Bilgisayar Mühendisliği Bölümü

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Citation - WoS: 21
Citation - Scopus: 30
A Compact Multiband Printed Monopole Antenna With Hybrid Polarization Radiation for GPS, LTE, and Satellite Applications
(Ieee-inst Electrical Electronics Engineers inc, 2020) Al-Mihrab, Mohammed A.; Salim, Ali J.; Ali, Jawad K.
A new compact printed monopole antenna is presented in this paper. An open-loop hexagonal radiator excited by a microstrip feed line, which is printed on top of the substrate, which is FR4 type, while on another side, a partial ground plane is fixed and embedded with two pairs of slits as well as a pair of rectangular strips. Triple operating bands with two different polarization types are obtained. The lower band has right-hand circular polarization (RHCP) characteristic, whereas the upper band has left-hand circular polarization (LHCP) characteristic means that a dual-band dual-sense circular polarization (CP). Concerning the middle band, a linear polarization (LP) has been gotten in this antenna. Numerical analysis and experimental validation of the proposed antenna structure have been performed, and results are demonstrated. The measured impedance bandwidths (IBWs) are 14.7% (1.478-1.714 GHz), 6.8% (2.54-2.72 GHz), and 13.1% (4.29-4.89 GHz), respectively. The measured 3-dB axial ratio bandwidths (ARBWs) are 6.2% (1.510-1.606 GHz), and 22.7% (4.035-5.07 GHz) for the lower and the upper band, respectively. So, it's suitable for covering modern wireless applications such as GPS (Global Positioning System), LTE (Long Term Evaluation), and Satellite.
Citation - WoS: 56
Citation - Scopus: 83
A Deep Neural-Network Based Stock Trading System Based on Evolutionary Optimized Technical Analysis Parameters
(Elsevier Science Bv, 2017) Sezer, Omer Berat; Doğdu, Erdoğan; Ozbayoglu, Murat; Dogdu, Erdogan; 142876; Bilgisayar Mühendisliği
In this study, we propose a stock trading system based on optimized technical analysis parameters for creating buy-sell points using genetic algorithms. The model is developed utilizing Apache Spark big data platform. The optimized parameters are then passed to a deep MLP neural network for buy-sell-hold predictions. Dow 30 stocks are chosen for model validation. Each Dow stock is trained separately using daily close prices between 1996-2016 and tested between 2007-2016. The results indicate that optimizing the technical indicator parameters not only enhances the stock trading performance but also provides a model that might be used as an alternative to Buy and Hold and other standard technical analysis models. (c) 2017 The Authors. Published by Elsevier B.V.
Citation - WoS: 40
Citation - Scopus: 40
A density functional study of bare and hydrogenated platinum clusters
(Elsevier, 2006) Sebetci, Ali; 20965; İç Mimarlık
We perform density functional theory calculations using Gaussian atomic-orbital methods within the generalized gradient approximation for the exchange and correlation to study the interactions in the bare and hydrogenated platinum clusters. The minimum-energy structures, binding energies, relative stabilities. vibrational frequencies and the highest occupied and lowest unoccupied molecular-orbital gaps of PtnHm (n = 1-5, m = 0-2) clusters are calculated and compared with previously studied pure platinum and hydrogenated platinum clusters. We investigate any magic behavior in hydrogenated platinum clusters and find that Pt4H2 is snore stable than its neighboring sizes. The lowest energy structure of Pt-4 is found to be a distorted tetrahedron and that of Pt-5 found to be a bridge site capped tetrahedron which is a new global minimum for Pt-5 cluster. The successive addition of H atoms to Pt-n clusters leads to an oscillatory change in the magnetic moment of Pt-3-Pt-5 clusters. (c) 2006 Elsevier B.V. All rights reserved.
Citation - WoS: 0
Citation - Scopus: 2
A Discovery and Analysis Engine for Semantic Web
(Assoc Computing Machinery, 2018) Yumusak, Semih; Doğdu, Erdoğan; Kamilaris, Andreas; Dogdu, Erdogan; Kodaz, Halife; Uysal, Elif; Aras, Riza Emre; Bilgisayar Mühendisliği
The Semantic Web promotes common data formats and exchange protocols on the web towards better interoperability among systems and machines. Although Semantic Web technologies are being used to semantically annotate data and resources for easier reuse, the ad hoc discovery of these data sources remains an open issue. Popular Semantic Web endpoint repositories such as SPARQLES, Linking Open Data Project (LOD Cloud), and LODStats do not include recently published datasets and are not updated frequently by the publishers. Hence, there is a need for a web-based dynamic search engine that discovers these endpoints and datasets at frequent intervals. To address this need, a novel web meta-crawling method is proposed for discovering Linked Data sources on the Web. We implemented the method in a prototype system named SPARQL Endpoints Discovery (SpEnD). In this paper, we describe the design and implementation of SpEnD, together with an analysis and evaluation of its operation, in comparison to the aforementioned static endpoint repositories in terms of time performance, availability, and size. Findings indicate that SpEnD outperforms existing Linked Data resource discovery methods.
Citation - Scopus: 0
A Mobile Application Flow Representation for Mutual Understanding of It and Healthcare Professionals
(2013) Erturan, Y.N.; Tokdemir, Gül; Bilgen, S.; Tokdemir, G.; Cagiltay, N.E.; Yildiz, E.; Özcebe, E.; 17411; Bilgisayar Mühendisliği
Ever since mobile applications were developed and became popular, they have started to take part in almost every field of our lives. Healthcare is one of the most popular fields that mobile applications have become a part of. However, development of mobile healthcare applications requires an inter-disciplinary work on which people from different domains should communicate. To do so efficiently, mobile application instructions should be provided as clearly as possible so that mutual understanding can be achieved. This study, aims to provide a methodology to provide the common grounds for healthcare and IT specialists so that to improve the satisfaction level of all the stakeholders of the system from the provided IT services and the end-user interfaces. In other words, by providing a better communication medium for the stakeholders during the design phase, we believe that software development process will be improved, so do their satisfaction from the developed system. © 2013 Springer-Verlag.
Citation - WoS: 9
Citation - Scopus: 11
A Pairwise Deep Ranking Model for Relative Assessment of Parkinson's Disease Patients from Gait Signals
(Ieee-inst Electrical Electronics Engineers inc, 2022) Ogul, Burcin Buket; Ozdemir, Suat
Continuous monitoring of the symptoms is crucial to improve the quality of life for patients with Parkinson's Disease (PD). Thus, it is necessary to objectively assess the PD symptoms. Since manual assessment is subjective and prone to misinterpretation, computer-aided methods that use sensory measurements have recently been used to make objective PD assessment. Current methods follow an absolute assessment strategy, where the symptoms are classified into known categories or quantified with exact values. These methods are usually difficult to generalize and considered to be unreliable in practice. In this paper, we formulate the PD assessment problem as a relative assessment of one patient compared to another. For this assessment, we propose a new approach to the comparative analysis of gait signals obtained via foot-worn sensors. We introduce a novel pairwise deep-ranking model that is fed by data from a pair of patients, where the data is obtained from multiple ground reaction force sensors. The proposed model, called Ranking by Siamese Recurrent Network with Attention, takes two multivariate time-series as inputs and produces a probability of the first signal having a higher continuous attribute than the second one. In ten-fold cross-validation, the accuracy of pairwise ranking predictions can reach up to 82% with an AUROC of 0.89. The model outperforms the previous methods for PD monitoring when run in the same experimental setup. To the best of our knowledge, this is the first study that attempts to relatively assess PD patients using a pairwise ranking measure on sensory data. The model can serve as a complementary model to computer-aided prognosis tools by monitoring the progress of the patient during the applied treatment.
Citation - WoS: 9
Citation - Scopus: 15
A serious game for improving the decision making skills and knowledge levels of Turkish football referees according to the laws of the game
(Springer international Publishing Ag, 2016) Güleç, Ulaş; Gulec, Ulas; Yilmaz, Murat; Yılmaz, Murat; 47439; Bilgisayar Mühendisliği; Yazılım Mühendisliği
Digital game-based learning environments provide emerging opportunities to overcome learning barriers by combining newly developed technologies and traditional game design. This study proposes a quantitative research approach supported by expert validation interviews to designing a game-based learning framework. The goal is to improve the learning experience and decision-making skills of soccer referees in Turkey. A serious game was developed and tested on a group of referees (N = 54). The assessment results of these referees were compared with two sample t-test and the Wilcoxon signed-ranked test for both the experimental group and the control group. The findings of the current study confirmed that a game-based learning environment has greater merit over the paper-based alternatives.
Citation - WoS: 3
Citation - Scopus: 6
A shallow 3D convolutional neural network for violence detection in videos
(Cairo Univ, Fac Computers & information, 2024) Dündar, Naz; Dundar, Naz; Keceli, Ali Seydi; Sever, Hayri; Kaya, Aydin; Sever, Hayri; 366608; 11916; Yazılım Mühendisliği; Bilgisayar Mühendisliği
With the recent worldwide statistical rise in the amount of public violence, automated violence detection in surveillance cameras has become a matter of high importance. This work introduces an end-to-end, trainable 3D Convolutional Neural Network (3D CNN) for detecting violence in video footage. The proposed network is inherently capable of processing both spatial and temporal information, thereby obviating the need for additional models that would introduce higher computational requirements and complexity. This work has two main contributions: 1) developing a lightweight 3D CNN suitable for inference on edge devices as mobile systems, and 2) a comprehensive explanation of all components comprising a CNN model, thereby enhances model interpretability. Experiments were conducted to assess the performance of the proposed model using a consolidated dataset combining four benchmark datasets. The results of the experiments support the asserted contributions, which are discussed in detail.
Citation - WoS: 13
Citation - Scopus: 16
A validated active contour method driven by parabolic arc model for detection and segmentation of mitochondria
(Academic Press inc Elsevier Science, 2016) Taşel, Faris Serdar; Tasel, Serdar F.; Mumcuoglu, Erkan U.; Hassanpour, Reza; Hassanpour, Reza Z.; Perkins, Guy; Bilgisayar Mühendisliği; Yazılım Mühendisliği
Recent studies reveal that mitochondria take substantial responsibility in cellular functions that are closely related to aging diseases caused by degeneration of neurons. These studies emphasize that the membrane and crista morphology of a mitochondrion should receive attention in order to investigate the link between mitochondria] function and its physical structure. Electron microscope tomography (EMT) allows analysis of the inner structures of mitochondria by providing highly detailed visual data from large volumes. Computerized segmentation of mitochondria with minimum manual effort is essential to accelerate the study of mitochondrial structure/function relationships. In this work, we improved and extended our previous attempts to detect and segment mitochondria from transmission electron microcopy (TEM) images. A parabolic arc model was utilized to extract membrane structures. Then, curve energy based active contours were employed to obtain roughly outlined candidate mitochondrial regions. Finally, a validation process was applied to obtain the final segmentation data. 3D extension of the algorithm is also presented in this paper. Our method achieved an average F-score performance of 0.84. Average Dice Similarity Coefficient and boundary error were measured as 0.87 and 14 nm respectively. (C) 2016 Elsevier Inc. All rights reserved.
Citation - WoS: 29
Citation - Scopus: 50
An Artificial Neural Network-Based Stock Trading System Using Technical Analysis And Big Data Framework
(Assoc Computing Machinery, 2017) Sezer, Omer Berat; Doğdu, Erdoğan; Ozbayoglu, A. Murat; Dogdu, Erdogan; Bilgisayar Mühendisliği
In this paper, a neural network-based stock price prediction and trading system using technical analysis indicators is presented. The model developed first converts the financial time series data into a series of buy-sell-hold trigger signals using the most commonly preferred technical analysis indicators. Then, a Multilayer Perceptron (MLP) artificial neural network (ANN) model is trained in the learning stage on the daily stock prices between 1997 and 2007 for all of the Dow30 stocks. Apache Spark big data framework is used in the training stage. The trained model is then tested with data from 2007 to 2017. The results indicate that by choosing the most appropriate technical indicators, the neural network model can achieve comparable results against the Buy and Hold strategy in most of the cases. Furthermore, fine tuning the technical indicators and/or optimization strategy can enhance the overall trading performance.
Citation - WoS: 69
Citation - Scopus: 98
An examination of personality traits and how they impact on software development teams
(Elsevier, 2017) Yilmaz, Murat; Yılmaz, Murat; O'Connor, Rory V.; Colomo-Palacios, Ricardo; Clarke, Paul; 55248; Yazılım Mühendisliği
Context Research has shown that a significant number of software projects fail due to social issues such as team or personality conflicts. However, only a limited number of empirical studies have been undertaken to understand the impact of individuals' personalities on software team configurations. These studies suffer from an important limitation as they lack a systematic and rigorous method to relate personality traits of software practitioners and software team structures. Objective: Based on an interactive personality profiling approach, the goal of this study is to reveal the personality traits of software practitioners with an aim to explore effective software team structures. Method: To explore the importance of individuals' personalities on software teams, we employed a two-step empirical approach. Firstly, to assess the personality traits of software practitioners, we developed a context-specific survey instrument, which was conducted on 216 participants from a middle-sized soft ware company. Secondly, we propose a novel team personality illustration method to visualize team structures. Results: Study results indicated that effective team structures support teams with higher emotional stability, agreeableness, extroversion, and conscientiousness personality traits. Conclusion: Furthermore, empirical results of the current study show that extroversion trait was more predominant than previously suggested in the literature, which was especially more observable among agile software development teams. (C) 2017 Elsevier B.V. All rights reserved.
An Exploratory Study to Assess Digital Map Zoom/Pan/Rotate Methods with HoloLens
(2018) Yılmaz, Murat; Yılmaz, Murat; Yazılım Mühendisliği
Geographical map display plays an important part of a GIS (GeographicalInformation System). The usability of a map display is certainly depends on how easilyuser navigates through spatial data and selects features on it. Currently, desktop computerbased GIS applications uses mouse movements, buttons and scroll for a set of functionssuch as zoom, pan and rotate. However currently Hololens supports only gaze, air tapgesture and voice commands as an input. Although the functionality looks simple, it ischallenging to find optimal solution by using input devices for those functions even whilecreating in a desktop application. This study aims to assess an optimal way to enable thesefunctions on hologram maps by investigating its validity and usability.
Citation - WoS: 0
Citation - Scopus: 0
Analysing Iraqi Railways Network by Applying Specific Criteria Using the GIS Techniques
(Coll Science Women, Univ Baghdad, 2019) Naji, Hayder Fans; Maras, H. Hakan; 34410
The railways network is one of the huge infrastructure projects. Therefore, dealing with these projects such as analyzing and developing should be done using appropriate tools, i.e. GIS tools. Because, traditional methods will consume resources, time, money and the results maybe not accurate. In this research, the train stations in all of Iraq's provinces were studied and analyzed using network analysis, which is one of the most powerful techniques within GIS. A free trial copy of ArcGIS (R) 10.2 software was used in this research in order to achieve the aim of this study. The analysis of current train stations has been done depending on the road network, because people used roads to reach those train stations. The data layers for this study were collected and prepared to meet the requirements of network analyses within GIS. In this study, the current train stations in Iraq were analyzed and studied depending on accessibility value for those stations. Also, to know the numbers of people who can reach those stations within a walking time of 20 minutes. So, this study aims to analyze the current train stations according to multiple criteria by using network analysis in order to find the serviced areas around those stations. Results will be presented as digital maps layers with their attribute tables that show the beneficiaries from those train stations and serviced areas around those stations depending on specific criteria, with a view to determine the size of this problem and to support the decision makers in case of locating new train stations within the best locations for it.
Citation - WoS: 8
Citation - Scopus: 8
Application of BiLSTM-CRF model with different embeddings for product name extraction in unstructured Turkish text
(Springer London Ltd, 2024) Arslan, Serdar; Arslan, Serdar; 325411; Bilgisayar Mühendisliği
Named entity recognition (NER) plays a pivotal role in Natural Language Processing by identifying and classifying entities within textual data. While NER methodologies have seen significant advancements, driven by pretrained word embeddings and deep neural networks, the majority of these studies have focused on text with well-defined grammar and structure. A significant research gap exists concerning NER in informal or unstructured text, where traditional grammar rules and sentence structure are absent. This research addresses this crucial gap by focusing on the detection of product names within unstructured Turkish text. To accomplish this, we propose a deep learning-based NER model which combines a Bidirectional Long Short-Term Memory (BiLSTM) architecture with a Conditional Random Field (CRF) layer, further enhanced by FastText embeddings. To comprehensively evaluate and compare our model's performance, we explore different embedding approaches, including Word2Vec and Glove, in conjunction with the Bidirectional Long Short-Term Memory and Conditional Random Field (BiLSTM-CRF) model. Furthermore, we conduct comparisons against BERT to assess the efficacy of our approach. Our experimentation utilizes a Turkish e-commerce dataset gathered from the internet, where traditional grammatical and structural rules may not apply. The BiLSTM-CRF model with FastText embeddings achieved an F1 score value of 57.40%, a precision value of 55.78%, and a recall value of 59.12%. These results indicate promising performance in outperforming other baseline techniques. This research contributes to the field of NER by addressing the unique challenges posed by unstructured Turkish text and opens avenues for improved entity recognition in informal language settings, with potential applications across various domains.
Citation - WoS: 6
Citation - Scopus: 6
Auction-Based Serious Game for Bug Tracking
(Wiley, 2019) Usfekes, Cagdas; Tuzun, Eray; Yilmaz, Murat; Macit, Yagup; Clarke, Paul
Today, one of the challenges in software engineering is utilising application lifecycle management (ALM) tools effectively in software development. In particular, it is hard for software developers to engage with the work items that are appointed to themselves in these ALM tools. In this study, the authors have focused on bug tracking in ALM where one of the most important metrics is mean time to resolution that is the average time to fix a reported bug. To improve this metric, they developed a serious game application based on an auction-based reward mechanism. The ultimate aim of this approach is to create an incentive structure for software practitioners to find and resolved bugs that are auctioned where participants are encouraged to solve and test more bugs in less time and improve quality of software development in a competitive environment. They conduct hypothesis tests by performing a Monte Carlo simulation. The preliminary results of this research support the idea that using a gamification approach for an issue tracking system enhances the productivity and decreases mean time to resolution.
Citation - WoS: 0
Citation - Scopus: 0
Auction-based serious game for bug tracking
(Mdpi, 2019) Marin, Marin; Yılmaz, Murat; Baleanu, Dumitru; Vlase, Sorin; Yazılım Mühendisliği; Matematik
Engineering practice requires the use of structures containing identical components or parts, which are useful from several points of view: less information is needed to describe the system, design is made quicker and easier, components are made faster than a complex assembly, and finally the time to achieve the structure and the cost of manufacturing decreases. Additionally, the subsequent maintenance of the system becomes easier and cheaper. This Special Issue is dedicated to this kind of mechanical structure, describing the properties and methods of analysis of these structures. Discrete or continuous structures in static and dynamic cases are considered. Theoretical models, mathematical methods, and numerical analysis of the systems, such as the finite element method and experimental methods, are expected to be used in the research. Such applications can be used in most engineering fields including machine building, automotive, aerospace, and civil engineering.
Author identification for Turkish texts
(Çankaya Üniversitesi, 2007) Görür, Abdül Kadir; Görür, Abdül Kadir; Bilgisayar Mühendisliği
The main concern of author identification is to define an appropriate characterization of documents that captures the writing style of authors. The most important approaches to computer-based author identification are exclusively based on lexical measures. In this paper we presented a fully automated approach to the identification of the authorship of unrestricted text by adapting a set of style markers to the analysis of the text. In this study, 35 style markers were applied to each author. By using our method, the author of a text can be identified by using the style markers that characterize a group of authors. The author group consists of 20 different writers. Author features including style markers were derived together with different machine learning algorithms. By using our method we have obtained a success rate of 80% in avarege
Citation - WoS: 32
Citation - Scopus: 43
Automated Classification of Rheumatoid Arthritis, Osteoarthritis, and Normal Hand Radiographs with Deep Learning Methods
(Springer, 2022) Ureten, Kemal; Maraş, Hadi Hakan; Maras, Hadi Hakan; 34410; Bilgisayar Mühendisliği
Rheumatoid arthritis and hand osteoarthritis are two different arthritis that causes pain, function limitation, and permanent joint damage in the hands. Plain hand radiographs are the most commonly used imaging methods for the diagnosis, differential diagnosis, and monitoring of rheumatoid arthritis and osteoarthritis. In this retrospective study, the You Only Look Once (YOLO) algorithm was used to obtain hand images from original radiographs without data loss, and classification was made by applying transfer learning with a pre-trained VGG-16 network. The data augmentation method was applied during training. The results of the study were evaluated with performance metrics such as accuracy, sensitivity, specificity, and precision calculated from the confusion matrix, and AUC (area under the ROC curve) calculated from ROC (receiver operating characteristic) curve. In the classification of rheumatoid arthritis and normal hand radiographs, 90.7%, 92.6%, 88.7%, 89.3%, and 0.97 accuracy, sensitivity, specificity, precision, and AUC results, respectively, and in the classification of osteoarthritis and normal hand radiographs, 90.8%, 91.4%, 90.2%, 91.4%, and 0.96 accuracy, sensitivity, specificity, precision, and AUC results were obtained, respectively. In the classification of rheumatoid arthritis, osteoarthritis, and normal hand radiographs, an 80.6% accuracy result was obtained. In this study, to develop an end-to-end computerized method, the YOLOv4 algorithm was used for object detection, and a pre-trained VGG-16 network was used for the classification of hand radiographs. This computer-aided diagnosis method can assist clinicians in interpreting hand radiographs, especially in rheumatoid arthritis and osteoarthritis.
Citation - WoS: 3
Citation - Scopus: 4
Automatic Coastline Detection Using Image Enhancement and Segmentation Algorithms
(Hard, 2016) Maras, Erdem Emin; Maraş, Hadi Hakan; Caniberk, Mustafa; Maras, Hadi Hakan; 34410; Bilgisayar Mühendisliği
Coastlines have hosted numerous civilizations since the earliest times of mankind due to the advantages they offer such as natural resources, transportation, arable areas, seafood, trade, and biodiversity. Coastal regions should be monitored vigilantly by planners and control mechanisms, and any changes in these regions should be detected with its human or natural origin, and future plans and possible interventions should be formed in these aspects to maintain ecological balance, sustainable development, and planned urbanization. Integrated coastal zone management (ICZM) provides an important tool to reach that goal. One of the important elements of ICZM is the detection of coastlines. While there are several methods to detect coastlines, remote sensing methods provide the fastest and the most efficient solutions. In this study, color infrared, grayscale, RGB, and fake infrared images were processed with the median filtering and segmentation software developed within the study, and coastal lines were detected by the edge detection method. The results show that segmentation with fake infrared images derived from RGB images give the best results.
Behavior Analysis Of Routing Protocols For A Health Decision Support System
(2014) Alyeksyeyenkov, Yuriy; Abdullah, Mohammed Najm; Tareq Nafea, Mustafa; Bilgisayar Mühendisliği
Mobile ad-hoc network (MANET) is an infrastructure less network, that is a collection of mobile devices connected together without centralized infrastructure that can be configured at any time and any where, it gives the network dynamic topology. The most important thing in MANETs is a routing protocol. MANETs have a three major routing protocols proactive, reactive and hybrid. In this work, the performance of reactive routing protocol Ad hoc on demand Distance Vector (AODV) and proactive routing protocol Destination Sequenced Distance Vector (DSDV) for a health decision support system (HDSS) were evaluated. The major goal of this work is to analyze the performance of well-known MANETs routing protocol in mobility case under low, medium and high density scenario. Hence it becomes important to study the performance of these routing protocols. The performance is analyzed with respect to Average End-to-End Delay, drop packets, Packet Delivery ratio (PDR) and Throughput. Simulation results verify that AODV gives better performance as compared to DSDV.

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