.
Analyzing Consumer Behavior: the Impact of Retro Music in Advertisements on a Chocolate Brand and Consumer Engagement
(IEEE, 2023) Girişken, Yener; Soyaltın, Tuğçe Ezgi; Filiz, Gözde; Çakar, Tuna; Türkyılmaz, Ceyda Aysuna
This study presents research utilizing binary classification models to analyze consumer behaviors such as chocolate consumption and retro music ad viewing. Retro music, with its potential to evoke nostalgic feelings in consumers, is used in advertisements, which can have a significant impact on brand perception and consumer engagement. Firstly, a model focusing on chocolate consumption was developed and tested. The model yields significant outcomes. Secondly, a model based on retro music ad viewing status was developed and tested. Significant potential findings were obtained. This study emphasizes the applicability of effective classification models that can be used to understand and predict consumer behaviors, yielding significant outcomes.
Analyzing Customer Churn: a Comparative Study of Machine Learning Models on Pay-Tv Subscribers in Turkey
(IEEE, 2023) Obalı, Emir; Çalışkan, Sibel Kırmızıgül; Karani Yılmaz, Veysel; Kara, Erkan; Meşe, Yasemin Kürtcü; Çakar, Tuna; Yıldız, Ayşenur; Hataş, Tuğce Aydın
Understanding the reasons for customer churn provides added value in terms of retaining existing customers, as customer attrition leads to revenue loss for companies and incurs marketing costs for acquiring new customers. In this study, the 6-month historical data of a Pay-TV company operating in Turkey was used, and due to the imbalanced nature of the dataset on a label basis, the oversampling method was applied. During the model development phase, various artificial learning algorithms (Random Forest, Logistic Regression, KNearest Neighbors, Decision Tree, AdaBoost, XGBoost, Extra Tree Classifier) were utilized, and their performances were compared. Based on the evaluation of success criteria for each model, it was observed that the tree-based Random Forest, Extra Tree Classifier and XGBoost achieved the highest performance for this dataset.
Citation - Scopus: 1
Artificial Intelligence Driven Multivariate Time Series Analysis of Network Traffic Prediction
(Institute of Electrical and Electronics Engineers Inc., 2024) Filiz, G.; Yıldız, A.; Kara, E.; Altıntaş, S.; Çakar, T.
The primary objective of this research is to employ artificial intelligence, machine learning, and neural networks in order to construct a network traffic prediction model. The analysis of network traffic data obtained from a digital media and entertainment provider operating in Turkey is conducted through the application of multivariate time-series analysis techniques in order to get insights into the temporal patterns and trends. In model development, Vector Autoregression (VAR), Vector Error Correction Model (VECM), Long-Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) algorithms have been utilized. LSTM and GRU models have performed better with low Mean Absolute Percentage Error (MAPE) and high R-squared Score (R2). LSTM model has reached 0.98 R2 and 8.95% MAPE. These results indicate that the models can be utilized in network management optimization as resource allocation, congestion detection, anomaly detection, and quality of service. © 2024 IEEE.
Citation - WoS: 2
Citation - Scopus: 2
Classification of Altruistic Punishment Decisions by Optical Neuroimaging and Machine Learning Methods
(IEEE, 2023) Erözden, Ozan; Şahin, Türkay; Akyürek, Güçlü; Filiz, Gözde; Çakar, Tuna
Altruistic 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.
Citation - Scopus: 4
Classification of Skin Lesion Images With Deep Learning Approaches
(University of Latvia, 2022) Kulavuz, Bahadır; Ertuğrul, Berkay; Bakırman, Tolga; Çakar, Tuna; Doğan, Metehan; Bayram, Bülent; Bayram, Buket
Skin cancer is one of the most dangerous cancer types in the world. Like any other cancer type, early detection is the key factor for the patient's recovery. Integration of artificial intelligence with medical image processing can aid to decrease misdiagnosis. The purpose of the article is to show that deep learning-based image classification can aid doctors in the healthcare field for better diagnosis of skin lesions. VGG16 and ResNet50 architectures were chosen to examine the effect of CNN networks on the classification of skin cancer types. For the implementation of these networks, the ISIC 2019 Challenge has been chosen due to the richness of data. As a result of the experiments, confusion matrices were obtained and it was observed that ResNet50 architecture achieved 91.23% accuracy and VGG16 architecture 83.89% accuracy. The study shows that deep learning methods can be sufficiently exploited for skin lesion image classification. © 2022 Baltic Journal of Modern Computing. All rights reserved.
Citation - Scopus: 1
Consumer Neuroscience Perspective for Brands: How Do Brands Influence Our Brains?
(IGI Global, 2020) Çakar, Tuna; Girişken, Yener
Neuroscientific 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.
Curvature Effect on Aesthetic Perception
(Cognitive Science Society, 2022) Demircioğlu, Tuna Esin; Çakar, Tuna; Girişken, Yener
Aesthetic perception is an inseparable part of the decision-making process in daily life. It also is an important partof the beauty and therefore tastes. The determination of preferences is directly related to the subregions of the PFC.The contour is the essential visual attribute for accurately perceiving the form of an object. It has been known thatsharp angles cause an implicit perception of threat, and perceived security is related to aesthetic pleasure. The aim ofthe study is to investigate the effect of contour type on decision making and aesthetic perception in PFC. The studyusing the fNIRS method has shown that there is a marginal significant relation between liking, contour type, and PFCareas (F(3.81)=2.225, p>.092, η2=.076). Current findings suggest that left mPFC, FPC, and right dlPFC have a significantcontribution to the liking of curved objects.
Customer Segmentation and Churn Prediction via Customer Metrics
(IEEE, 2022) Bozkan, Tunahan; Cakar, Tuna; Sayar, Alperen; Ertugrul, Seyit
In this study, it is aimed to predict whether customers operating in the factoring sector will continue to trade in the next three months after the last transaction date, using data-driven machine learning models, based on their past transaction movements and their risk, limit and company data. As a result of the models established, Loss Analysis (Churn) of two different customer groups (Real and Legal factory) was carried out. It was estimated by the XGBoost model with an F1 Score of 74% and 77%. Thanks to this modeling, it was aimed to increase the retention rate of customers through special promotions and campaigns to be made to these customer groups, together with the prediction of the customers who will leave. Thanks to the increase in retention rates, a direct contribution to the transaction volume on a company basis was ensured.
Citation - WoS: 4
Citation - Scopus: 5
Designing restorative landscapes for students: A Kansei engineering approach enhanced by VR and EEG technologies
(Elsevier, 2024) Karaca, Elif; Çakar, Tuna; Karaca, Mehmet; Gul, Hasan Huseyin Mirac
This study explores the alignment of specific landscape features within school environments with the core elements of Attention Restoration Theory (ART) that includes Coherence, Fascination, Compatibility, and Being Away. Utilizing Kansei Engineering, this research integrates emotional analysis into landscape design by employing Virtual Reality (VR) and Electroencephalogram (EEG) technologies to record students' responses to different landscape simulations. Analytical techniques, including the Taguchi Method and Analysis of Variance (ANOVA), were applied to evaluate the data. The findings have revealed that students associate a sense of enclosure with a coherent landscape and openness with a fascinating landscape, the lawn's significance was also highlighted for coherent landscape. However, limited insights were gained regarding Compatibility and Being Away. The study advocates for diverse cognitive zones within school landscapes to promote mental restoration, emphasizing the need for varied design elements that cater to the elevated experience of students.
Detecting the Effect of Voice-Over in Tv Ads Via Optic Brain Imaging (fnirs) and In-Depth Interview Methods
(2016) Çakar, Tuna; Girisken, Yener
Voice-overs are used extensively to increase the effectiveness of the TV ads especially in the last decade. The main purpose is to provide the brand message via a clear feature that will inevitably grab the attention of the viewers. The current study contains the neuro tests of 12 TV ads in banking and finance sectors on 168 participants in 8 groups. Optic brain imaging (fNIRS) and in-depth interviews were the methodologies utilized during the test of these TV ads. The obtained results indicate that the use of voice-over during the TV ads possibly causes the decrease in attention and emotional engagement levels of the participants.
Determination of Alzheimer's Disease Levels by Ordinal Logistic Regression and Artificial Learning Algorithms
(Ieee, 2024) Bulut, Nurgül; Çakar, Tuna; Arslan, Ilker; Akinci, Zeynep Karaoglu; Oner, Kevser Setenay
This 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.
Development of a Knowledge-Based Multimodal Deep Learning System for Automatic Breast Lesion Segmentation and Diagnosis in Mg/Dmr Images
(IEEE, 2023) Orhan, Gözde; Çavuşoğlu, Mustafa; Sürmeli, Hulusi Emre; Çakar, Tuna; Araz, Nusret; Bayram, Bülent
Deep learning networks (DLNs) rely on labeled training datasets as their fundamental building blocks. While various databases exist worldwide, there is currently no domestic solution available in our country. This project aims to create a domestic database by automatically segmenting breast lesions in MG/DMR images based on their types and developing a knowledge-based multimodal DL-based integrated computer-aided diagnosis system to analyze the images, thereby providing the system with continuous learning capability. Different brands of devices exist for MG/DMR, necessitating the multimodal operation of image processing/artificial intelligence algorithms. To achieve this goal, the network was trained first, and then prelearned data were transferred to enable the training of data from different networks once accurate results are obtained. The developed system has the potential to enable the automatic detection of breast lesions, ensuring fast and high diagnostic accuracy. Additionally, it might also facilitate the retrospective analysis of patients' periodic check-up results.
Differential Effect of Young Adults and Students Metacognitive Skills in Mathematics Problem Solving Process
(eScholarship, 2023) Birgili, Bengi; Can, Rümeysa; Çakar, Tuna; Akar, Hanife
The 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.
Citation - Scopus: 1
Distinguishing Cognitive Processes: a Machine Learning Approach To Decode Fnirs Data for Third-Party Punishment and Credit Decision-Making
(Ieee, 2024) Filiz, Gozde; Son, Semen; Sayar, Alperen; Ertugrul, Seyit; Sahin, Turkay; Akyurek, Guclu; Çakar, Tuna
Functional 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.
Dog Walker Segmentation
(IEEE, 2022) Ercan, Alperen; Karan, Baris; Çakar, Tuna
In this study dog walkers were separated into clusters according to walkers' walk habits. Due to the fact that the distributions were non-normal, normalization algorithms were applied before the onset of clustering. After normalizing, K Means algorithm and Gaussian Mixture Models used for finding optimum cluster count. According to these clusters, walkers' consecutive months separated to follow-up their behavioral traits. This part of the study adds value to the project to examine walkers' behaviors closer.
Citation - Scopus: 3
Emg-Based Bci for Picar Mobilization
(IEEE, 2022) Yilmaz, Yasin; Günden, Burak Bahri; Ertekin, Efe; Sayar, Alperen; Çakar, Tuna; Arslan, Şefik Şuayb
In this study, the main scope was to develop a brain-computer interface (BCI) with the use of PiCar and EEG/ERP devices. Thus, it is aimed to facilitate the lives of people with certain diseases and disabilities. The ultimate goal of this project has been to direct and control a BCI-based PiCar concerning the signals captured via the EEG/ERP device. With the EEG headset, the EMG signals of the gestures (facial expressions) of the participant were captured. With the collected data, filtering and other preprocessing methods were applied to have noise-free signals. In the preprocessing, the detrending method was used to clean the data set which showed a constantly increasing trend, to a certain range, and zero trends. The denoising (Wavelet Denoising) and outlier detection/elimination methods (OneClassSVM) were used for noise elimination. The SMOTE oversampling method was used for data augmentation. Welch's method was used to get band powers from the signals. With the use of augmented data, several machine learning algorithms were applied such as Support Vector Machine, Logistic Regression, Linear Discriminant Analysis, Random forest Classifier, Gradient Boosting Classifier, Multinomial Naive Bayes, Decision tree, K-Nearest Neighbor, and voting classifier. The developed models were used to predict the direction that is passed as an input to PiCar's API. After that, PiCar was controlled concerning the predicted direction with HTTP GET requests. In this project, the OpenBCI headset and the Brainflow library for EEG/EMG signal obtaining and processing were used. Also, the Tkinter library was used for the Graphical user interface and Django for establishing a server on PiCar's brain which is RaspberryPi. © 2022 IEEE.

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