Bilgisayar Mühendisliği Bölümü Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1940
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Conference Object Citation - WoS: 2Citation - Scopus: 2Classification of Altruistic Punishment Decisions by Optical Neuroimaging and Machine Learning Methods(IEEE, 2023-07-05) 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.Conference Object Citation - WoS: 6Citation - Scopus: 6Efficient Strategy for Multi-Uav Path Planning in Target Coverage Problems(IEEE, 2022-09-29) Bekmezci, İlker; Pehlivanoğlu, Perihan; Pehlivanoğlu, Y. VolkanIn recent years, multi unmanned aerial vehicles (UAVs) are used in the same system to accomplish more complex missions. In many multi-UAV system applications, the main objective is to visit some predetermined checkpoints in operational area. If the number of check points and constraints increases, finding a feasible solution takes up too much time. In this paper, a checkpoint based multi-UAV path planning problem is solved by using improved genetic algorithm. The main contributions of this paper are: (1) the introducing revisit time interval concept, (2) the investigating of the effect of objective function description, and (3) looking into an outcome of using multiple runways on optimal multi-UAV path planning. The proposed strategy-based optimization methodology is performed for checkpoint based multi-UAV path planning problems in two-dimensional (2D) environment. Performance results show that the proposed strategy provides effective and feasible paths for each UAV.Conference Object Noise Effect on Forecasting(IEEE, 2023-07-05) Tuncer, Suat; Kayan, Ersan; Çakar, TunaThe lack of regulation and liquidity in crypto money markets causes higher volatility compared to other financial markets. This situation increases the noise in price change. The high noise and random walk create a problem that cannot be explained by traditional stochastic financial methods. For this reason, a multi-layered deep learning model with an additive attention layer, which uses a single observation in 10-day sequences, was used in this study. Different transformations are used to reduce the noise of the closing values. As a result of the comparisons made between different approaches, it has been revealed that exponential moving averages, to be used as the value to predict, give better results than other conversions and estimation of the original price, since they explain the price better than simple moving averages and reduce the noise of the original price.Conference Object Citation - Scopus: 2Alternative Data Sources and Psychometric Scales Supported Credit Scoring Models(IEEE, 2023-07-05) Şahin, Türkay; Filiz, Gözde; Çakar, Tuna; Özvural, Özden Gebizlioğlu; Nicat, Şahin; Gebizlioǧlu Özvural, ÖzdenThis 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.Conference Object Neural Correlates of Identity Judgments in the Prefrontal Cortex: an Optical Brain Imaging (fnirs) Study(IEEE, 2023-07-05) Çakar, Tuna; Hohenberger, AnnetteThe prefrontal cortex (PFC) plays a crucial role in human reasoning and decision-making. Studies using neuroimaging techniques have shown that situations involving conflicts lead to increased activity in both the prefrontal cortex and the anterior cingulate cortex (ACC). This research specifically investigates the activity in the prefrontal cortex when individuals assess statements related to identity. The results obtained through optical brain imaging (fNIRS) indicate that participants experience greater conflict when evaluating propositions they strongly disagree with, compared to propositions they strongly agree with. Furthermore, responses that are indeterminate lead to higher activation levels in prefrontal regions. Additionally, the analysis of the participants' reaction times reveals significant differences associated with the content of their responses.Conference Object Transaction Volume Estimation in Financial Markets With Lstm(IEEE, 2023-07-05) Bozkan, Tunahan; Çakar, Tuna; Ertuğrul, Seyit; Sayar, Alperen; Akçay, AhmetIn this study, it was aimed to determine the transaction volume that will be encountered in the future (hourly) in the factoring sector, and then to take financial and operational action early. For the study, the LSTM model, which is a kind of recurrent neural network (RNN) that can capture long and short-term dependencies, was applied by using data-driven approaches to estimate the check amounts of hourly transactions. As a result of the results, it was aimed to increase the operational efficiency in a broad scope by allowing the factoring company to determine the loan amounts to be obtained from banks in the most optimal way, and then to take early action within the scope of both the workforce and business management of the financial resource allocation management process and operational activities. MAPE score was used as a measure of error in the time series analysis model. MAPE scores were found as %5.05 for 30 days, %4.18 for 10 days, %3.47 for 5 days, %3.09 for 3 days and %1.83 for 1 day. According to the MAPE scores calculated for different days, the enterprise will be able to decide on the loan to be drawn from banks both in terms of time and amount, and the necessary action will be taken.Conference Object Predicting Animal Behaviours: Physical and Behavioural Classification Of Dog Walking Levels(IEEE, 2022-05-15) Ozen, Guris; Karan, Baris; Çakar, TunaMethods 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, TunaIn 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: 1Citation - Scopus: 1Face Recognition With Local Zernike Moments Features Around Landmarks(IEEE, 2016-05-01) Gökmen, Muhittin; Basaran, EmrahIn 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 Citation - Scopus: 1Liking Prediction Using fNIRS and Machine Learning: Comparison of Feature Extraction Methods(IEEE, 2022-05-15) Koksal, Mehmet Yigit; Çakar, Tuna; Demircioğlu, Esin Tuna; Girisken, Yener; Tuna, EsinThe 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.