Oğuz, Kaya
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Oguz, K. A. Y. A.
Oguz, K.
Oğuz, K.
Oguz, Kaya
Oguz, K.
Oğuz, K.
Oguz, Kaya
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Email Address
kaya.oguz@ieu.edu.tr
Main Affiliation
05.05. Computer Engineering
Status
Current Staff
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Sustainable Development Goals
8
DECENT WORK AND ECONOMIC GROWTH
0
Research Products
9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
6
Research Products
10
REDUCED INEQUALITIES
0
Research Products
17
PARTNERSHIPS FOR THE GOALS
0
Research Products
12
RESPONSIBLE CONSUMPTION AND PRODUCTION
0
Research Products
7
AFFORDABLE AND CLEAN ENERGY
0
Research Products
1
NO POVERTY
0
Research Products
5
GENDER EQUALITY
0
Research Products
13
CLIMATE ACTION
0
Research Products
4
QUALITY EDUCATION
3
Research Products
14
LIFE BELOW WATER
0
Research Products
2
ZERO HUNGER
0
Research Products
15
LIFE ON LAND
0
Research Products
16
PEACE, JUSTICE AND STRONG INSTITUTIONS
0
Research Products
6
CLEAN WATER AND SANITATION
0
Research Products
3
GOOD HEALTH AND WELL-BEING
0
Research Products
11
SUSTAINABLE CITIES AND COMMUNITIES
2
Research Products
Documents
42
Citations
950
h-index
8
Documents
36
Citations
637
Scholarly Output
49
Articles
19
Views / Downloads
96/136
Supervised MSc Theses
6
Supervised PhD Theses
1
WoS Citation Count
520
Scopus Citation Count
822
WoS h-index
6
Scopus h-index
7
Patents
0
Projects
10
WoS Citations per Publication
10.61
Scopus Citations per Publication
16.78
Open Access Source
18
Supervised Theses
7
| Journal | Count |
|---|---|
| 2019 Medıcal Technologıes Congress (Tıptekno) | 3 |
| 3rd International Informatics and Software Engineering Conference, IISEC 2022 | 2 |
| 2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024 -- 2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024 -- 16 October 2024 through 18 October 2024 -- Ankara -- 204562 | 2 |
| 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 | 2 |
| Informatıon Scıences | 2 |
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49 results
Scholarly Output Search Results
Now showing 1 - 10 of 49
Article Estimating the Difficulty of Tartarus Instances(Pamukkale Univ, 2021) Oguz, KayaTartarus is a commonly used benchmark problem for genetic programming. However, it has never been fully explored for its difficulty tuning property. Using the data from a previous study in which we have executed millions of Tartarus instances, we contribute to the literature with an equation to estimate their difficulty. Our approach uses four metrics that are embedded into the equation. These metrics are related to the number of clusters and clusters sizes, the distances of boxes to the edges of the board grid, the number of boxes around the agent, and the minimum number of actions for the agent to reach the largest cluster. The coefficients of these metrics have been fit to the data using the general linear model and a mean residual error of similar to 0.1 has been achieved. This is the first study that can estimate the difficulty of a Tartarus board without modifying the problem in any way.Article Citation - WoS: 2Citation - Scopus: 2The Effect of Emotional Faces on Reward-Related Probability Learning in Depressed Patients(Elsevier B.V., 2024) Keskin-Gokcelli, D.; Kizilates-Evin, G.; Eroglu-Koc, S.; Oğuz, Kaya; Eraslan, C.; Kitis, O.; Gonul, A.S.Background: Existing research indicates that individuals with Major Depressive Disorder (MDD) exhibit a bias toward salient negative stimuli. However, the impact of such biased stimuli on concurrent cognitive and affective processes in individuals with depression remains inadequately understood. This study aimed to investigate the effects of salient environmental stimuli, specifically emotional faces, on reward-associated processes in MDD. Methods: Thirty-three patients with recurrent MDD and thirty-two healthy controls (HC) matched for age, sex, and education were included in the study. We used a reward-related associative learning (RRAL) task primed with emotional (happy, sad, neutral) faces to investigate the effect of salient stimuli on reward-related learning and decision-making in functional magnetic resonance imaging (fMRI). Participants were instructed to ignore emotional faces during the task. The fMRI data were analyzed using a full-factorial general linear model (GLM) in Statistical Parametric Mapping (SPM12). Results: In depressed patients, cues primed with sad faces were associated with reduced amygdala activation. However, both HC and MDD group exhibited reduced ventral striatal activity while learning reward-related cues and receiving rewards. Limitations: The patients'medication usage was not standardized. Conclusions: This study underscores the functional alteration of the amygdala in response to cognitive tasks presented with negative emotionally salient stimuli in the environment of MDD patients. The observed alterations in amygdala activity suggest potential interconnected effects with other regions of the prefrontal cortex. Understanding the intricate neural connections and their disruptions in depression is crucial for unraveling the complex pathophysiology of the disorder. © 2024 Elsevier B.V.Article Citation - WoS: 40Citation - Scopus: 70Perspectives on the Gap Between the Software Industry and the Software Engineering Education(IEEE-Inst Electrical Electronics Engineers Inc, 2019) Oguz, Damla; Oguz, KayaThe gap between the software industry and software engineering education was first mentioned three decades ago, in 1989. Since then, its existence has been regularly reported on and solutions to close it have been proposed. However, after thirty years this gap resists all efforts for closure. In this study we assert that the gap between industry and academia exists for several reasons that are related and intertwined. To take a broader look at the problem from the perspective of all related entities, we (i) provide a detailed overview of the profession and identify the entities, (ii) extract the causes that stem from these entities and discuss what each entity should do, (iii) report and analyze the results of a questionnaire that has been conducted with students and recent graduates, (iv) emphasize the highlights of the interviews conducted with students, recent graduates and academics, (v) and compile a list of skills that are sought by the industry by analyzing the software engineering job advertisements. We further contribute to finding solutions by considering all entities involved, which provides an opportunity to access all of them, so that each can find out what they can do to acknowledge and narrow the gap. Our study concludes that the gap requires constant attention and hard work for all of the entities involved, and therefore all should be on the lookout for new technologies, learn to embrace the changes and adapt to them, so that the gap is kept at a minimum.Article Citation - WoS: 1Citation - Scopus: 1Robust Activation Detection Methods for Real-Time and Offline Fmri Analysis(Elsevier Ireland Ltd, 2017) Oguz, Kaya; Cinsdikici, Muhammed G.; Gonul, Ali SaffetWe propose two contributions with novel approaches to fMRI activation analysis. The first is to apply confidence intervals to locate activations in real-time, and second is a new metric based on robust regression of fMRI signals. These contributions are implemented in our four proposed methods; Instantaneous Activation Method (TAM), Instantaneous Activation Method with Past Blocks (TAMP) for real-time analysis, Task Robust Regression Distance Method (TRRD) for the new metric with robust regression and Instantaneous Robust Regression Distance Method (IRRD) for both contributions. For comparison, a statistical offline method called Task Activation Method (TAM) and a correlation analysis method are also implemented. The methods are initially evaluated with synthetic data generated using two different approaches; first using varying hemodynamic response function signals to simulate a wide range of stimuli responses, along with a Gaussian white noise, and second using no activity state data of a real fMRI experiment, which removes the need to generate noise. The methods are also tested with real fMRI experiments and compared with the results obtained by the widely used SPM tool. The results show that instantaneous methods reveal activations that are lost statistically in an offline analysis. They also reveal further improvements by robust fitting application, which minimizes the outlier effect. TRRD has an area under the ROC curve of 0,7127 for very noisy synthetic images, is reaching up to 0,9608 as the noise decreases, while the instantaneous score is in the range of 0,6124 to 0,8019 in the same noise levels. (C) 2017 Elsevier B.V. All rights reserved.Article Citation - Scopus: 1From Attributes To Communities: a Novel Approach in Social Network Generation(Peerj inc, 2024) Uludagli, Muhtar cagkan; Oguz, KayaGenerating networks with attributes would be useful in computer game development by enabling dynamic social interactions, adaptive storylines, realistic economic systems, ecosystem modelling, urban development, strategic planning, and adaptive learning systems. To this end, we propose the Attribute-based Realistic Community and Associate NEtwork (ARCANE) algorithm to generate node-attributed networks with functional communities. We have designed a numerical node attribute-edge relationship computation system to handle the edge generation phase of our network generator, which is a different method from our predecessors. We combine this system with the proximity between nodes to create more life-like communities. Our method is compared against other node-attributed social network generators in the area with using both different evaluation metrics and a real-world dataset. The model properties evaluation identified ARCANE as the leading generator, with another generator ranking in a tie for first place. As a more favorable outcome for our approach, the community detection evaluation indicated that ARCANE exhibited superior performance compared to other competing generators within this domain. This thorough evaluation of the resulting graphs show that the proposed method can be an alternate approach to social network generators with node attributes and communities.Article Citation - WoS: 23Citation - Scopus: 30Apal: Adjacency Propagation Algorithm for Overlapping Community Detection in Biological Networks(Elsevier Science Inc, 2021) Doluca, Osman; Oguz, KayaWe propose a novel method called Adjacency Propagation Algorithm (APAL) which considers the notion that the adjacent vertices are the best candidates for detecting overlapping communities in an undirected, unweighted, nontrivial graph. This is a compact algorithm with a single threshold parameter used to filter the detected communities according to their intraconnectivity property. In this study, APAL was tested rigorously using synthetic generators, such as the widely accepted LFR benchmark, as well as real data sets of yeast and human protein interactions networks. It was compared against the foremost algorithms in the field; the Clique Percolation Method (CPM), Community Overlap Propagation Algorithm (COPRA) and Neighbourhood-Inflated Seed Expansion (NISE). The results show that APAL outperforms its competitors for networks with increases in the number of memberships of the overlapping vertices. Such conditions are often found in biological networks, where a particular protein subunit may form part of several complexes. We believe that this shows the value of the implementation of APAL for protein interaction and other biological networks. (c) 2021 Elsevier Inc. All rights reserved.Conference Object Fmrı Deneylerinin Tasarlml, Yürütülmesi ve Analizi Design, Execution, And Analysis Of Fmrı Experiments(Institute of Electrical and Electronics Engineers Inc., 2019) Oguz K.Functional magnetic resonance imaging aims to correlate brain functions with the regions of the brain. To find out the region responsible for a brain function, the brain should be exposed to stimuli related to the function, and the changes in time should be monitored. To acquire images that change over time makes it more complex and difficult to extract deductions from functional images than structural images. To get the design right, it is necessary to ask the right questions, to give the correct stimuli at the right times, and to analyze the obtained data according to the design of the experiment. As a result, the design of the experiment affects the whole process. This paper presents a wide view of the complete process in the design, execution and the analysis of fMRI experiments, along with the related software and hardware requirements, as well as mentioning the challenges involved. © 2019 IEEE.Conference Object Citation - WoS: 4Emotion Recognition Using Neural Networks(World Scientific And Engineering Acad And Soc, 2009) Unluturk, Mehmet S.; Oguz, Kaya; Atay, CoskunSpeech and emotion recognition improve the quality of human computer interaction and allow more easy to use interfaces for every level of user in software applications. In this study, we have developed the emotion recognition neural network (ERNN) to classify the voice signals for emotion recognition. The ERNN has 128 input nodes, 20 hidden neurons, and three summing Output nodes. A set of 97932 training sets is used to train the ERNN. A new set of 24483 testing sets is utilized to test the EPNN performance. The samples tested for voice recognition are acquired from the movies Anger Management and Pick of Destiny. ERNN achieves an average recognition performance of 100%. This high level of recognition suggests that the ERNN is a promising method for emotion recognition in computer applications.Conference Object Citation - WoS: 5Citation - Scopus: 5Classification of Patients With Bipolar Disorder and Their Healthy Siblings From Healthy Controls Using Mri(Institute of Electrical and Electronics Engineers Inc., 2019) Cigdem O.; Horuz E.; Soyak R.; Aydeniz B.; Sulucay A.; Oguz K.; Demirel H.Detection of Bipolar Disease (BD), one of the most common neuroanatomical abnormalities, using machine learning algorithms together with Magnetic Resonance Imaging (MRI) data has been widely studied. BD is a highly heritable disease, yet not all siblings tend to have it despite they might have similar genetic and environmental risk factors. In this paper, the classifications of two self-acquired data groups, namely 26BD patients and 38 unrelated Healthy Controls (HCs) as well as 27 Healthy Siblings of BD (BDHSs) and 38HCs are examined. Voxel-Based Morphometry (VBM) is utilized to segment and pre-process the MRI data. In order to obtain the morphological alterations in the Gray Matter (GM) and White Matter (WM) of data groups separately, a general linear model is configured and a two sample t-test based statistical method is used. The obtained differentiated voxels are considered as Voxel of Interests (VOIs) and using VOIs reduces the dimension of the original data into the number of VOIs. The effects of using different covariates (i.e. total intracranial volume (TIV), age, and sex) on classification of the two data groups have been studied for GM-only, WM-only, and their combination. Principle Component Analysis (PCA) is used to reduce the dimension of the extracted VOIs data and Support Vector Machine (SVM) with Gaussian kernel is taken into account as a classifier. The experimental results indicate that among three covariates, TIV provides better results for both data groups and the classification accuracies of the combination of GM and WM maps is higher than that of GM-only and WM- only for both groups. In BD and HC comparison, the highest classification accuracies of 70.3% for GM, 79.7% for WM, and 82.8% for fusion of extracted GM as well as WM are obtained. In BDHS and HC comparison, the highest classification accuracies of 72.3% for GM, 76.9% for WM, and 78.5% for fusion of extracted GM as well as WM are obtained. © 2019 IEEE.Article Citation - WoS: 13Citation - Scopus: 30Non-player character decision-making in computer games(Springer, 2023) Uludağlı, Muhtar Çağkan; Oğuz, KayaOne of the most overlooked challenges in artificial intelligence (AI) for computer games is to create non-player game characters (NPCs) with human-like behavior. Modern NPCs determine their actions in different situations using certain decision-making methods, enabling them to change the current state of the game world. In this paper, we survey current decision-making methods used by NPCs in games, identifying five categories. We give detailed overview of these five categories and determine the previous studies that belong to each of these categories. We also discuss the hybrid methods which are the combinations of different decision-making methods and the frameworks that are created for NPC decision-making. As a result of this analysis, we create a taxonomy table based on these covered studies. Lastly, the challenges faced in our study and future possibilities for improvement are described.
