Bilgisayar Mühendisliği Bölümü Yayın Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/253

Browse

Now showing 1 - 20 of 40

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: 3
Citation - Scopus: 4
Block Size Analysis for Discrete Wavelet Watermarking and Embedding a Vector Image as a Watermark
(Zarka Private Univ, 2019) Sever, Hayri; Sever, Hayri; Senol, Ahmet; Elbasi, Ersin; Bilgisayar Mühendisliği
As telecommunication and computer technologies proliferate, most data are stored and transferred in digital format. Content owners, therefore, are searching for new technologies to protect copyrighted products in digital form. Image watermarking emerged as a technique for protecting image copyrights. Early studies on image watermarking used the pixel domain whereas modern watermarking methods convert a pixel based image to another domain and embed a watermark in the transform domain. This study aims to use, Block Discrete Wavelet Transform (BDWT) as the transform domain for embedding and extracting watermarks. This study consists of 2 parts. The first part investigates the effect of dividing an image into non overlapping blocks and transforming each image block to a DWT domain, independently. Then, effect of block size on watermark success and, how it is related to block size, are analyzed. The second part investigates embedding a vector image logo as a watermark. Vector images consist of geometric objects such as lines, circles and splines. Unlike pixel-based images, vector images do not lose quality due to scaling. Vector watermarks deteriorate very easily if the watermarked image is processed, such as compression or filtering. Special care must be taken when the embedded watermark is a vector image, such as adjusting the watermark strength or distributing the watermark data into the image. The relative importance of watermark data must be taken into account. To the best of our knowledge this study is the first to use a vector image as a watermark embedded in a host image.
Citation - WoS: 8
Citation - Scopus: 8
Comparative Analysis on Wavelet-Based Detection of Finite Duration Low-Amplitude Signals Related To Ventricular Late Potentials
(Iop Publishing Ltd, 2004) Mousa, A; Yilmaz, A
Ventricular late potentials (VLPs) are considered as a noninvasive marker of patients with myocardial infarction, who are prone to the development of ventricular tachycardia. This paper investigates the effects of variations in physical properties of myocardial infarcts in terms of their effects on the parametric variations in VLP analysis. A sufficiently large set of signals underlining the behavior of physical parameters was employed to represent the effect of physical size, position, orientation and type of infarct. The approximated signals are variations from real electrocardiography signals by adding potentials representing late potentials based on duration, frequency, amplitude and position. The aim is not to exactly model VLP but rather to generate an approximate set of signals to examine the performance of the standard methods for different possibilities in infarct dynamics. We investigate some of the detection approaches together with their related assumptions, and try to pinpoint the drawbacks and inaccuracies of these methods and also their assumptions. The three widely accepted criteria-QRS duration, root-mean-square and duration of the signal at the end of QRS for VLP detection-were used in the investigation. Results from the application of these parameters to the set of signals are presented. In addition we investigate the physical nature of an infarct and list a number of possible reasons that might be the cause of a low success rate for the detection of additive potentials. To improve the performance of the common methods, two more wavelet transform parameters are added to those of the standard methods. The method derived from this analysis is presented as an alternative means for the detection of late signals named as delayed potentials, a more general class that includes VLP as a subset.
Citation - WoS: 21
Citation - Scopus: 22
Computerized Detection and Segmentation of Mitochondria on Electron Microscope Images
(Wiley, 2012) Tasel, S. F.; Perkins, G.; Martone, M. E.; Gurcan, M. N.; Mumcuoglu, E. U.; Hassanpour, R.
Mitochondrial function plays an important role in the regulation of cellular life and death, including disease states. Disturbance in mitochondrial function and distribution can be accompanied by significant morphological alterations. Electron microscopy tomography (EMT) is a powerful technique to study the 3D structure of mitochondria, but the automatic detection and segmentation of mitochondria in EMT volumes has been challenging due to the presence of subcellular structures and imaging artifacts. Therefore, the interpretation, measurement and analysis of mitochondrial distribution and features have been time consuming, and development of specialized software tools is very important for high-throughput analyses needed to expedite the myriad studies on cellular events. Typically, mitochondrial EMT volumes are segmented manually using special software tools. Automatic contour extraction on large images with multiple mitochondria and many other subcellular structures is still an unaddressed problem. The purpose of this work is to develop computer algorithms to detect and segment both fully and partially seen mitochondria on electron microscopy images. The detection method relies on mitochondria's approximately elliptical shape and double membrane boundary. Initial detection results are first refined using active contours. Then, our seed point selection method automatically selects reliable seed points along the contour, and segmentation is finalized by automatically incorporating a live-wire graph search algorithm between these seed points. In our evaluations on four images containing multiple mitochondria, 52 ellipses are detected among which 42 are true and 10 are false detections. After false ellipses are eliminated manually, 14 out of 15 fully seen mitochondria and 4 out of 7 partially seen mitochondria are successfully detected. When compared with the segmentation of a trained reader, 91% Dice similarity coefficient was achieved with an average 4.9 nm boundary error.
Citation - WoS: 22
Citation - Scopus: 24
Deep Learning Methods in the Diagnosis of Sacroiliitis From Plain Pelvic Radiographs
(Oxford Univ Press, 2023) Ureten, Kemal; Maras, Yuksel; Duran, Semra; Gok, Kevser
Objectives The aim of this study is to develop a computer-aided diagnosis method to assist physicians in evaluating sacroiliac radiographs. Methods Convolutional neural networks, a deep learning method, were used in this retrospective study. Transfer learning was implemented with pre-trained VGG-16, ResNet-101 and Inception-v3 networks. Normal pelvic radiographs (n = 290) and pelvic radiographs with sacroiliitis (n = 295) were used for the training of networks. Results The training results were evaluated with the criteria of accuracy, sensitivity, specificity and precision calculated from the confusion matrix and AUC (area under the ROC curve) calculated from ROC (receiver operating characteristic) curve. Pre-trained VGG-16 model revealed accuracy, sensitivity, specificity, precision and AUC figures of 89.9%, 90.9%, 88.9%, 88.9% and 0.96 with test images, respectively. These results were 84.3%, 91.9%, 78.8%, 75.6 and 0.92 with pre-trained ResNet-101, and 82.0%, 79.6%, 85.0%, 86.7% and 0.90 with pre-trained inception-v3, respectively. Conclusions Successful results were obtained with all three models in this study where transfer learning was applied with pre-trained VGG-16, ResNet-101 and Inception-v3 networks. This method can assist clinicians in the diagnosis of sacroiliitis, provide them with a second objective interpretation and also reduce the need for advanced imaging methods such as magnetic resonance imaging.
Citation - WoS: 56
Citation - Scopus: 88
A Deep Neural-Network Based Stock Trading System Based on Evolutionary Optimized Technical Analysis Parameters
(Elsevier Science Bv, 2017) Ozbayoglu, Murat; Dogdu, Erdogan; Sezer, Omer Berat
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: 42
Citation - Scopus: 42
A Density Functional Study of Bare and Hydrogenated Platinum Clusters
(Elsevier, 2006) Sebetci, Ali
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: 42
Citation - Scopus: 49
Detection of Hip Osteoarthritis by Using Plain Pelvic Radiographs With Deep Learning Methods
(Springer, 2020) Ureten, Kemal; Arslan, Tayfun; Gultekin, Korcan Emre; Demir, Ayse Nur Demirgoz; Ozer, Hafsa Feyza; Bilgili, Yasemin
Objective The incidence of osteoarthritis is gradually increasing in public due to aging and increase in obesity. Various imaging methods are used in the diagnosis of hip osteoarthritis, and plain pelvic radiography is the first preferred imaging method in the diagnosis of hip osteoarthritis. In this study, we aimed to develop a computer-aided diagnosis method that will help physicians for the diagnosis of hip osteoarthritis by interpreting plain pelvic radiographs. Materials and methods In this retrospective study, convolutional neural networks were used and transfer learning was applied with the pre-trained VGG-16 network. Our dataset consisted of 221 normal hip radiographs and 213 hip radiographs with osteoarthritis. In this study, the training of the network was performed using a total of 426 hip osteoarthritis images and a total of 442 normal pelvic images obtained by flipping the raw data set. Results Training results were evaluated with performance metrics such as accuracy, sensitivity, specificity, and precision calculated by using the confusion matrix. We achieved accuracy, sensitivity, specificity and precision results at 90.2%, 97.6%, 83.0%, and 84.7% respectively. Conclusion We achieved promising results with this computer-aided diagnosis method that we tried to develop using convolutional neural networks based on transfer learning. This method can help clinicians for the diagnosis of hip osteoarthritis while interpreting plain pelvic radiographs, also provides assistance for a second objective interpretation. It may also reduce the need for advanced imaging methods in the diagnosis of hip osteoarthritis.
Citation - WoS: 107
Citation - Scopus: 111
Determination of Complete Melting and Surface Premelting Points of Silver Nanoparticles by Molecular Dynamics Simulation
(Amer Chemical Soc, 2013) Yavuz, M.; Zhou, Y.; Alarifi, H. A.; Atis, M.; Ozdogan, C.; Hu, A.
A molecular dynamics simulation based on the embedded-atom method was conducted at different sizes of single-crystal Ag nanoparticles (NPs) with diameters of 4 to 20 nm to find complete melting and surface premelting points. Unlike the previous theoretical models, our model can predict both complete melting and surface premelting points for a wider size range of NPs. Programmed heating at an equal rate was applied to all sizes of NPs. Melting kinetics showed three different trends that are, respectively, associated with NPs in the size ranges of 4 to 7 rim, 8 to 10 nm, and 12 to 20 nm. NPs in the first range melted at a single temperature without passing through a surface premelting stage. Melting of the second range started by forming a quasi-liquid layer that expanded to the core, followed by the formation of a liquid layer of 1.8 nm thickness that also subsequently expanded to the core with increasing temperature and completed the melting process. For particles in the third range, the 1.8 nm liquid layer was formed once the thickness of the quasi-liquid layer reached S rim. The liquid layer expanded to the core and formed thicker stable liquid layers as the temperature increased toward the complete melting point. The ratio of the quasi-liquid layer thickness to the NP radius showed a linear relationship with temperature.
Citation - WoS: 8
Citation - Scopus: 11
The Diagnosis of Femoroacetabular Impingement Can Be Made on Pelvis Radiographs Using Deep Learning Methods
(Turkish Joint Diseases Foundation, 2023) Atalar, Ebru; Ureten, Kemal; Kanatli, Ulunay; Ciceklidag, Murat; Kaya, Ibrahim; Vural, Abdurrahman; Maras, Yuksel
Objectives: The aim of this study was to evaluate diagnostic ability of deep learning models, particularly convolutional neural network models used for image classification, for femoroacetabular impingement (FAI) using hip radiographs. Materials and methods: Between January 2010 and December 2020, pelvic radiographs of a total of 516 patients (270 males, 246 females; mean age: 39.1 +/- 3.8 years; range, 20 to 78 years) with hip pain were retrospectively analyzed. Based on inclusion and exclusion criteria, a total of 888 hip radiographs (308 diagnosed with FAI and 508 considered normal) were evaluated using deep learning methods. Pre-trained VGG-16, ResNet-101, MobileNetV2, and Inceptionv3 models were used for transfer learning. Results: As assessed by performance measures such as accuracy, sensitivity, specificity, precision, F-1 score, and area under the curve (AUC), the VGG-16 model outperformed other pre-trained networks in diagnosing FAI. With the pre-trained VGG-16 model, the results showed 86.6% accuracy, 82.5% sensitivity, 89.6% specificity, 85.5% precision, 83.9% F1 score, and 0.92 AUC. Conclusion: In patients with suspected FAI, pelvic radiography is the first imaging method to be applied, and deep learning methods can help in the diagnosis of this syndrome.
Citation - WoS: 11
Citation - Scopus: 11
Diagnosis of Osteoarthritic Changes, Loss of Cervical Lordosis, and Disc Space Narrowing on Cervical Radiographs With Deep Learning Methods
(Turkish Joint Diseases Foundation, 2022) Tokdemir, Gul; Ureten, Kemal; Atalar, Ebru; Duran, Semra; Maras, Hakan; Maras, Yuksel
Objectives: In this study, we aimed to differentiate normal cervical graphs and graphs of diseases that cause mechanical neck pain by using deep convolutional neural networks (DCNN) technology. Materials and methods: In this retrospective study, the convolutional neural networks were used and transfer learning method was applied with the pre-trained VGG-16, VGG-19, Resnet-101, and DenseNet-201 networks. Our data set consisted of 161 normal lateral cervical radiographs and 170 lateral cervical radiographs with osteoarthritis and cervical degenerative disc disease. Results: We compared the performances of the classification models in terms of performance metrics such as accuracy,
Citation - WoS: 2
The Effect of Population and Tourism Factors on Covid-19 Cases in Italy: Visual Data Analysis and Forecasting Approach
(Wiley, 2022) Ozyer, Baris; Ozyer, Gulsah Tumuklu; Tokdemir, Gul; Uguz, Sezer; Yaganoglu, Mete
At the beginning of 2020, the new coronavirus disease (Covid-19), a deadly viral illness, is declared as a public health emergency situation by WHO. Consequently, it is accepted as pandemic that affected millions of people worldwide. Italy is one of the most affected countries by Covid-19 disease among the world. In this article, our main goal is to investigate the effect of intensity of Covid-19 cases based on the population size and tourism factors in certain regions of Italy by visual data analysis. The regions of Lombardia, Veneto, Campania, Emilia-Romagna, Piemonte are the top five regions covering 58.50% of the total Covid-19 cases diagnosed in Italy. It has been shown by visual data analysis that population and tourism factors play an important role in the spread of Covid-19 cases in these five regions. In addition, a prediction model was created using Bi-LSTM and ARIMA algorithms to forecast the number of Covid-19 cases occurring in these five regions in order to take early action. We can conclude that these northern regions have been affected mostly by Covid-19 and the distribution of the resident population and tourist flow factors affected the number of Covid-19 cases in Italy.
Citation - WoS: 5
Citation - Scopus: 6
Exploring the Belief Systems of Software Development Professionals
(Taylor & Francis inc, 2015) O'Connor, Rory V.; Yilmaz, Murat
It is commonly accepted that an individual's beliefs and actions are based on his or her assessment and perceptions of the world. In order to determine what practices an individual is likely to follow at any given time, it is necessary to understand the individual's behavioral intention in a given circumstance. From an Information Technology perspective, a software development professional's belief systems are potentially the basis for the adoption and implementation of new and innovative work practices and processes. In this article, we explore the belief systems of software development professionals in order to understand the beliefs underlying intention and practice, and we seek answers about how they adopt or reject new and innovative software development processes and practices. The results point out a strong influence of past experiences, personality types, and repeated behavior on current software development processes and practices in industrial settings.
Citation - WoS: 7
Citation - Scopus: 10
Extending a Sentiment Lexicon With Synonym-Antonym Datasets: Swnettr Plus
(Tubitak Scientific & Technological Research Council Turkey, 2019) Genc, Burkay; Sever, Hayri; Saglam, Fatih
In our previous studies on developing a general-purpose Turkish sentiment lexicon, we constructed SWNetTR-PLUS, a sentiment lexicon of 37K words. In this paper, we show how to use Turkish synonym and antonym word pairs to extend SWNetTR-PLUS by almost 33% to obtain SWNetTR++, a Turkish sentiment lexicon of 49K words. The extension was done by transferring the problem into the graph domain, where nodes are words, and edges are synonym- antonym relations between words, and propagating the existing tone and polarity scores to the newly added words using an algorithm we have developed. We tested the existing and new lexicons using a manually labeled Turkish news media corpus of 500 news texts. The results show that our method yielded a significantly more accurate lexicon than SWNetTR-PLUS, resulting in an accuracy increase from 72.2% to 80.4%. At this level, we have now maximized the accuracy rates of translation-based sentiment analysis approaches, which first translate a Turkish text to English and then do the analysis using English sentiment lexicons.
Citation - WoS: 3
Citation - Scopus: 3
Finite Size Scaling by Using Scaling Functions in Two-Dimensional Q=2 and 7 State Potts Models
(World Scientific Publ Co Pte Ltd, 2001) Seferoglu, N; Aydin, M; Gündüç, Y; Demirtürk, S
The scaling behaviors of the percolation cumulant and the surface renormalization are studied on q = 2 and 7 state Potts models. The results show that the scaling functions can be safely used to determine infinite lattice transition points and the thermal and magnetic exponents indicating that these functions have very small correction to scaling contributions.
Citation - WoS: 22
Citation - Scopus: 21
Functionalizing Graphene by Embedded Boron Clusters
(Iop Publishing Ltd, 2008) Ozdogan, Cem; Kunstmann, Jens; Fehske, Holger; Quandt, Alexander
We present a model system that might serve as a blueprint for the controlled layout of graphene based nanodevices. The systems consists of chains of B-7 clusters implanted in a graphene matrix, where the boron clusters are not directly connected. We show that the graphene matrix easily accepts these alternating B-7-C-6 chains and that the implanted boron components may dramatically modify the electronic properties of graphene based nanomaterials. This suggests a functionalization of graphene nanomaterials, where the semiconducting properties might be supplemented by parts of the graphene matrix itself, but the basic wiring will be provided by alternating chains of implanted boron clusters that connect these areas.
Citation - WoS: 2
Citation - Scopus: 2
Guest Editorial: Gamification and Persuasive Games for Software Engineering
(inst Engineering Technology-iet, 2019) O'Connor, Rory V.; Colomo-Palacios, Ricardo; Clarke, Paul; Yilmaz, Murat
Citation - WoS: 6
Citation - Scopus: 7
Illicit Material Detection Using Dual-Energy X-Ray Images
(Zarka Private Univ, 2016) Hassanpour, Reza; Hassanpour, Reza; Yazılım Mühendisliği
Dual energy X-ray inspection systems are widely used in security and controlling systems. The performance of these systems however, degrades with the poor performance of human operators. Computer vision based systems are of vital importance in improving the detection rate of illicit materials, while keeping false alarms at a reasonably low level. In this study, a novel method is proposed for detecting material overlapping and reconstructing multiple images by alleviating these overlaps. Evaluation tests were conducted on images taken from luggage inspection X-ray screening devices used in shopping centres. The experimental results indicate that the reconstructed images are much easier to inspect by human operators than the unprocessed original images.
Citation - WoS: 13
Citation - Scopus: 17
Integration of Accessibility Design Patterns With the Software Implementation Process of Iso/Iec 29110
(Wiley, 2019) Sanchez-Gordon, Mary; Yilmaz, Murat; O'Connor, Rory V.; Sanchez-Gordon, Sandra
The Web Content Accessibility Guidelines was developed by World Wide Web Consortium with a goal of providing a single shared standard for web content accessibility that meets the needs of individuals, organizations, and governments. Given that there is a large percentage of very small entities that develop software who also utilize the ISO/IEC software process standard, the purpose of this study is the development of software design patterns for users with visual disabilities. As a result, four accessibility design patterns are defined: Authentication adapter, Blindness adapter, Dichromatic color vision adapter, and Blurry vision adapter. These patterns will help to improve the design of the web applications built using them while being compliant with the ISO/IEC 29110 standard. The use of design patterns also enables the transfer of design experience to programming practices and improves the software documentation. To validate the set of patterns, an online course for Spanish speakers was developed, and the evaluation was carried out using simulators, automated tools, experts, and users. Simulators and automated tools showed no accessibility errors and experts evaluated 10 heuristics principles and did not identify any severity issues. Taken together, our results provide positive evidence that users with visual disabilities could benefit from the proposed features.
Citation - WoS: 23
Citation - Scopus: 28
Interactive Three-Dimensional Virtual Environment To Reduce the Public Speaking Anxiety Levels of Novice Software Engineers
(inst Engineering Technology-iet, 2019) Yilmaz, M.; Gulec, U.; Yilmaz, A. E.; Isler, V.; O'Connor, R. V.; Clarke, P.; Nazligul, M. Denizci
Software engineering is a set of activities that rely not only on the technical tasks but also require abilities focused on social duties such as daily meetings and product introduction presentations. However, engineers may experience elevated levels of anxiety when required to present their work in an unfamiliar environment. More specifically, they may suffer from public speaking anxiety even though they are supposed to be effective in those social tasks as well as in their engineering activities. Fortunately, previous studies suggest that the virtual exposure intervention is an effective strategy to reduce public speaking anxiety. In this study, an interactive three-dimensional virtual environment similar to real classrooms and auditoriums was developed to examine whether this might decrease the anxiety levels of novice software engineers. To compare the traditional and virtual exposure intervention, the sample set was divided equally into two groups including one experimental group and one control group. For 4 weeks, the virtual exposure intervention was conducted in the experimental group, whereas the cognitive behaviour therapy-based psychoeducation was used in the control group. The findings from authors' study illustrate that the virtual exposure intervention may represent an alternative solution to the traditional interventions for software engineers seeking to overcome public presentation anxiety.

Browse

Browsing Bilgisayar Mühendisliği Bölümü Yayın Koleksiyonu by Scopus Q "Q2"