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Öğe A New Solution Approach for Non-Linear Equation Systems with Grey Wolf Optimizer(Sakarya Üniversitesi, 2018) Erdoğmuş, PakizeThe aim of this study is to bring a new perspective for the solutions of non-linear equation systems. So this study handles the non-linear equation systems as a constrained optimization problem, while generally is handled unconstrained optimization problem or multi objective optimization problem. The object is to minimize the sum of the squares of nonlinear equations under the nonlinear equality constraints. A recently developed heuristic optimization algorithm called Grey Wolf Optimizer (GWO) is proposed for the solution of nonlinear equation systems. Two results were obtained. Firstly, it has been seen that GWO can be an alternative solution technique for the solution of nonlinear equation systems. Secondly, modelling the systems of nonlinear equations as constrained optimization gives better results.Öğe Binary apple tree: A game approach to tree traversal algorithms(2012) Şentürk, Zehra Karapınar; Şentürk, Arafat; Zavrak, Sultan; Kara, Resul; Erdoğmuş, PakizeThe computer science students mostly face with the difficulties in learning the topics of algorithms courses. Only listening the topic from the teacher or just writing makes the learning volatile. Instead of listening or writing, if there is something visual, it would be more permanent to learn because visuality increases the learning potential and the time for learning is minimized. The adversities of classical education techniques were intended to be eliminated in this study via computer games which are becoming more and more popular in this age. An educational convenience is provided for the subject of tree traversal algorithms. Tree traversal algorithms are one of the basic and confused concepts in algorithms and programming courses in computer science. A game called "binary apple tree" was established to teach and learn the subject easier. © 2012 IEEE.Öğe The Classification of Breast Cancer with Machine Learning Techniques(Ieee, 2016) Kolay, Nurdan; Erdoğmuş, PakizeIn this study, it is aimed to classify breast cancer data attained from UCI(University of California-Irvine), Machine Learning Laboratory with some Machine Learning Techniques. With this aim, clustering performance of some distance measures in Matlab(C) has been compared, using breast cancer data. Later without using any pre-processing, some of the machine learning techniques are used for the clustering breast cancer data, using WEKA data mining software(C). As a result, it has been seen that distance measures effects the clustering performance nearly 12 percentage and the succes of the classification varies from % 45 to % 79, according to the methods.Öğe Comparative Study of Heart Disease Classification(Ieee, 2017) Ekiz, Simge; Erdoğmuş, PakizeThe aim of this paper is to compare two important machine learning platform results for the same dataset. With this aim, we conducted an experiment to classify heart disease both in Matlab(C) environment and WEKA(C), by using six different algorithms. Linear SVM, Quadratic SVM, Cubic SVM, Medium Gaussian SVM, Decision Tree and Ensemble Subspace Discriminant machine learning approaches are used for classifying the heart disease.Öğe Deep Learning Performance on Medical Image, Data and Signals(2019) Erdoğmuş, PakizeIn this study, the recent medical studies with deep learning between 2009-2019 have been researched forobserving the performance of deep learning on medical images, data and signal. Recent studies attained fromWeb of Science have been evaluated and selected according to the citation numbers. Studies have been listed asa table, according to the publication year, deep network structure, database used training and testing, evaluationmetric and results. The studies have also been classified into the organs and the types of important diagnosis.The results have shown that the deep learning network structures, applied on fundus images, have attained nearly%99 percent accuracy. Although most of the studies between the range, made by Radiology and NuclearMedicine Imaging, the accuracy of the results are 80-90% range. The current studies especially focus onautomatic detection or classification of the tumor as benign or malign. Studies are mostly on medical CT,ultrasound, radiography and MRI images. This results show that computer aided medical diagnosis systems willbe used in a very near future with fully performance.Öğe Destek Vektör Makineleri, YSA, K-Means ve KNN Kullanarak Arı Türlerinin Sınıflandırılması(2018) Demir, Hasan; Erdoğmuş, Pakize; Kekeçoğlu, MeralBu çalışmada arı kanatları üzerindeki kavşak noktalarına göre arı türlerinin sınıflandırılması amaçlanmıştır. Bu amaçla beş farklı ilden alınan arı kanat resimleri üzerinde kavşak noktaları belirlenmiştir. Arı kanatları üzerinde kavşak noktalarının belirlenmesi işleminin minimum hata ile yapılması için yeni bir algoritma önerilmiştir. Kavşak noktaları kullanılarak 27 morfolojik özellik çıkarılmıştır. Bu özellikler normalize edilerek sınıflandırmada kullanılmıştır. Destek vektör makineleri, yapay sinir ağları, K-Ortalama ve K en yakın komşuluk sınıflandırma yöntemi olarak kullanılmış, yapay sinir ağları ile sınıflandırma diğer sınıflandırma yöntemlerine göre daha iyi sonuç vermiştir. Kavşak noktaları için önerilen algoritmanın sınıflandırma başarısını arttırdığı görülmüştürÖğe Distance network learning with real network devices(2010) Kara, Resul; Şentürk, Zehra Karapınar; Erdoğmuş, PakizeNetwork education given in the universities and in the other education areas constitutes the backbone of computer science. This education is provided not only in theoretical form but also in the practical way. Since it is hard to settle some information of computer networks in learners' mind practices are the necessary activities of learning process. As the visuality increases, learners start learning easier. So, network education is given in the applied form with real devices such as switches, routers, servers, etc. Although it is necessary to perform this education in this way, it may not always be possible with some reasons like high cost and installation hardness. The reason of planning to do this project comes exactly from this point. We are supposed to get over the difficulties of a qualified network education with some techniques used for distance learning and the remote communication with the network devices. To realize the project we will use the network labs of computer engineering department of Düzce University. With only this lab we will reach many learners and this will be profitable for both the client and the manufacturer. The system will include authorization, network knowledge assessment test, application, and logging out operations. After opening the system some questions are directed to the users optionally and according to the result of the assessment related part of the lesson is given. In this point, users first interact with the authorization server and then they are directed to the application server. Next, they will be using the network devices out of classroom wherever they want. ©2010 IEEE.Öğe Doğrusal Olmayan Regresyon Parametrelerinin Sezgisel Yöntemlerle Tahmini(2017) Erdoğmuş, Pakize; Ekiz, SimgeGerçek dünyadaki deneysel olarak elde edilen veriler ve sinyallerin matematiksel modelleri her zaman kesin ve belirli değildir. Sinyal işlemede ve diğer deneysel çalışmalarda veriler için bir matematiksel model elde etmek ve bu matematiksel modelin parametrelerinin tahmini önemli bir konudur. Bu çalışmada üç farklı veri seti ve bu veri setleri için literatürde önerilen beş farklı modelin parametreleri doğrusal olmayan regresyon metodları ve sezgisel arama algoritmaları ile tespit edilmeye çalışılmıştır. Doğrusal olmayan regresyon metodlarının en büyük dezavantajı yakınsamalarının parametrelerin ilk tahminlerine bağlı olmasıdır. Oysa bu çalışmada kullanılan sezgisel algoritmaların en iyi sonuca yakınsamaları başlangıç değerlerinden bağımsızdır. Bu amaçla iki tür test yapılmıştır. Birinci testte veri setleri ve her veri seti için önerilen modellere gerçek değerlerine yakın ilk değerler atanmış ve modeller hem klasik hemde sezgisel algoritmalar ile optimize edilmeye çalışılmıştır. Sezgisel agoritmalardan Genetic Algoritma(GA) ve Parçacık Sürü Optimizasyonu(PSO) algoritmaları ile elde edilen sonuçlar, klasik algoritmalar ile karşılaştırılmıştır. Birinci testte hem klasik yöntemler hemde sezgisel yöntemler model parametrelerini tahmin etmişlerdir. İkinci testte ise modellerin ilk değerleri gerçek değerlerden uzak seçilmiştir. Bu testte sezgisel algoritmalar daha başarılı sonuçlar vermiştir. Doğrusal olmayan regresyon analizinde kullanılan klasik algoritma sonuçları gerçek parametre değerine tüm çözümlerde yakınsayamamıştır. Yapılan analizler sonucunda model parametrelerinin ik değerleri hakkında bir bilgi olmadığı durumlarda sezgisel yöntemlerin doğrusal olmayan regresyon analizinde iyi bir alternatif olacağı görülmüştürÖğe DÜZCE İLİNİN HAYVANSAL ATIKLARDAN ÜRETİLEBİLECEK BİYOGAZ POTANSİYELİ VE K-MEANS KÜMELEME İLE OPTİMUM TESİS KONUMUNUN BELİRLENMESİ(Düzce Üniversitesi, 2015) Yürük, Fuat; Erdoğmuş, PakizeArtan nüfusla birlikte fosil kökenli yakıtların sınırlı olması ve çevreye verdiği zararlar nedeniyle yeni enerji kaynakları için arayış başlamıştır. Bu sebeple, sürekli, yenilenebilir ve çevreye zararsız enerji kaynakları önem kazanmıştır. Biyogaz, anaerobik sindirim ya da biyolojik maddelerin fermantasyonu ile elde edilir. Diğer enerji türlerine göre temiz, ısı değeri yüksek bir enerji kaynağıdır ve fermente olmuş gübre tarımda daha değerli bir kaynaktır. Bu çalışmanın amacı Düzce ili ve ilçelerinde hayvansal atıklarından biyogaz potansiyelini hesaplamak ve bu tesisleri K-means kümeleme ile konumlarına göre kümelere ayırmaktır. Sonra K küme tek bir küme ile kümelenerek tüm tesisler ele alındığında Euklid uzaklıkları toplamı en küçük değeri veren (tüm tesislere en yakın) tesis konumunu belirlemektir. Bunun için, Türkiye İstatistik Kurumunun 2013 yılı verileri dikkate alınmıştır.Öğe Elektrik Enerji Dağıtım Sisteminde Ekonomik Aktif Güç Dağıtımının Genetik Algoritma İle Belirlenmesi(2009) Öztürk, Ali; Tosun, Salih; Erdoğmuş, Pakize; Hasırcı, UğurBu çalışmada, kayıpları olan iletim hattı şebekesini besleyen farklı yakıt türlerine sahip termik santrallerin optimum çalışma noktaları belirlenmiştir. Tüketicilerin talep ettikleri toplam aktif güç değerleri ve iletim hatlarında meydana gelen toplam aktif güç kayıplarının, santrallerce karşılanması ön şart olarak belirlenmiştir. Bu koşullar altında çalışan güç sisteminde, toplam yakıt maliyetinin minimum olmasını sağlayan santrallerin aktif güç değerleri hesaplanmıştır. Bu şekilde yapılan çalışmalara ekonomik aktif güç dağıtımı denilmektedir. Çalışmada, ilk olarak geleneksel optimizasyon yöntemi olarak kabul edilen Lagrange İterasyon (Lİ) yöntemi kullanılarak problemin çözümü sağlanmıştır. Aynı problem, alternatif bir yöntem olarak Genetik Algoritma (GA) ile de çözülmüştür. Her iki yöntem ile elde edilen değerler karşılaştırılmıştır. Ortaya çıkan sonuçlar ekonomik aktif güç dağıtımının, GA yöntemi ile daha güvenilir belirlenebileceğini göstermiştir.Öğe EMPOWERING SELF-DETECTION: A GRAPHICAL USER INTERFACE POWERED BY MACHINE LEARNING FOR EARLY DIAGNOSIS OF ALZHEIMER'S DISEASE(Istanbul Ticaret University, 2024) Kabakuş, Abdullah Talha; Erdoğmuş, PakizeAlzheimer’s Disease (AD) is one of the most, if not the most, devastating neurodegenerative diseases that are incurable and progressive. Early diagnosis of AD comes with many promises in terms of medicine, sociology, and economics. Despite the existence of numerous studies that aim for early diagnosis of AD, to the best of our knowledge, there is not a publicly available tool that lets end-users assess AD. To address this gap, we propose a Graphical User Interface (GUI) powered by Machine Learning (ML) that makes self-assessment of AD possible – without any input from medical experts. The developed GUI lets end-users enter various information considering both commonly used features for the diagnosis of AD and the questions available in the gold standard screening tool for the diagnosis of AD, namely the Mini-Mental State Exam. In addition to employing 11 traditional ML algorithms, we propose a novel 1-dimensional (1D) Convolutional Neural Network (CNN). All ML models were trained on a gold standard dataset that comprised 373 records from three subject classes as follows: (i) non-demented, (ii) demented, and (iii) converted. Once the end-user enters the required input through the developed GUI, the previously trained ML model assesses the diagnosis of AD through this input in a couple of seconds. According to the experimental results, the proposed novel 1D CNN outperformed the state-of-the-art by obtaining an accuracy as high as 95,3% on the used gold standard dataset.Öğe Environmental/economic dispatch using genetic algorithm and simulated annealing(2009) Erdoğmuş, Pakize; Öztürk, Ali; Duman, SerhatIn recent years, running of the generators at minimum cost and desired limit values has gradually increasing importance at Power systems using thermal-fueled generators. Various algorithms for solving economic dispatch problem have been found in the literature. In this study, Genetic Algorithm (GA) and Simulated Annealing (SA) solutions to Economic Cost Dispatch (ECD), Environmental Dispatch (ED), Environmental/Economic Dispatch (EED) have been found, by taking into account the environmental issue. A sample consisting of six thermal generators are presented. Transmission losses are included. Results taken with both methods have been compared to each other.Öğe Finding an optimum location for biogas plant: a case study for Duzce, Turkey(Springer, 2018) Yürük, Fuat; Erdoğmuş, PakizeThis study is a case study for modelling and solving a real-life problem. In this study, a practical approximation for finding an optimum location of a foundation was realized with k-means clustering and optimization. Duzce, in the northwest of Turkey, has been researched for the biogas potential to found biogas plant. With this aim, the number of poultry in Duzce has been determined and presented their potential of biogas. Since the number of poultry is quite enough to found a biogas plant, later the location of the poultry farms and their potentials has been determined. Since there are more than 400 poultry farms in Duzce, firstly locations are clustered with classical k-means algorithm. k is specified as 6-8 with an expert knowledge. Later, the nearest location for each cluster center has been attained with simulated annealing with the objective of minimizing the transportation cost. As a result, it has been determined an optimum location for probable biogas plant for Duzce.Öğe Font and Turkish Letter Recognition in Images with Deep Learning(Ieee, 2018) Sevik, Aylin; Erdoğmuş, Pakize; Yalçın, ErdiThe purpose of this article is to recognize letter and especially font from images which are containing texts. In order to perform recognition process, primarily, the text in the image is divided into letters. Then, each letter is sended to the recognition system. Results are filtered according to vowels which are most used in Turkish texts. As a result, font of the text is obtained. In order to separate letters from text, an algorithm used which developed by us to do separation. This algorithm has been developed considering Turkish characters which has dots or accent such as i, j, u, o and g and helps these characters to be perceived by the system as a whole. In order to provide recognition of Turkish characters, all possibilities were created for each of these characters and the algorithm was formed accordingly. After recognizing the each character, these individual parts are sended to the pre-trained deep convolutional neural network. In addition, a data set has been created for this pre-trained network. The data set contains nearly 13 thousands of letters with 227(star)227(star)3 size have been created with different points, fonts and letters. As a result, 100 percent of success has been attained in the training. %79.08 letter and %75 of font success has been attained in the tests.Öğe FPGA Implementation of RK4 based Van der Pol Oscillator(Turgut Ozal Univ, 2012) Koyuncu, İsmail; Erdoğmuş, PakizeFPGA chips have quite high speed and capacity now. They are also used in several disciplines. For high speed and performance required applications, FPGAs are of great importance since they provide flexible and low-cost solutions. Most of these applications require intensive mathematical operations and the calculations of these mathematical operations are both time consuming and difficult. Differential equations are the biggest part of such applications. This study aims to implement the Van der Pol oscillator on FPGA. In this process, the fourth order Runge-Kutta algorithm was selected as ODE solver. Van der Pol oscillator equations are quite sensitive to the parameter which specifies the nonlinearity and the strength of damping. Van der Pol oscillator's differential equations can have stiff property related to damping parameter. So, implementation performance was tested for different parameter values which make the differential equations non-stiff.Öğe A fuzzy image clustering method based on an improved backtracking search optimization algorithm with an inertia weight parameter(Elsevier Science Bv, 2019) Toz, Güliz; Yücedağ, İbrahim; Erdoğmuş, PakizeIn this paper, we introduced a novel image clustering method based on combination of the classical Fuzzy C-Means (FCM) algorithm and Backtracking Search optimization Algorithm (BSA). The image clustering was achieved by minimizing the objective function of FCM with BSA. In order to improve the local search ability of the new algorithm, an inertia weight parameter (w) was proposed for BSA. The improvement was accomplished by using w in the steps of the determination of the search-direction matrix of BSA and the new algorithm was named as w-BSAFCM. In order to show the effectiveness of the new algorithm, FCM was also combined with the general forms of BSA in the same manner and three benchmark images were clustered by utilizing these algorithms. The obtained results were analyzed according to the objective function and Davies-Bouldin index values to compare the performances of the algorithms. According to the results, it was shown that w-BSAFCM can be effectively be used for solving image clustering problem. (C) 2018 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University.Öğe g-BSAFCM : A New Hybrid Clustering Algorithm(Ieee, 2016) Toz, Güliz; Erdoğmuş, PakizeClustering is dividing a dataset into subsets that has similar characteristics. In this study, fuzzy c-means clustering algorithm (FCM) and a new evolutionary optimization algorithm, Backtracking Search (BSA) algorithm, were combined and a new hybrid clustering algorithm (BSAFCM) was proposed. Moreover, the local search abilities of the new algorithm was improved and the new algorithm was named as g-BSAFCM. Three benchmark datasets from UCI Machine Learning Repository database were clustered by using the developed algorithms and FCM. According to the results g-BSAFCM has achieved better results than FCM and BSAFCM.Öğe A game to test pointers: Path finding(2012) Şentürk, Zehra Karapınar; Zavrak, Sultan; Şentürk, Arafat; Kara, Resul; Erdoğmuş, PakizePointers are one of the most difficult to understand topics in programming courses. Since the topic is some virtual, the students of computer science face with difficulties in understanding. They hardly imagine the addresses of memory cells, their contents, and the pointers pointing to those memory cells. To test the knowledge and to reinforce the explanations done on the lecture hours, we have intended to create a game related to pointers. This game was constituted with the purpose of visualizing the concept of pointers into the students' brains and so to increase the understanding of the subject. The students will not only listen the topic from the teacher or just write, but in an evaluation test, they will also see what is going on in the memory cells of their computer. This will make the teaching more and more permanent. © 2012 IEEE.Öğe GPU accelerated training of image convolution filter weights using genetic algorithms(Elsevier Science Bv, 2015) Akgün, Devrim; Erdoğmuş, PakizeGenetic algorithms (GA) provide an efficient method for training filters to find proper weights using a fitness function where the input signal is filtered and compared with the desired output. In the case of image processing applications, the high computational cost of the fitness function that is evaluated repeatedly can cause training time to be relatively long. In this study, a new algorithm, called sub-image blocks based on graphical processing units (GPU), is developed to accelerate the training of mask weights using GA. The method is developed by discussing other alternative design considerations, including direct method (DM), population-based method (PBM), block-based method (BBM), and sub-images-based method (SBM). A comparative performance evaluation of the introduced methods is presented using sequential and other GPUs. Among the discussed designs, SBM provides the best performance by taking advantage of the block shared and thread local memories in GPU. According to execution duration and comparative acceleration graphs, SBM provides approximately 55-90 times more acceleration using GeForce GTX 660 over sequential implementation on a 3.5 GHz processor. (C) 2015 Elsevier B.V. All rights reserved.Öğe Histogram-based automatic segmentation of images(Springer, 2016) Küçükkülahlı, Enver; Erdoğmuş, Pakize; Polat, KemalThe segmentation process is defined by separating the objects as clustering in the images. The most used method in the segmentation is k-means clustering algorithm. k-means clustering algorithm needs the number of clusters, the initial central points of clusters as well as the image information. However, there is no preliminary information about the number of clusters in real-life problems. The parameters defined by the user in the segmentation algorithms affect the results of segmentation process. In this study, a general approach performing segmentation without requiring any parameters has been developed. The optimum cluster number has been obtained searching the histogram both vertically and horizontally and recording the local and global maximum values. The quite nearly values have been omitted, since the near local peaks are nearly the same objects. Segmentation processes have been performed with k-means clustering giving the possible centroids of the clusters and the optimum cluster number obtained from the histogram. Finally, thanks to histogram method, the number of clusters of k-means clustering has been automatically found for each image dataset. And also, the histogram-based finding of the number of clusters in datasets could be used prior to clustering algorithm for other signal or image-based datasets. These results have shown that the proposed hybrid method based on histogram and k-means clustering method has obtained very promising results in the image segmentation problems.
