Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1926

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Now showing 1 - 20 of 51

Citation - Scopus: 1
A Microwave Imaging Scheme for Detection of Pulmonary Edema and Hemorrhage
(IEEE, 2022) Ertek, Didem; Kucuk, Gokhan; Bilgin, Egemen
The microwave imaging systems have the potential to present a cost effective and less hazardous alternative to conventional medical imaging techniques. In this paper, a Contrast Source Inversion method based microwave imaging scheme is proposed and tested for the detection of pulmonary edema and hemorrhage. To this end, a realistic human torso phantom is used, and the electromagnetic parameters of the human tissues is determined via Cole-Cole model. The scattered field is simulated via Method of Moments at the operating frequency of 350 MHz, and a 50 dB white Gaussian noise is added to model a realistic measurement setup. The numerical tests performed with the proposed technique suggest that the method can be used to locate the pulmonary edema and hemorrhage, and it is capable of distinguishing these two medical conditions successfully.
A Resonator Design For Mutual Coupling Reduction Between Microstrip Antennas In Mımo Applications At 28 Ghz
(Institute of Electrical and Electronics Engineers Inc., 2024) Gollu, A.A.; Polat, B.; Semerci, D.; Bilgin, E.
A simple resonator structure is proposed to reduce the mutual coupling between rectangular microstrip patch antennas positioned close to each other for MIMO applications at 28 GHz center frequency. Here, the frequency of 28 GHz is chosen because it is one of middle bands for 5G communication in USA. Two microstrip patch antennas with gaps using a common dielectric substrate and a ground plane are employed as antennas and the patches are closely placed with an edge-to-edge distance of 0.6 mm (approximately λ/18). In order to reduce the mutual coupling between these radiating elements and increase the isolation, a resonator is positioned between them and its parameters are optimized. In the simulations, it is observed that the proposed resonator reduces the coupling by approximately 10 dB. By this result, it can be concluded that the proposed structure may be suitable for tightly packed MIMO systems. © 2024 IEEE.
Citation - Scopus: 2
A Visualization Platfom for Disk Failure Analysis
(IEEE, 2018) Arslan, Şuayb Şefik; Yiğit, İbrahim Onuralp; Zeydan, Engin
It has become a norm rather than an exception to observe multiple disks malfunctioning or whole disk failures in places like big data centers where thousands of drives operate simultaneously. Data that resides on these devices is typically protected by replication or erasure coding for long-term durable storage. However, to be able to optimize data protection methods, real life disk failure trends need to be modeled. Modelling helps us build insights while in the design phase and properly optimize protection methods for a given application. In this study, we developed a visualization platform in light of disk failure data provided by BackBlaze, and extracted useful statistical information such as failure rate and model-based time to failure distributions. Finally, simple modeling is performed for disk failure predictions to alarm and take necessary system-wide precautions.
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.
Citation - WoS: 14
Citation - Scopus: 40
An Overview of Blockchain Technologies: Principles, Opportunities and Challenges
(IEEE, 2018) Arslan, Şuayb Şefik; Mermer, Gültekin Berahan; Zeydan, Engin
Blokzincir, toplumumuzun birbiriyle iletişim kurma ve ticaret yapma biçiminde devrim yapma potansiyeline sahip, yakın zamanda ortaya çıkmış olan bir teknolojidir. Bu teknolojinin sağladığı en önemli avantaj aracı gerektiren bir oluşumda güvenilir bir merkezi kuruma ihtiyaç duymadan değer taşıyan işlemleri değiş tokuş edebilmesidir. Ayrıca, veri bütünlüğü, dahili orijinallik ve kullanıcı şeffaflığı sağlayabilir. Blokzincir, birçok yenilikçi uygulamanın temel alınacağı yeni internet olarak görülebilir. Bu çalışmada, genel çalışma prensibi, oluşan fırsatlar ve ileride karşılaşılabilecek zorlukları içerecek şekilde güncel blokzincir teknolojilerinin genel bir görünümünü sunmaktayız.
Citation - Scopus: 7
Bilinçli-farkındalık Temelli Öz-yeterlik Ölçeği-yenilenmiş (bföö-y): Türkiye Uyarlama Çalışması
(2017) Taylan, Rukiye Didem; Bulgan, Gökçe; Atalay, Zümra; Aydın, Utkun
Bu araştırmanın amacı, Cayoun, Francis, Kasselis ve Skilbeck (2012) tarafından geliştirilen "Bilinçli- Farkındalık Temelli Öz-yeterlik Ölçeği-Yenilenmiş"i (Mindfulness-Based Self Efficacy Scale-Revised) Türkçe'ye uyarlayarak geçerlik ve güvenirliğini araştırmaktır. Özgün ölçek İngilizce'dir ve altı boyutta toplam 22 maddeden oluşan beşli likert tipi bir ölçme aracıdır. Uyarlanan Türkçe form iki farklı devlet okulunun 5., 6. ve 7. sınıflarında okuyan 713 öğrenciye uygulanmıştır. Tüm ölçek (?= .72) ve ölçeğin Duygu Düzenleme (?= .73), Duygusal Denge (?= .68), Sosyal Beceriler (?= .65), Sıkıntı Tahammülü (?= .62), Sorumluluk Alma (?= .61) ve Kişilerarası Etkenlik (?= .65) alt boyutları için Cronbach Alfa içtutarlık katsayıları her bir alt boyutta yer alan düşük madde sayısı göz önüne alındığında kabul edilebilir seviyededir. Ayırt edici geçerlik analizleri kız ve erkeklerin bilinçli-farkındalık temelli öz-yeterlik ortalama puanları arasında anlamlı bir fark olmadığını gösterirken sınıf düzeyi açısından anlamlı farklılıklar gözlemlenmiştir. Analiz sonuçları, Türkçe'ye uyarlama çalışması gerçekleştirilen bu ölçeğin öğrencilerin bilinçli-farkındalık temelli öz-yeterlik düzeylerini belirlemede geçerli ve güvenilir bir ölçme aracı olduğunu göstermektedir. Sonuçların kuramsal ve yöntemsel uygulamaları tartışılmıştır
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: 1
Cnn-Based Emotion Recognition Using Data Augmentation and Preprocessing Methods
(Institute of Electrical and Electronics Engineers Inc., 2023) Toktaş, Tolga; Kırbız, Serap; Kayaoğlu, Bora
In this paper, a system that recognizes emotion from human faces is designed using Convolutional Neural Networks (CNN). CNN is known to perform well when trained with a large database. The lack of large and balanced publicly available databases that can be used by deep learning methods for emotion recognition is still a challenge. To overcome this problem, the number of data is increased by merging FER+, CK+ and KDEF databases; and preprocessing is applied to the face images in order to reduce the variations in the database. Data augmentation methods are used to reduce the imbalance in the data distribution that still remains despite the increasing number of data in the merged database. The CNN-based method developed using database merging, image preprocessing and data augmentation, achieved emotion recognition with 80% accuracy.
Citation - Scopus: 8
Çocuklarda Sınav Kaygısı Ölçeği'nin Türkçe Uyarlaması
(İlköğretim Online, 2017) Bulgan, Gökçe; Aydın, Utkun
The purpose of this study was to adapt the “Children’s Test Anxiet Scale (CTAS)” developed by Wren and Benson (2004) into Turkish. The original scale was in English and comprised of three factors including 30 items. Seven experts were involved in the adaptation process to translate the scale into Turkish and then back to English for providing evidence based on the consistency between the two forms. Following the translation process, a pilot study was conducted and the scale was given its final form. The Turkish form was administered to 1100 students who were attending to 3rd, 4th, 5th, and 6th grade classes in 3 public schools. Findings regarding the construct validity of the scale, which were obtained from the confirmatory analysis, supported the three-factor structure of the original scale. Subdimensions of the scale were Thoughts, Off-Task Behaviors, and Autonomic Reactions. Cronbach Alpha coefficients for the overall scale (???= .88) as well as the subdimensions of Thoughts (???= .82), Off-Task Behaviors (???= .72), and Autonomic Reactions (???= .75) were substantial in size. Regarding the discriminant validity analyses, there were no significant gender differences in students’ test anxiety while there were significant grade level differences. These results demonstrated that the Turkish version of the scale is a valid and reliable instrument, which may serve as useful in measuring elementary school students’ test anxiety levels. Directions for future research and practical implications for educational practice are discussed in terms of mathematics education.
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: 2
Citation - Scopus: 2
Data Repair in Bs-Assisted Distributed Data Caching
(IEEE, 2020) Kaya, Erdi; Haytaoğlu, Elif; Arslan, Şuayb Şefik
In this paper, centralized and independent repair approaches based on device-to-device communication for the repair of the lost nodes have been investigated in a cellular network where distributed caching is applied whose fault tolerance is provided by erasure codes. The caching mechanisms based on Reed-Solomon codes and minimum bandwidth regenerating codes are adopted. The proposed approaches are analyzed in a simulation environment in terms of base station utilization load during the repair process. Based on the intuitive assumption that the base station is usually more costly than device-to-device communication, the centralized repair approach demonstrates a better performance than the independent repair approaches on the number of symbols retrieved from the base station. On the other hand, the centralized approach has not achieved a dramatic reduction in the number of symbols downloaded from the other devices.
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.
Dialogue Enhancement Using Kernel Additive Modelling
(Institute of Electrical and Electronics Engineers Inc., 2015) Liutkus, A.; Kırbız, Serap; Cemgil, A. Taylan
It is a major problem for the sound engineers to find the right balance between the dialogue signals and the ambient sources. This problem also makes one of the main causes of the audience concerns. The audience wants to arrange the sound balance based on their personal preferences, listening environment and their hearing. In this work, a method is proposed for enhancing the dialogue signals in stereo recordings that consist of more than one source. The kernel additive modelling that has been used successfully in sound source separation is used to extract the dialogues and the ambient sources from the movie sounds. The separated dialogue and ambient sources can later be upmixed by the user to make a personal mix. The separation performance of the proposed method is evaluated on the sounds generated by mixing the sources which were taken from the only dialogue and only music parts of the movies. It has been shown that the Kernel Additive Modelling (KAM) based method can be successfully used for dialogue enhancement. © 2015 IEEE.
Dil Modelleri ile Akademik Özet Üretimi
(Institute of Electrical and Electronics Engineers Inc., 2025) Bektas, Busra; Gultekin, Ali Ozgun; Ozdemiroglu, Emre; Yilmaz, Zeynep; Dikici, Buse; Demir, Seniz
In recent years, large language models have demonstrated extraordinary capabilities in natural language processing tasks. The integration of these models to text summarization has highlighted the need for evaluating varying model performances under a standardized benchmarking framework. In this study, the performance of different large language models in generating abstracts of scientific papers which has a common structure and unique language is compared through an extensive experimental analysis. The abstracts automatically generated by these models using prompt engineering were evaluated via various evaluation metrics based on content overlap and semantic similarity. The results that we obtained demonstrated the effectiveness of large language models in abstract generation. © 2025 Elsevier B.V., All rights reserved.
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 - WoS: 1
Citation - Scopus: 1
Domain Adaptation Approaches for Acoustic Modeling
(IEEE, 2020) Arısoy, Ebru; Fakhan, Enver
In the recent years, with the development of neural network based models, ASR systems have achieved a tremendous performance increase. However, this performance increase mostly depends on the amount of training data and the computational power. In a low-resource data scenario, publicly available datasets can be utilized to overcome data scarcity. Furthermore, using a pre-trained model and adapting it to the in-domain data can help with computational constraint. In this paper we have leveraged two different publicly available datasets and investigate various acoustic model adaptation approaches. We show that 4% word error rate can be achieved using a very limited in-domain data.
Eaft: Evolutionary Algorithms for Gcc Flag Tuning
(IEEE, 2022) Tagtekin, Burak; Çakar, Tuna
Due to limited resources, some methods come to the fore in finding and applying the factors that affect the working time of the code. The most common one is choosing the correct GCC flags using heuristic algorithms. For the codes compiled with GCC, the selection of optimization flags directly affects the speed of the processing, however, choosing the right one among hundreds of markers during this process is a resource consuming problem. This article explains how to solve the GCC flag optimization problem with EAFT. Rather than other autotuner tools such as Opentuner, EAFT is an optimized tool for GCC marker selection. Search infrastructure has been developed with particle swarm optimization and genetic algorithm with diffent submodels rather than using only Genetic Algorithm like FOGA. © 2022 IEEE.
Evaluating Electrophysiological Responses Due To Identity Judgments
(Ieee, 2024) Çakar, Tuna; Hohenberger, Annette
This study was conducted to explore how the brain processes decisions about identity, employing event-related potentials (ERPs) as a measure. The aim was to ascertain if the EEG/ERP technique could be used to monitor the cognitive processing of identity judgments as they happen. The investigation focused on comparing two groups of statements: those that used the concept of 'same' and those that used 'different'. The researchers hypothesized that there would be notable differences in the ERPs, particularly around the 400-millisecond mark, correlating with the reaction time disparities observed behaviorally. The ERP data revealed that the 'different' statements generated a unique N400 response when contrasted with the 'same' statements, implying that the participants' cognitive responses to these two types of judgments were not the same.
Citation - WoS: 1
Citation - Scopus: 1
Face Recognition With Local Zernike Moments Features Around Landmarks
(IEEE, 2016) Gökmen, Muhittin
In this paper, a new method that extracts the features from the complex Local Zernike Moments (LZM) images around facial landmarks is proposed. In this method, multiple grids which are in different sizes are located on landmarks and Phase-Magnitude (PM) histograms are calculated in each cells of these grids. The PM histograms are calculated for every component of LZM and the feature vectors are created by concatenating these histograms. By reducing the dimensionality of these vectors using Whitened Principle Component Analysis, more robust descriptors are constructed. It is shown that the state-of-the-art results are obtained in the experiments performed on FERET database using the proposed method. © 2016 IEEE.

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