Bilgisayar Mühendisliği Bölümü Tezleri

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/58

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Finding the ethnical identity of human face
(2012) Yenice, Merve
In this thesis, how to find a human being’s ethnical identity from his/her face is analyzed. Parts of the face like eyes, nose, mouth, skin colour are used for defining the face. In addition to this, some programs like C# and Luxand are also used in correctly defining and calculating the facial parts. This calculation is very important and necessary in fractionating the human face and finding the dimensions of members of the face, because it gives the main idea about the shape, length and colour of face. The most important issues in determining and finding the ethnical identity of human face are shape, length and skin colour of the face. After finding these items the ethnical identity of a human can easily be found. The evidence found after working in this thesis is shown that the thesis has reached its aim willingly.
Multi-label and single-label text classification using standard machine learning algorithms and pre-trained bert transformer
(2023) Alfigi, Huda
Doğal dil işleme (DDİ) araştırmaları, dijital belgelerin artan kullanılabilirliği ve bunlara çeşitli şekillerde erişme ihtiyacı nedeniyle son zamanlarda büyük ilgi görmüştür. Dijital metin verilerindeki patlama, çeşitli metin işleme ve sınıflandırma tekniklerinin geliştirilmesi ihtiyacını ortaya koymaktadır. DDİ'deki en temel ve hayati zorluk metin sınıflandırmasıdır. Bu amaçla, belgeleri ve metinleri içeriklerine göre önceden belirlenmiş kategorilere ayırmak için önerilmiştir ve o zamandan beri makine öğrenimini uygulamanın en popüler yöntemlerinden biri haline gelmiştir. Makine öğrenimi (MÖ) yaklaşımı, genel bir tümevarım yaklaşımının bir dizi sınıflandırılmış metin ve ilgi sınıflarının özelliklerini kullanarak özel olarak sınıflandırılmış bir metin oluşturmayı öğrendiği bir yöntemdir. Ayrıca, ilgili bilgilerin keşfedilmesi, fazla bilgi yükünü azaltırken bilgi alma verimliliğini artırmaya yardımcı olabilir. Geleneksel modeller, standart makine öğrenimi algoritmalarını kullanarak sınıflandırmadan önce iyi örnek nitelikleri elde etmek için genellikle yapay yöntemler gerektirir. Bu nedenle, özellik çıkarma yöntemin etkinliğini önemli ölçüde kısıtlar. Öte yandan, derin öğrenme, özellik temsillerinin çıktılara aktarılmasına yardımcı olan bir dizi doğrusal olmayan dönüşüm gerçekleştirerek özellik çıkarma işlemini model oluşturma yaklaşımına dahil ettiği için daha fazla ilgi gören tipik modellerden farklıdır. Ayrıca, derin öğrenme algoritmaları, uzmanların kuralları ve öznitelikleri tanımlama ihtiyacını ortadan kaldırır, bunun yerine metinler için otomatik olarak üst düzey anlamsal temsiller sağlar. Bu nedenle, bu çalışmalarda, BERT gibi önceden eğitilmiş modellerden elde edilen bağlamsal gömme yeteneklerini keşfediyoruz ve küçük bir İngilizce haber veri kümesinde uygulanacak bazı geleneksel makine öğrenimi yöntemlerine ek olarak, büyük bir İngilizce haber veri kümesindeki metin belgelerinin çok etiketli sınıflandırmasından yararlanıyoruz. Son olarak, BERT'in bir başka versiyonu olan Arapça BERT, Arapça bir otel incelemesi veri kümesinden çıkarılan yönlere yönelik duygu eğlimini araştırmaktadır.
Prediction of the football match results with using machine learning algorithms
(2019) Çimen, Emre Altuğ
In this thesis, prediction results of the Spanish La Liga football matches were obtained by using three machine learning algorithms. The dataset includes four season match statistics and the results of these matches. In addition, this thesis investigated which performance parameters of the football game statistics affected the game results. Feature selection techniques were used to reduce the number of attributes. Three different classifiers which are artificial neural network, support vector machine and k- nearest neighborhood were used for prediction. Support vector machine classifier reached better results than the other classifiers when applied for the chosen fifteen attributes in the dataset.
Temporal video segmentation
(2019) Erol, Mehmet Murat
Teknolojinin ilerlemesi ile birlikte video içerik üretimi de hızlı bir şekilde artmıştır. Teknolojideki bu ilerleme aynı zamanda görsel bilgi kullanımını da arttırdı. Video içeriğinin üretimindeki ve tüketimindeki bu artışlar, videoların etkili bir şekilde bölümlenmesi, özetlenmesi ve sıralanması ihtiyacını doğurmuştur. Video bölümle, video özetleme ve sıralama işlemlerinin ilk adımıdır. Video bölümleme bir video anlamlı ve kendi içinde bütüncül parçalara ayırmayı hedefler. Videoyu kendi içinde bütüncül parçalara böldükten sonra videonun daha ileri analizi için anlamsal bölümleme tekniklerini uygulayabiliriz. Bu tezde, Zamansal Video Bölümleme sıkıştırılmış ve sıkıştırılmamış alanlarda incelenmiştir ve yapay sinir ağları kullanılarak ilgili çalışmada geçen metotlardan daha iyi performans gösteren bir metot sunulmuştur.
A computational analysis of a language structure in natural language text processing
(2005) Eş, Sinan
Text categorization or classification is a general task of classifying un-organized natural language texts according to specific subject matter or category. Electronic mail (e-mail) filtering is a binary text classification problem which the user emails can be classified as legitimate (non-spam) or un-wanted mail (spam). In this study, we tried to find a filtering solution that is able to automatically classify emails into spam and legitimate categories. In order to automatically and efficiently classify emails as spam or legitimate we took advantage of some Machine Learning methods and some novel ideas from Information Retrieval
Pattern recognition: Comparison study
(2005) Salim, Fawzı
Öne of the most important effects the field of Cognitive Science can have on the field of Computer Science is the development of technologies that make our tools more human. Evidenced by the fact that we are not currently ali using Tablet computers, accurate hand writing recognition is clearly a difficult problem to solve. Neural netvvorks field is interested with applications for many practices such as industrial process, marketing, medicine, business which is also relevant with hand writing recognition (our main study in this thesis). Hand writing recognition is an important field with applications in business 'form-filling1, including handwritten postal addresses, cheques, insurance applications, mail-order forms, tax returns, credit card sales slips, customs declarations and many others. These applications ali generale a handwritten script from an unconstrained population of writers and writing implements, which must subsequently be processed off-line by computer. To consider at the importance of neural networks and its applications, we used it in this research and we applied backpropagation algorithm to give us better results. We will present more details in the thesis especially in chapter four. The process of recognizing of handwriting from pixel information falls into a field of artificial iii l intelligence called pattern ör image recognition. Lots of work has been done in this field recently, and most techniques for pattern and image classifıcation make use of neural networks. This work implements neural networks in order to "learn" to recognize general features of hand vvritten digits using the well know backpropagation algorithm
An energy-efficient clustering based communication protocol with dividing the overall network area for wireless sensor networks
(2014) Khalaf, Abdulrahman Zaidan
In this thesis, the energy efficient and connectivity problem in wireless sensor networks (WSNs) is presented. There are more difference between energy levels of near nodes and far nodes of cluster heads. This problem compensated by dividing the entire network (sensor field) into equal area and applies different clustering policies to each section. The results compared with results of LEACH (Low Energy Adaptive Clustering Hierarchy). The performance of proposal system overcomed the previous studies. Also this protocol guaranted transmitting data and transmission in high traffic networks to reduce energy consumption and packet failure
Machine learning in artificial intelligence
(2006) Ercan, Tardu
In today’s world, learning is a process of computers as well as human being. “Learnable” systems and computers will become more important in following years and affect our lives in many ways. In this thesis, a survey has been carried out in the field of artificial intelligence, machine learning and especially on decision tree learning algorithms. Some of the decision tree learning algorithms was used to learn rules which are extracted from a dataset. The dataset which consists of water consumption of Ankara for one year and meteorological data of Ankara was used. The results indicate that which learning method is more efficient and have better performance.
Selection of the software development process measurement component on scrum software development: An analytic hierarchy process approach
(2019) Tekin, Muhammed Nesib
In today's world, software evolves faster than software production can respond; therefore, software development organizations not only deal with the uncertainties inherited from requirements but also work continuously to deal with deployment issues. Scrum is the most widely known and used agile development framework that guides the development process with its ability to create customer-valued software artifacts iteratively and incrementally, while seeking best practices to provide continuous measurement during the production. However, measuring success in Scrum is a challenging endeavor. In particular, it is hard to select the best fitting agile metric during consecutive Scrum sprints. The goal of this industrial case study was to utilize a multi-criteria decision-making by using the Analytic Hierarchy Process. To this end, a systematic selection process was designed for selecting appropriate software measurement component related to the project process management with the TÜBİTAK SAGE software development group. The set of criteria, which was used for selecting the software development process measurement components, determined as relevance, experience, functionality and feasibility & usability. According to results of this study, it was determined that the criterion of relevance has the most precedence by the ratio 49.225%, this was followed by experience criterion with 22.512%, feasibility & usability criterion with 17.040%, and criterion of functionality as 11.223%. Moreover, the distribution of the process metrics preferences of the software developers was analyzed according to their characteristic features and defense industry structure by using different distribution charts. Finally, the software process measurement components, which can be easily integrated the agile software process tool that is used by TÜBİTAK SAGE software development group are determined alternatives for performing selection process with Analytic Hierarchy Process method. Among the other options, Alternative-1 was chosen as the first with 40.259%, followed by Alternative-3 with 23.632%.
An interactive learning approach to teach ISO/IEC 12207 software life cycle processes
(2016) Aydan, Ufuk
Yazılım mühendisliği alanında özellikle yazılım projesi geliştirme kapsamında literatür dahilinde birçok iş ve çalışma olmasına karşın ISO/IEC 12207 Yazılım Yaşam Döngüsü Süreçleri temellerini ve etkileşimli öğrenme sürecini kapsayan uygun bir ciddi oyun örneği eksikliği bulunmaktadır. Ciddi oyunlar belirli konularda eğitici ve belirli beceri alanlarında yetkinlik kazanılmasını sağlayan uygulamalar olup oyun ve oyunlaştırma literatüründe önemli bir yere sahiptir. "Floors", bir ciddi oyun olmakta ve etkili bir şekilde öğrenme sürecini etkileşimli ve ilerlemeli bir tasarım ile sunmaktadır. Oyunun sahip olduğu ve kullanıcılara edindirdiği esas avantaj ISO/IEC 12207 yazılım yaşam döngüsü süreçlerinin temel presipleri hakkında aşinalık kazandırmak, aynı zamanda ilerlemeli yapısı sayesinde belirli süreçlerin akışını etkileşimli diyaloglarla 3 boyutlu sanal bir ortamda birleştirmesidir. Tüm bunları amaç kapsamı için adanmış görsel bir ofis ortamında gerçekleştirmesidir Floors özellikle öğrencilerin ISO/IEC 12207'nin belirli süreçlerinin kapsamında temel bir eğitim sunan, yardımcı bir araç olarak kullanılabilir. Oyunun katılımcılar tarafında oynanması itibariyle belirtilen kavramların ve konseptin hakkında ve ayrıca yazılım geliştirme yaşam döngüsünün izlenmesi ve devam ettirilmesi ile ilgili kullanıcılarda belirli bir farkındalığın sağlanması temel amaçlardandır. Araştırma sonuçları katılımcı popülasyonundan elde edilen veriler ile yapılan anket çalışması ile belirli derecede farkın ortaya çıktığı belirlenmiştir. Çalışmanın sonuçları dikkate alındığında Floors ile tecrübe edinen katılımcıların standart hakkında daha olumlu bir bakış açısı edindiği ve temel kavramları kazandığı gözlemlenmiştir.
Adopting rup (rational unified process) on a software development project
(2009) Taş, Tufan
This thesis analyzes the process of applying Rational Unified Process (RUP) successfully on a software development project step by step. Many software development projects today have a tendency to fail on some level. Even though they may not fail entirely, they might be completed with schedule delays, budget overrun or with poor quality that do not meet the requirements of customers because of poor management and lack of necessary documentation of the project. Applying RUP avoids these major problems in a project by developing set of work products which depict the essentials of the system from requirements to detailed design before the system could be implemented. However, software development teams have an overall attitude that RUP becomes less agile and too rigid as the size of projects get smaller. The thesis will also try to prove that this opinion is not true by using tools Rational Method Composer (RMC) and Rational Software Modeler (RSM) to successfully complete the project.
Defect product estimation using customer reviews, Amazon use case
(2022) Eyerci, Tarkan
Teknoloji her alanı etkilediği gibi ticareti de çok etkiledi. Günümüzde artık, üreticiler, perakendeciler, hizmet sağlayıcılar gibi son kullanıcıya hitap eden tüm işletmeler e-ticaret siteleri ve mobil uygulamaları gibi yöntemlerle internet üzerinden müşterilerine hızlıca ulaşabiliyorlar. Diğer yandan, müşteriler ise artık birçok seçenek arasından seçim yapma şansına sahipler. Kullanıcılar genellikle seçimlerini yaparken daha önce aynı tecrübeyi paylaşmış diğer kullanıcıların yorumlarından faydalanırlar. Bu açıdan kullanıcı yorumları çok değerli bilgiler içerir. Fakat yoğun kullanılan sitelerde bir insanın tek tek inceleyemeyeceği kadar çok yorum birikir. Biz bu çalışmada, ürünlerin belli bir özelliğine, yani kusurlu özelliklerine odaklandık. Kusur bilgisi içeren milyonlarca yorum içinden ilgili yorumları filtre edebilmek için bir yöntem öneriyoruz. Kusur ile ilgili kelimeleri sözlük yardımı ile elle oluşturup bu kelimeler geçen yorumları filtrelemek bir çözüm önerisi olabilir. Fakat bu kelime listesini elle oluşturmak verimli olmayacaktır. Bunun için sadece ilgili ürün gruplarına ait yorumları kullanarak kendi kelime temsil modelimizi eğitip, bu modelle birlikte kelime yakınlıklarını kullanarak daha verimli bir kusur kelimeleri listesi oluşturduk. Kullanıma hazır önceden eğitilmiş bir kelime temsil modelini indirip, bu modelle kendi modelimizi kıyasladık. Genel konularda hazır modelin daha başarılı olurken, özel bir konuda kendi modelimizin kelime listesi oluşturmada daha başarılı olduğunu gördük.
Matching composite drawings and mugshot photographs to determine the identity of the person
(2019) Karasolak, Mustafa
In this thesis, a new photo-sketch generation and recognition technique is proposed using residual convolutional neural network architecture. For this, the proposed architecture is trained with face photos and sketches. Sketches are applied to the proposed Region-based Convolutional Neural Networks (RCNN) architecture and, face photos are obtained at network output. Then, the obtained face photographs are compared with the images in the database. It is associated with the highest similarity photograph. Structural Similarity Index (SSIM) is used to measure similarity. It is very useful for law enforcement for image processing applications. 188 images are used for training and testing. Of these, 148 are used for training. 20 are used for validation and 20 are used for testing. Data augmentation is applied to 148 images used for training. As a result of the data augmentation process, 444 face images are obtained and used for network training. As a result of network training, the success of the training curve is 90.55% and the validation success is 91.1%. True face recognition success from generated face images with SSIM is 93.89% for CUHK database and 84.55% AR database.
Integrating computer vision with a robot arm system
(2014) Yosif, Zead Mohammed
During last decades, robotic system has been employed in different fields, such as, industrial, civil, military, medical, and many other applications. Vision system is integrated with robot systems to enhance the controlling performance of the robot system. A great deal of features can be computed using the information have been gotten from vision sensors (camera). The extracted information from vision system can be used in the feedback to have the ability to control the robot armtor motion, but the operation of extracting this information from vision system is time consuming. This thesis addressed the problem of following (tracking) and grasping of moving target (object) with limited velocity in real time by employing the technology of Eyein- Hand, whereas a camera attached (mounted) to the robot arm end effector. This done by using a predictor (Kalman filter) that estimates the positions of the target in the future, an algorithm was designed to track an object move in different trajectories, within the camera field of view. The Kalman filter uses the measured position of the target as well as previous state estimates to fix the location of thetarget object at the next time step, in other word, the Kalman filter is applied to keep observing the object till grasp it. The employing of vision system information in the feedback control of the robot systems have been the major research in robotics and Mechatronic systems. The utilizing from this information has been proposed to handle stability and reliability issues in vision-based control system.
Evaluation of terrain rendering algorithms
(2005) İnam, Emin
errain rendering plays an important role in outdoor virtual reality applications, games, Geographic Information System (GIS), military mission planning's and flight simulations, etc. Many of these applications require real-time dynamic interaction from end users and thus are required to rapidly process terrain data to adapt to user input. Typical height fields consist of a large number of polygons, so that even most high performance graphics computers have great difficulties to display even moderately sized height fields at interactive frame rates. The common solution is to reduce the complexity of the scene while maintaining a high image quality. This thesis is an evaluation of three real-time continuous terrain levels of detail algorithms described in the papers ROAMing Terrain: Real-time Optimally Adapting Meshes by Duchaineau, Real-Time Generation of Continuous Levels of Detail for Height Fields by Röttger and Fast Terrain Rendering Using Geometrical MipMapping by Willem H. de Boer. The evaluation and comparison of the algorithms is based on the trade- off of polygon count to terrain accuracy over separate test data sets. The main aim of this thesis is research on terrain rendering algorithms that is generate high quality image in real-time with using height data.
Multifunction robot controlled by computer vision system
(2014) Mustafa, Mohammed Sulaiman
In this thesis, we try to come up and build a robot platform with multifunction capabilities, easy to add, modify and delete those functions without redesigning, by using easy use technology that can create a suitable efficient platform. The process of building platform is by using figures, tables and programming code to make this thesis capable to apply and implement in real world, showing obstacles and challenges that lead to the key of success until it reaches the final goal. This thesis requires only basic level in electronic and computer programming because we are using a simplified way for building robot. The multifunction platform is a unique idea and opens new space to experimenters to get benefits from this opinions or ideas to use these functions in raw state, with no need to study hardware and software material of robot. The final robot form is shown in the last pages of this thesis as appendix
Classification of diabetic retinopathy using pre-trained deep learning models
(2019) Al-Kamachy, Inas Mudheher Raghib Kafı
Diabetic Retinopathy (DR) is considered to be the first factor that leads to blindness. If it is not detected early, many people around the world would suffer from the diabetic disease that may lead to DR in their eyes. Any delay in regular monitoring and screening by ophthalmologists may cause rapid and dangerous progress of this disease which finally leads to human vision loss. The imbalance between the numbers of doctors required to monitor this disease and the number of patients around the world increasing year by year shows a major problem leading to poor regular monitoring and loss vision in many cases which could have been detected had there been good treatment in the earlier stages of DR. In order to solve this problem, serious aid was needed for a computer aid diagnosis (CAD). Deep learning pre-trained models are state-of-art in image recognition and image detection with good performance. In this research, we used image pre-processing and we built several convolution neural network models from scratch and fine-tuned five pre-trained deep learning models which used ImageNet as the dataset for medical images of diabetic retinopathy in order to classify diabetic retinopathy into five classes. After that, we selected the model that showed good performance to build a diabetic retinopathy web application using Flask as a framework web service. We used the KAGGLE kernel website with Jupyter as a notebook as well as Flask to build our web application. The final result of the AUC was 0.68 using InceptionResNetV2.
Web services based real time data warehouse
(2012) Obalı, Murat
Today's business environment is quickly changing and business decision makers need for a historical picture of what happened and a picture of what was happening today. Traditional data warehouses provide a historical picture, but there is lack of fresh data. However, fresh data in data warehouses is a strong feature from the part of the users. The aim of this study is building a real time data warehouse using web services. First, we modelled both the conceptual and the logical design of real time data warehouse. For change data capture from source systems, we implemented web services based server and client software. Then, we used real time partition for real time data which is merged into data warehouse in a daily fashion. We, also, implemented a data integration service using query re-write approach to integrate data warehouse and real time partition data.
Parallelization study on the clustering technique to mine large datasets
(2011) Yıldırım, Ahmet Artu
Parallel clustering algorithm implementations concerning message passing interface (MPI) and compute unified device architecture (CUDA) model with their applications to very large datasets have been presented in the thesis. WaveCluster is a novel clustering approach based on wavelet transforms. Despite it?s novelty, it requires considerable amount of time to collect results for large sizes of multidimensional datasets. In the MPI algorithm; divide and conquer approach has been followed and communication among processors are kept at minimum to achieve high efficiency. Developed parallel WaveCluster algorithm exposes high speedup and scales linearly with the increasing number of processors. Parallel behavior of WaveCluster approach has been also investigated by executing the algorithm on graphical processing unit (GPU). High speedup values have been obtained in the computation of wavelet transform and connected component labeling algorithms in the GPUs with respect to the sequential algorithms running on the CPU
Model based human face detection using skin color segmentation
(2002) Özbay, Eylem
Model Based Human Face Detection Using Skin Color Segmentation Özbay, Eylem Ms, Department of Computer Engineering Supervisor: Dr. Reza Hassanpour January 2005, 85 pages For identification of the people easiest way using the faces. However, it requires determining the location of the faces in the images. Face Identification systems are generally preceded by face segmentation systems. The main goal of thesis is locating the human faces and segmentation regions belonging to them using skin color segmentation methods and facial features such as nose, eyes, mouth etc. The segmentation results may be used as input to other related systems

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Browsing Bilgisayar Mühendisliği Bölümü Tezleri by Department "Çankaya Üniversitesi, Fen Bilimleri Enstitüsü, Bilgisayar Mühendisliği Bölümü"