Bilgisayar Mühendisliği Bölümü Koleksiyonu

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

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Now showing 1 - 10 of 28
  • Article
    Citation - WoS: 15
    Citation - Scopus: 15
    Enhanced Primordial Gravitational Waves From a Stiff Postinflationary Era Due To an Oscillating Inflaton
    (Amer Physical Soc, 2024-09-25) Chen, Chao; Dimopoulos, Konstantinos; Eroncel, Cem; Ghoshal, Anish
    We investigate two classes of inflationary models, which lead to a stiff period after inflation that boosts the signal of primordial gravitational waves (GWs). In both families of models studied, we consider an oscillating scalar condensate, which when far away from the minimum is overdamped by a warped kinetic term, a la alpha-attractors. This leads to successful inflation. The oscillating condensate is in danger of becoming fragmented by resonant effects when nonlinearities take over. Consequently, the stiff phase cannot be prolonged enough to enhance primordial GWs at frequencies observable in the near future for low orders of the envisaged scalar potential. However, this is not the case for a higher-order scalar potential. Indeed, we show that this case results in a boosted GW spectrum that overlaps with future observations without generating too much GW radiation to destabilize big bang nucleosynthesis. For example, taking alpha=O(1), we find that the GW signal can be safely enhanced up to Omega(GW) (f)similar to 10(-11) at frequency f similar to 10(2) Hz, which will be observable by the Einstein Telescope. Our mechanism ends up with a characteristic GW spectrum, which if observed, can lead to the determination of the inflation energy scale, the reheating temperature, and the shape (steepness) of the scalar potential around the minimum.
  • Article
    Citation - WoS: 6
    Citation - Scopus: 8
    Designing restorative landscapes for students: A Kansei engineering approach enhanced by VR and EEG technologies
    (Elsevier, 2024-09-01) Karaca, Elif; Çakar, Tuna; Karaca, Mehmet; Gul, Hasan Huseyin Mirac; Hüseyin Miraç Gül, Hasan
    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.
  • Article
    Citation - WoS: 1
    Citation - Scopus: 1
    Understanding the Psychological and Financial Correlates for Consumer Credit Use;
    (Sosyoekonomi Society, 2024-01-31) Ertuğrul, Seyit; Sayar, Alperen; Şahin, Türkay; Çakar,Tuna; Ertuğru, Seyit
    This study investigated the behavioural and cognitive predictors of consumer credit usage to develop a behavioural credit risk assessment procedure for a factoring company. Participants completed surveys measuring personality traits, self-esteem, material and monetary values, compulsive and impulsive buying tendencies, self-control, and impulsiveness. Financial surveys also assessed financial literacy and knowledge of financial concepts. The results indicated that extraversion, conscientiousness, emotional stability, and experiential self-control were significant predictors of consumer credit usage. These findings suggest that a finance company can use these personality traits and financial characteristics to develop a more accurate and effective credit risk assessment procedure, such as psychometric tests. © 2024, Sosyoekonomi Society. All rights reserved.
  • Article
    Citation - WoS: 4
    Citation - Scopus: 8
    Unraveling Neural Pathways of Political Engagement: Bridging Neuromarketing and Political Science for Understanding Voter Behavior and Political Leader Perception
    (Frontiers Media SA, 2023-12-21) Çakar, Tuna; Filiz, Gözde
    Political 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
  • Conference Object
    Predicting Animal Behaviours: Physical and Behavioural Classification Of Dog Walking Levels
    (IEEE, 2022-05-15) Ozen, Guris; Karan, Baris; Çakar, Tuna
    Methods of predicting canine behaviour is an area covered by canine behaviour experts. This study aims to predict the behaviour of dogs during walking based on available information about dogs. In this data-driven project based on up-to-date company data, the problem of predicting dog behaviour was addressed in two different ways. First, it is aimed to create a supervised classification model. Within the scope of this study, improvements were made to various classification algorithms. The results were analyzed in different axes. Secondly, it is aimed to create a new parameter that predicts dog walking difficulties by formulating the parameters.
  • Conference Object
    Dog Walker Segmentation
    (IEEE, 2022-05-15) 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.
  • Conference Object
    Citation - WoS: 1
    Citation - Scopus: 1
    Face Recognition With Local Zernike Moments Features Around Landmarks
    (IEEE, 2016-05-01) Gökmen, Muhittin; Basaran, Emrah
    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.
  • Conference Object
    Customer Segmentation and Churn Prediction via Customer Metrics
    (IEEE, 2022-05-15) 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.
  • Conference Object
    Residual Data Usage in LDPC Codes
    (IEEE, 2022-05-15) Kaya, Erdi; Pourmandi, Massoud; Haytaoglu, Elif; Arslan, Şefik Şuayb
    In distributed storage systems/coded caching systems, padding operations should be performed when the encoded data cannot be divided by the number of storage nodes evenly. Thus, extra zero values are stored in one of the nodes to balance each node's storage content. In this study, distribution of data to storage nodes with no padding was investigated for distributed caching context in which a base station and devices both store the coded data. In other words, no redundancy (no-padding) is included into the encoded data. This approach is named as residual data distribution. LDPC codes are selected as the erasure code due to their low complexity encode/decode operations. Moreover, performance comparisons were conducted between using traditional data distribution approach (with padding) and using residual data (use of no-padding) (standard) in terms of repair time. In our work, the effect of no-padding data usage on the repair time and the ratios of storage savings have been also demonstrated.
  • Conference Object
    Citation - WoS: 2
    Citation - Scopus: 2
    Base Station-Assisted Cooperative Network Coding for Cellular Systems With Link Constraints
    (IEEE, 2022-06-26) Arslan, Suayb S.; Pourmandi, Massoud; Haytaoglu, Elif
    We consider a novel distributed data storage/caching scenario in a cellular network, where multiple nodes may fail/depart simultaneously To meet reliability, we allow cooperative regeneration of lost nodes with the help of base stations allocated in a set of hierarchical layers1. Due to this layered structure, a symbol download from each base station has a different cost, while the link capacities between the nodes of the cellular system and the base stations are also constrained. Under such a setting, we formulate the fundamental trade-off with closed form expressions between repair bandwidth cost and the storage space per node. Particularly, the minimum storage as well as bandwidth cost points are formulated. Finally, we provide an explicit optimal code construction for the minimum storage regeneration point for a special set of system parameters.