Bilgisayar Mühendisliği Bölümü Yayın Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/253
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Article Citation - WoS: 3Citation - Scopus: 5Block 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 MühendisliğiAs 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.Article Citation - WoS: 6Citation - Scopus: 7Illicit Material Detection Using Dual-Energy X-Ray Images(Zarka Private Univ, 2016) Hassanpour, Reza; Hassanpour, Reza; Yazılım MühendisliğiDual 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.Article Citation - WoS: 2The 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, MeteAt 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.Article Citation - WoS: 43Citation - Scopus: 50Detection 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, YaseminObjective 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.Article Citation - WoS: 1Mm-Food: a High-Dimensional Index Structure for Efficiently Querying Content and Concept of Multimedia Data(Ios Press, 2023) Yazici, Adnan; Arslan, SerdarThe semantic query problem is commonly called the semantic gap and is one of the significant problems in multimedia data retrieval. In this study, we focus on multimedia data retrieval by combining semantic information with data content to solve the semantic gap problem effectively. The main idea behind the combination of low-level content descriptors and the concept of multimedia data is to represent the content information with the semantic information by adding a low-level content descriptor as a new dimension to the index structure. This new dimension is represented by constructing an array index structure that uses a fuzzy clustering algorithm. Thus, a new high-dimensional index structure, named MM-FOOD, supporting querying of multimedia data, including fuzzy querying, is presented in this paper. This proposed index structures construction and query algorithms are explained throughout this paper. Our experiments show that our indexing mechanism is considerably efficient compared to the basic indexing approach, which stores low-level content and semantic concept descriptors in separate structures when the data size is large.Article Citation - WoS: 22Citation - Scopus: 25Deep Learning Methods in the Diagnosis of Sacroiliitis From Plain Pelvic Radiographs(Oxford Univ Press, 2023) Ureten, Kemal; Maras, Yuksel; Duran, Semra; Gok, KevserObjectives 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.Article Structural stability and energetics of single-walled carbon nanotubes under uniaxial strain(2003) Dereli, G.; Özdoğan, CemA (10x10) single-walled carbon nanotube consisting of 400 atoms with 20 layers is simulated under tensile loading using our developed O(N) parallel tight-binding molecular-dynamics algorithms. It is observed that the simulated carbon nanotube is able to carry the strain up to 122% of the relaxed tube length in elongation and up to 93% for compression. Young's modulus, tensile strength, and the Poisson ratio are calculated and the values found are 0.311 TPa, 4.92 GPa, and 0.287, respectively. The stress-strain curve is obtained. The elastic limit is observed at a strain rate of 0.09 while the breaking point is at 0.23. The frequency of vibration for the pristine (10x10) carbon nanotube in the radial direction is 4.71x10(3) GHz and it is sensitive to the strain rate.Article Citation - WoS: 4Citation - Scopus: 4Phase Changes in Icosahedral 54-, 55-, 56-Atom Platinum Clusters(World Scientific Publ Co Pte Ltd, 2004) Güvenç, ZB; Kökten, H; Sebetci, AUsing the Voter and Chen version of an embedded-atom model, derived by fitting simultaneously to experimental data both the diatomic molecule and bulk platinum, we have studied the melting behavior of free, icosahedral, 54-, 55- and 56-atom platinum clusters in the molecular dynamics simulation technique. We present an atom-resolved analysis method that includes physical quantities such as the root-mean-square bond-length fluctuation and coordination number for individual atoms as functions of temperature. The effect of a central atom in the icosahedral structure to the melting process is discussed. The results show that the global minimum structures of the 54-, 55- and 56-atom Pt clusters do not melt at a specific temperature, rather, melting processes take place over a finite temperature range. The heat capacity peaks are not delta-functions, but instead remain finite. An ensemble of clusters in the melting region is a mixture of solid-like and liquid-like clusters.Article The impact of feature types, classifiers, and data balancing techniques on software vulnerability prediction models(2019) Kaya, Aydın; Keçeli, Ali Seydi; Çatal, Çağatay; Tekinerdoğan, BedirSoftware vulnerabilities form an increasing security risk for software systems, that might be exploited to attack and harm the system. Some of the security vulnerabilities can be detected by static analysis tools and penetration testing, but usually, these suffer from relatively high false positive rates. Software vulnerability prediction (SVP) models can be used to categorize software components into vulnerable and neutral components before the software testing phase and likewise increase the efficiency and effectiveness of the overall verification process. The performance of a vulnerability prediction model is usually affected by the adopted classification algorithm, the adopted features, and data balancing approaches. In this study, we empirically investigate the effect of these factors on the performance of SVP models. Our experiments consist of four data balancing methods, seven classification algorithms, and three feature types. The experimental results show that data balancing methods are effective for highly unbalanced datasets, text-based features are more useful, and ensemble-based classifiers provide mostly better results. For smaller datasets, Random Forest algorithm provides the best performance and for the larger datasets, RusboostTree achieves better performance.Article Citation - WoS: 3Citation - Scopus: 3Finite 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, SThe 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.
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