Bilgisayar Mühendisliği Bölümü Yayın Koleksiyonu

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

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An Exploratory Study to Assess Digital Map Zoom/Pan/Rotate Methods with HoloLens
(2018) Kılınç, İsmail; Yılmaz, Murat
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.
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: 14
Citation - Scopus: 18
Application of Bilstm-Crf Model With Different Embeddings for Product Name Extraction in Unstructured Turkish Text
(Springer London Ltd, 2024) Arslan, Serdar; 325411
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: 29
Citation - Scopus: 50
An Artificial Neural Network-Based Stock Trading System Using Technical Analysis and Big Data Framework
(Assoc Computing Machinery, 2017) Ozbayoglu, A. Murat; Dogdu, Erdogan; Sezer, Omer Berat
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: 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.
Author identification for Turkish texts
(Çankaya Üniversitesi, 2007) Taş, Tufan; Görür, Abdül Kadir
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: 37
Citation - Scopus: 49
Automated Classification of Rheumatoid Arthritis, Osteoarthritis, and Normal Hand Radiographs With Deep Learning Methods
(Springer, 2022) Maras, Hadi Hakan; Ureten, Kemal; 34410
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) Caniberk, Mustafa; Maras, Hadi Hakan; Maras, Erdem Emin; 34410
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
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.
Calculation of signal sources coordinates in 2D and 3D space
(Çankaya Üniversitesi, 2008) Alyeksyeyenkov, Yuriy
Methods of calculations of coordinate of signal sources which are inaccessible and direct measurement of their properties is impossible are analyzed. Cases of 2D – 3D spaces and single – multiple sources of signal are investigated separately. Recommendations and necessary mathematical equations for practical applications are given. Advantage of sources coordinate measurement using signal parameters in frequency domain is shown. Experimental analysis of methods on the base of MATLAB modeling demonstrates full coincidence of theory and practice
Cassandra ve MongoDB NoSQL Veri Tabanlarının Karşılaştırmalı Güvenlik Analizi
(2019) Saran, Murat; Saran, Nurdan; 17753
In this study, we analyze the security of two NoSQL databases, MongoDB 3.6.3 and Cassandra 3.11.1 in a multi-node configuration in two steps. The first step is a comparative study of both databases’ security features according to ten selected criteria from the literature. The second step is analyzing data encryption overhead using the Yahoo Cloud Serving Benchmark tool. This study will help decision-makers and researchers to realize the most crucial security features concerning NoSQL databases as well as to be able to analyze the NoSQL databases regarding the security features. Our security comparison results show that both databases have noteworthy security features. However, Cassandra takes the lead as it supports more security criteria. Besides, the encryption/decryption performance of the MongoDB business version is 53% faster than the Cassandra business version, and the average amount of data that the MongoDB business version can process per minute is 45% higher than the Cassandra business version. This result shows that it is more appropriate to use MongoDB in environments where encryption is required.
Classification of Linked Data Sources Using Semantic Scoring
(Ieice-inst Electronics information Communication Engineers, 2018) Dogdu, Erdogan; Kodaz, Halife; Yumusak, Semih; 142876
Linked data sets are created using semantic Web technologies and they are usually big and the number of such datasets is growing. The query execution is therefore costly, and knowing the content of data in such datasets should help in targeted querying. Our aim in this paper is to classify linked data sets by their knowledge content. Earlier projects such as LOD Cloud, LODStats, and SPARQLES analyze linked data sources in terms of content, availability and infrastructure. In these projects, linked data sets are classified and tagged principally using VoID vocabulary and analyzed according to their content, availability and infrastructure. Although all linked data sources listed in these projects appear to be classified or tagged, there are a limited number of studies on automated tagging and classification of newly arriving linked data sets. Here, we focus on automated classification of linked data sets using semantic scoring methods. We have collected the SPARQL endpoints of 1,328 unique linked datasets from Datahub, LOD Cloud, LODStats, SPARQLES, and SpEnD projects. We have then queried textual descriptions of resources in these data sets using their rdfs: comment and rdfs: label property values. We analyzed these texts in a similar manner with document analysis techniques by assuming every SPARQL endpoint as a separate document. In this regard, we have used WordNet semantic relations library combined with an adapted term frequency-inverted document frequency (tfidf) analysis on the words and their semantic neighbours. In WordNet database, we have extracted information about comment/label objects in linked data sources by using hypernym, hyponym, homonym, meronym, region, topic and usage semantic relations. We obtained some significant results on hypernym and topic semantic relations; we can find words that identify data sets and this can be used in automatic classification and tagging of linked data sources. By using these words, we experimented different classifiers with different scoring methods, which results in better classification accuracy results.
Citation - WoS: 23
Citation - Scopus: 32
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.
Content-based lecture video retrieval
(2023) Şahin, Yiğit
Günümüzde e-öğrenme veya çevrimiçi öğrenme olarak sıklıkla karşımıza çıkan uzaktan eğitim, eğitim-öğretim sırasında eğitmen ile öğrencinin fiziksel olarak yan yana olmadığı ve öğrenci-eğitmen iletişimini kolaylaştırmak için çeşitli teknolojilerin kullanıldığı yeni nesil bir eğitim yaklaşımıdır. Bu yaklaşım koronavirüs pandemisi (COVID-19) ile dünya çapındaki ağda (www) özellikle ders-eğitim içerikli videolar ile daha yaygın ve kullanılabilir hale gelmiştir. Ancak internet ortamında bulunan video sayılarındaki yüksek artış hızı, belirli bir içeriğe sahip videoya ulaşmak isteyen kullanıcıların video içeriklerine erişimini oldukça zorlaştırmıştır. Bahsedilen bu zorluklara bir öneri geliştirmek bağlamında bu araştırmada kullanıcıların belirli eğitim içerikleri ile ilgili videolara erişimini amaçlayan içerik tabanlı erişim yöntemi ele alınmıştır. Kullanıcıların aradıkları video içeriklerine daha kolay ulaşması için videoların doğru sınıflandırılması gerekmektedir. Teknik açıdan sınıflandırılmanın yapılabilmesi için ise öncelikle videoların metin bilgilerine ulaşılmalıdır. Bu çerçevede araştırmada videoların metin bilgilerini çıkarmak için optik karakter tanıma (OCR) ve otomatik konuşma tanıma (ASR) isimli iki farklı indeksleme yöntemi kullanılmıştır. Bu iki yöntem ve bu iki yöntemin birlikte kullanıldığı bir analiz, bu tezde belirli bir veri kümesi üzerinden ele alınmıştır. Veri kümesi olarak ise 110 videolu bir eğitim koleksiyonu kullanılmıştır. Bu kapsamda aynı veriyi kullanarak OCR ile analiz yapmış bir tez referans alınarak, bu kez de ASR yöntemi ile aynı metrik analizler yapılmıştır. Son olarak ise, hem OCR hem de ASR yöntemi kullanılarak çeşitli metrik değerler hesaplanmıştır. Verilerin sınıflandırma analizi için 3 farklı geleneksel makine öğrenme yöntemi kullanılmıştır. Kullanılan geleneksel makine öğrenme yöntemleri Support Vector Machine (SVM), Naïve Bayes ve Random Forest yöntemleridir. Bu doğrultuda farklı makine öğrenme yöntemleri ve farklı indeksleme yöntemlerinin aynı veri kümesi üzerinde metrik analizleri karşılaştırılmıştır. Yapılan analizler sonucunda ders videolarının içerik tabanlı erişimde kullanılabilmesi için günümüzde mümkün olan geleneksel makine öğrenme yöntemleri ile indeksleme yöntemlerinin güçlü ve zayıf yönlerinin açıklaması yapılmıştır. Bununla birlikte aynı konuda yapılacak gelecek çalışmalar için yöntemin geliştirilebilecek yönleri vurgulanmış ve bu konudaki öneriler sunulmuştur. Bu tez, yapılan karşılaştırmalı araştırmanın hem eğitim hem de yazılım sektörünü nasıl etkileyebileceği tartışması ile noktalanmaktadır.
Covariance Features for Trajectory Analysis
(Kaunas Univ Technology, 2018) Karadeniz, Talha; Maraş, Hadi Hakan; 304886; 34410
In this work, it is demonstrated that covariance estimator methods can be used for trajectory classification. It is shown that, features obtained via shrunk covariance estimation are suitable for describing trajectories. Compared to Dynamic Time Warping, application of explained technique is faster and yields more accurate results. An improvement of Dynamic Time Warping based on counting statistical comparison of base distance measures is also achieved. Results on Australian Sign Language and Character Trajectories datasets are reported. Experiment realizations imply feasibility through covariance attributes on time series.
Covid-19 Salgını Sırasında Evden Çalışma: Türk Yazılım Profesyonellerinin Deneyimleri
(2021) Tokdemir, Gul; 17411
Bu çalışma, Covid-19 salgını sırasında yazılım profesyonellerinin evden çalışma deneyimlerini araştırmaktadır. Bir anket aracılığıyla, bu tür çalışma ortamlarının özellikleriyle ilişkili olarak evden çalışmanın zorlukları incelenmiştir. Ayrıca, iki değişkenli analiz yoluyla, ev tabanlı çalışma özellikleri ile üretkenlik arasındaki ilişki araştırılmıştır. Bu çalışmanın sonuçları, yazılım profesyonellerinin pandemi döneminde daha uzun saatler çalıştıklarını ve evden çalışma ortamına adapte olmanın çoğunlukla kolay olduğunu göstermektedir. Evden çalışma ortamlarında ev işleri ve çocukların en önemli kesinti nedeni olduğu bildirilmiştir. Ayrıca yazılım profesyonelleri için öğleden sonraları ve sabahların en verimli çalışma aralıkları olduğu belirtilmiştir.
Citation - WoS: 56
Citation - Scopus: 86
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; 142876
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
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: 8
Citation - Scopus: 10
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; 17411; 34410
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,

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