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Öğe QDNS: Quantum Dynamic Network Simulator Based on Event Driving(Institute of Electrical and Electronics Engineers Inc., 2021) Ceylan, Osman Semi; Yılmaz, İhsanAfter the no-cloning theory was presented in the quantum physics field, researchers offered more secure but theoretical protocols than classic ones shaped around this theorem. Without much time passed, we then observed that experimental studies have been made particularly for the national security concerns. In line with these events, with this study, we are presenting the QDNS, an event driven quantum network simulation framework for enthusiasts of the filed to simulate their custom protocols in quantum network topology. With the event triggered way, we tried to make a more understandable and user-friendly environment yet powerful enough to take into account of complex nature of the quantum world.Öğe Strange quark matter solutions for Marder's universe in f (R, T) gravity with Λ(Springer, 2016) Aygün, Sezgin; Aktaş, Can; Yılmaz, İhsanIn this paper, we investigate homogeneous cylindrically symmetric Marder's universe in the presence of strange quark matter (SQM) source in f (R, T) gravity with cosmological constant Lambda. For this aim we have used the anisotropy feature (sigma(x)(x)/theta) of Marder type universe and equation of state (EoS) strange quark matter to obtain solutions in two classes f (R, T) gravity (Harko et al. in Phys. Rev. D 84:024020, 2011). Finally, some physical and kinematical properties are discussed.Öğe New fully automated software for assessment of brachial artery flow-mediated dilation with advantages of continuous measurement(Turkish Soc Cardiology, 2012) Ercan, Ertugrul; Kırılmaz, Bahadır; Kahraman, İsmail; Bayram, Vildan; Doğan, HüseyinObjective: Flow-mediated dilation (FMD) is used to evaluate endothelial functions. Computer-assisted analysis utilizing edge detection permits continuous measurements along the vessel wall. We have developed a new fully automated software program to allow accurate and reproducible measurement. Methods: FMD has been measured and analyzed in 18 coronary artery disease (CAD) patients and 17 controls both by manually and by the software developed (computer supported) methods. The agreement between methods was assessed by Bland-Altman analysis. Results: The mean age, body mass index and cardiovascular risk factors were higher in CAD group. Automated FMD% measurement for the control subjects was 18.3+/-8.5 and 6.8+/-6.5 for the CAD group (p=0.0001). The intraobserver and interobserver correlation for automated measurement was high (r=0.974, r=0.981, r=0.937, r=0.918, respectively). Manual FMD% at 60th second was correlated with automated FMD % (r=0.471, p=0.004). Conclusions: The new fully automated software (c) can be used to precise measurement of FMD with low intra-and interobserver variability than manual assessment. (Anadolu Kardiyol Derg 2012; 12: 553-9)Öğe Higher dimensional FRW universe solutions with quark and strange quark matter in creation field cosmology(Elsevier Science Bv, 2016) Aygün, Sezgin; Aktaş, Can; Yılmaz, İhsan; Şahin, MustafaIn this study, firstly we have studied the behavior of quark and strange quark matter for a (n + 2) -dimensional Friedmann-Robertson-Walker (FRW) universe which is homogeneous and isotropic in creation field (C-field) cosmology. Using the deceleration parameter two different exact solutions of the modified Einstein equations in C-field cosmology are obtained. In addition, we obtain exact solutions of the quark and strange quark matter for a (n + 2)-dimensional homogeneous and isotropic static Einstein universe (SEU) and a maximally symmetric de Sitter vacuum universe in four dimensions. Also, using. C = 0 in C-field theory, we get the SEU and de Sitter vacuum universes in Riemann geometry. Finally, some physical and kinematical quantities are discussed. (C) 2016 The Physical Society of the Republic of China (Taiwan). Published by Elsevier B.V. All rights reserved.Öğe Teleparallel energy-momentum distribution of various black hole and wormhole metrics(World Scientific Publ Co Pte Ltd, 2018) Aygün, Sezgin; Baysal, Hüsnü; Aktaş, Can; Yılmaz, İhsan; Sahoo, P. K.; Tarhan, İsmailUsing the Einstein, Bergmann-Thomson (BT) and Landau-Lifshitz (LL) energy and momentum formulations in teleparallel gravity (TG), we obtain the total energy and momentum distributions for phantom black hole metric. We get different energy distributions similar to the earlier study and the momentum distributions vanish for phantom black hole metric in TG. These momentum solutions agree with the study of Sahoo et al. in general relativity. However, using Einstein, Bergmann-Thomson and Landau-Lifshitz energy and momentum complexes, we investigate regular black hole, asymptotically flat wormhole, anti-de Sitter wormhole and Ellis wormhole solutions in TG. We obtain i) same BT and LL energy density solutions for regular black hole metric, (ii) same and zero energy distribution for asymptotically flat wormhole, (iii) proportion with Einstein and BT energy density solutions for anti-de Sitter wormhole, (iv) same and negative Einstein and BT energy density solutions for Ellis wormhole in TG.Öğe Improving effectiveness of different deep learning-based models for detecting COVID-19 from computed tomography (CT) images(Springer Science and Business Media Deutschland GmbH, 2021) Acar, Erdi; Şahin, Engin; Yılmaz, İhsanCOVID-19 has caused a pandemic crisis that threatens the world in many areas, especially in public health. For the diagnosis of COVID-19, computed tomography has a prognostic role in the early diagnosis of COVID-19 as it provides both rapid and accurate results. This is crucial to assist clinicians in making decisions for rapid isolation and appropriate patient treatment. Therefore, many researchers have shown that the accuracy of COVID-19 patient detection from chest CT images using various deep learning systems is extremely optimistic. Deep learning networks such as convolutional neural networks (CNNs) require substantial training data. One of the biggest problems for researchers is accessing a significant amount of training data. In this work, we combine methods such as segmentation, data augmentation and generative adversarial network (GAN) to increase the effectiveness of deep learning models. We propose a method that generates synthetic chest CT images using the GAN method from a limited number of CT images. We test the performance of experiments (with and without GAN) on internal and external dataset. When the CNN is trained on real images and synthetic images, a slight increase in accuracy and other results are observed in the internal dataset, but between 3 % and 9 % in the external dataset. It is promising according to the performance results that the proposed method will accelerate the detection of COVID-19 and lead to more robust systems.Öğe A novel approach to distinguish complicated and non-complicated acute cholecystitis: Decision tree method(Lippincott Williams and Wilkins, 2023) Gojayev, Afig; Karakaya, Emre; Erkent, Murathan; Yücebaş, Sait Can; Aydın, Hüseyin Onur; Kavasoğlu, Lara; Aydoğan, Cem; Yıldırım, SedatIt is difficult to differentiate between non-complicated acute cholecystitis (NCAC) and complicated acute cholecystitis (CAC) preoperatively, which are two separate pathologies with different management. The aim of this study was to create an algorithm that distinguishes between CAC and NCAC using the decision tree method, which includes simple examinations. In this retrospective study, the patients were divided into 2 groups: CAC (149 patients) and NCAC (885 patients). Parameters such as patient demographic data, American Society of Anesthesiologists (ASA) score, Tokyo grade, comorbidity findings, white blood cell (WBC) count, neutrophil/lymphocyte ratio, C-reactive protein (CRP) level, albumin level, CRP/albumin ratio (CAR), and gallbladder wall thickness (GBWT) were evaluated. In this algorithm, the CRP value became a very important parameter in the distinction between NCAC and CAC. Age was an important predictive factor in patients with CRP levels >57 mg/L, and the critical value for age was 42. After the age factor, the important parameters in the decision tree were WBC and GBWT. In patients with a CRP value of ≤57 mg/L, GBWT is decisive and the critical value is 4.85 mm. Age, neutrophil/lymphocyte ratio, and WBC count were among the other important factors after GBWT. Sex, ASA score, Tokyo grade, comorbidity, CAR, and albumin value did not have an effect on the distinction between NCAC and CAC. In statistical analysis, significant differences were found groups in terms of gender (34.8% vs 51.7% male), ASA score (P < .001), Tokyo grade (P < .001), comorbidity (P < .001), albumin (4 vs 3.4 g/dL), and CAR (2.4 vs 38.4). By means of this algorithm, which includes low-cost examinations, NCAC and CAC distinction can be made easily and quickly within limited possibilities. Preoperative prediction of pathologies that are difficult to manage, such as CAC, can minimize patient morbidity and mortality.Öğe An Ontology based product recommendation system for next generation e-retail(Taylor and Francis Ltd., 2023) Tiryaki, Ali Murat; Yücebaş, Sait CanThe number of e-commerce resources has increased considerably. Thus, it has become important for sellers to be able to quickly recommend products to potential buyers. Some product recommendation systems developed for this purpose. However, due to the lack of semantics, the systems’ success in recommending accurate products according to user preferences is low. In this study carried out within the scope of a state-funded R&D project, an ontology-based personalized product recommendation system named E-Prod was developed. E-Prod tracks various e-commerce systems in real time and transfers the product information to the ontology model. E-Prod uses a novel recommendation approach that combines machine learning and semantic matching to provide personalized recommendations. The system learns user’s preferences based on semantic relationships between products by monitoring their behaviors. In this way, accurate recommendations are made by semantic matching between products and user preferences. E-Prod has been tested with over 250 registered users and compared to traditional collaborative recommendations in terms of accuracy, precision, and recall. As a result, E-Prod outperformed traditional methods by 92.79% accuracy, 92.93% precision, and 90.58% recall. Within the scope of this study, E-Prod covers the clothing, shoes, and bag retail sectors. However, it provides a generic infrastructure for new generation e-commerce systems. Its reusable modules can be adapted to any domain.Öğe Optimized Decision Tree and Black Box Learners for Revealing Genetic Causes of Bladder Cancer(Tech Science Press, 2023) Yücebaş, Sait CanThe number of studies in the literature that diagnose cancer with machine learning using genome data is quite limited. These studies focus on the prediction performance, and the extraction of genomic factors that cause disease is often overlooked. However, finding underlying genetic causes is very important in terms of early diagnosis, development of diagnostic kits, preventive medicine, etc. The motivation of our study was to diagnose bladder cancer (BCa) based on genetic data and to reveal underlying genetic factors by using machine-learning models. In addition, conducting hyperparameter optimization to get the best performance from different models, which is overlooked in most studies, was another objective of the study.Within the framework of these motivations, C4.5, random forest (RF), artificial neural networks (ANN), and deep learning (DL) were used. In this way, the diagnostic performance of decision tree (DT)-based models and black box models on BCa was also compared. The most successful model, DL, yielded an area under the curve (AUC) of 0.985 and a mean square error (MSE) of 0.069. For each model, hyper-parameters were optimized by an evolutionary algorithm. On average, hyper-parameter optimization increased MSE, root mean square error (RMSE), LogLoss, and AUC by 30%, 17.5%, 13%, and 6.75%, respectively. The features causing BCa were extracted. For this purpose, entropy and Gini coefficients were used for DT-based methods, and the Gedeon variable importance was used for black box methods. The single nucleotide polymorphisms (SNPs) rs197412, rs2275928, rs12479919, rs798766 and rs2275928, whose BCa relations were proven in the literature, were found to be closely related to BCa. In addition, rs1994624 and rs2241766 susceptibility loci were proposed to be examined in future studies.Öğe Predictors of rapidly progressive glomerulonephritis in acute poststreptococcal glomerulonephritis(Springer Science and Business Media Deutschland GmbH, 2023) Karakaya, Deniz; Güngör, Tülin; Kargin Çakıcı, Evrim; Yazılıtaş, Fatma; Çelikkaya, Evra; Yücebaş, Sait Can; Bülbül, MehmetBackground Acute post-streptococcal glomerulonephritis (APSGN) is an immune-mediated inflammatory respsonse in the kidneys caused by nephritogenic strains of group A (3-hemolytic streptococcus (GAS). The present study aimed to present a large patient cohort of APSGN patients to determine the factors that can be used for predicting the prognosis and progression to rapidly progressive glomerulonephritis (RPGN).Methods The study included 153 children with APSGN that were seen between January 2010 and January 2022. Inclusion criteria were age 1-18 years and follow-up of >= 1 years. Patients with a diagnosis that could not be clearly proven clinically or via biopsy and with prior clinical or histological evidence of underlying kidney disease or chronic kidney disease (CKD) were excluded from the study.Results Mean age was 7.36 +/- 2.92 years, and 30.7% of the group was female. Among the 153 patients, 19 (12.4%) progressed to RPGN. The complement factor 3 and albumin levels were significantly low in the patients who had RPGN (P = 0.019). Inflammatory parameters, such as C-reactive protein (CRP), platelet-to-lymphocyte ratio, CRP/albumin ratio, and the erythrocyte sedimentation rate level at presentation were significantly higher in the patients with RPGN (P < 0.05). Additionally, there was a significant correlation between nephrotic range proteinuria and the course of RPGN (P = 0.024).Conclusions We suggest the possibility that RPGN can be predicted in APSGN with clinical and laboratory findings.Öğe İnsanın günlük aktivitelerinin yeni bir veri kümesi: Derin öğrenme tekniklerini kullanarak sınıflandırma performansı için kıyaslama sonuçları(Gazi Üniversitesi, 2021) Metin, İbrahim Ali; Karasulu, BahadırStudies to classify human activities can contribute to the development of new systems that will facilitate daily life by evaluating the interaction of individuals with their environment. In this study, a novel data set is presented to be used in classifying the activities that individuals perform during the day. First of all, various deep architectural models presented in the study were tested with publicly available datasets well-known in the literature. Afterwards, various classification experiments were carried out by using our novel dataset, which was created with the sensor data collected with the smartphone located onto the belly region of ten volunteer individuals consisting of five males and five females aged between 25 and 55 years. Data of each activity at two different positions were taken, and also, 15 seconds raw data including 4 dynamic and 3 static activities were acquired. With 20 Hz sampling frequency for each activity position, 20 readings are made per signal window in 1 second. Thanks to the software tool developed for the study, various human activities were succesfully classified in experiments by allowing different network parameters and layer selection for the deep learning architectures including recurrent neural network models and convolutional neural network model. The novel dataset contains raw data, as well as, it involves some alternative subsets created with the use of Butterworth filter. As a result of experiments, the classification performance at accuracy rate of 97% to 99% for various activities of individuals was obtained on various datasets. The suitability of using the novel data set in studies on classification and prediction of human activities has been proven.Öğe COVID-19 detection on IBM quantum computer with classical-quantum transfer learning(TUBITAK, 2021) Acar, Erdi; Yılmaz, İhsanDiagnose the infected patient as soon as possible in the coronavirus 2019 (COVID-19) outbreak which is declared as a pandemic by the world health organization (WHO) is extremely important. Experts recommend CT imaging as a diagnostic tool because of the weak points of the nucleic acid amplification test (NAAT). In this study, the detection of COVID-19 from CT images, which give the most accurate response in a short time, was investigated in the classical computer and firstly in quantum computers. Using the quantum transfer learning method, we experimentally perform COVID-19 detection in different quantum real processors (IBMQx2, IBMQ-London and IBMQ-Rome) of IBM, as well as in different simulators (Pennylane, Qiskit-Aer and Cirq). By using a small number of data sets such as 126 COVID-19 and 100 normal CT images, we obtained a positive or negative classification of COVID-19 with 90% success in classical computers, while we achieved a high success rate of 94%–100% in quantum computers. Also, according to the results obtained, machine learning process in classical computers requiring more processors and time than quantum computers can be realized in a very short time with a very small quantum processor such as 4 qubits in quantum computers. If the size of the data set is small; due to the superior properties of quantum, it is seen that according to the classification of COVID-19 and normal, in terms of machine learning, quantum computers seem to outperform traditional computers.Öğe GovdeTurk: A Novel Turkish Natural Language Processing Tool for Stemming, Morphological Labelling and Verb Negation(Zarka Private University, 2021) Yücebaş, Sait; Tintin, RabiaGovdeTurk is a tool for stemming, morphological labeling and verb negation for Turkish language. We designed comprehensive finite automata to represent Turkish grammar rules. Based on these automata, GovdeTurk finds the stem of the word by removing the inflectional suffixes in a longest match strategy. Levenshtein Distance is used to correct spelling errors that may occur during suffix removal. Morphological labeling identifies the functionality of a given token. Nine different dictionaries are constructed for each specific word type. These dictionaries are used in the stemming and morphological labeling. Verb negation module is developed for lexicon based sentiment analysis. GovdeTurk is tested on a dataset of one million words. The results are compared with Zemberek and Turkish Snowball Algorithm. While the closest competitor, Zemberek, in the stemming step has an accuracy of 80%, GovdeTurk gives 97.3% of accuracy. Morphological labeling accuracy of GovdeTurk is 93.6%. With outperforming results, our model becomes foremost among its competitors.Öğe An investigation of the causal relationship between sunspot groups and coronal mass ejections by determining source active regions(Royal Astronomical Society, 2021) Raheem, Abd-ur; Çavuş, Hüseyin; Çoban, Gani Çağlar; Kınacı, Ahmet Cumhur; Wang, Haimin; Wang, Jason T. L.Although the source active regions of some coronal mass ejections (CMEs) were identified in CME catalogues, vast majority of CMEs do not have an identified source active region. We propose a method that uses a filtration process and machine learning to identify the sunspot groups associated with a large fraction of CMEs and compare the physical parameters of these identified sunspot groups with properties of their corresponding CMEs to find mechanisms behind the initiation of CMEs. These CMEs were taken from the Coordinated Data Analysis Workshops (CDAW) data base hosted at NASA's website. The Helioseismic and Magnetic Imager (HMI) Active Region Patches (HARPs) were taken from the Stanford University's Joint Science Operations Center (JSOC) data base. The source active regions of the CMEs were identified by the help of a custom filtration procedure and then by training a long short-term memory network (LSTM) to identify the patterns in the physical magnetic parameters derived from vector and line-of-sight magnetograms. The neural network simultaneously considers the time series data of these magnetic parameters at once and learns the patterns at the onset of CMEs. This neural network was then used to identify the source HARPs for the CMEs recorded from 2011 till 2020. The neural network was able to reliably identify source HARPs for 4895 CMEs out of 14604 listed in the CDAW data base during the aforementioned period.Öğe A quantum edge detection algorithm for quantum multi-wavelength images(World Scientific Publishing, 2021) Şahin, Engin; Yılmaz, İhsanQuantum edge detection is one of the important part of quantum image processing. In this paper, a quantum edge detection algorithm is designed for the quantum representation of multi-wavelength image (QRMW) model. The algorithm includes all stages of filtering, enhancement and detection. The proposed algorithm is also designed to apply any filtering operation to QRMW images, not only for a particular filtering operation. The proposed algorithm aims to solve the problems that quantum edge detection algorithms in the literature have processing only for a particular operator and noise reduction. Moreover, the algorithm aims to perform operations more efficiently by using less resources. Low-pass filter (LPF) smoothing operators are applied in the filtering stage for the noise reduction problem. In order to apply all filtering operations to the image, arithmetic operators that can operate with all signed integers are used in the algorithm. The operators Sobel, Prewitt and Scharr in the enhancement stage and the gradient method in the detection stage are used for both verification of the proposed algorithm and comparisons with the existing algorithms. A method with quantitative outcomes is shown to evaluate the performance of the edge detection algorithms. Analysis of the simulations performed on sample images with different operators. The circuit complexity of the algorithm is presented and the comparisons are made with the existing studies. The superiority of the proposed algorithm and its flexibility to be used in other studies are clearly demonstrated by analysis.Öğe A novel filter feature selection method for text classification: Extensive Feature Selector(SAGE Publications, Early Access) Parlak, Bekir; Uysal, Alper KürşatAs the huge dimensionality of textual data restrains the classification accuracy, it is essential to apply feature selection (FS) methods as dimension reduction step in text classification (TC) domain. Most of the FS methods for TC contain several number of probabilities. In this study, we proposed a new FS method named as Extensive Feature Selector (EFS), which benefits from corpus-based and classbased probabilities in its calculations. The performance of EFS is compared with nine well-known FS methods, namely, Chi-Squared (CHI2), Class Discriminating Measure (CDM), Discriminative Power Measure (DPM), Odds Ratio (OR), Distinguishing Feature Selector (DFS), Comprehensively Measure Feature Selection (CMFS), Discriminative Feature Selection (DFSS), Normalised Difference Measure (NDM) and Max–Min Ratio (MMR) using Multinomial Naive Bayes (MNB), Support-Vector Machines (SVMs) and k-Nearest Neighbour (KNN) classifiers on four benchmark data sets. These data sets are Reuters-21578, 20-Newsgroup, Mini 20-Newsgroup and Polarity. The experiments were carried out for six different feature sizes which are 10, 30, 50, 100, 300 and 500. Experimental results show that the performance of EFS method is more successful than the other nine methods in most cases according to microF1 and macro-F1 scores.Öğe Nonlocal implementation of multi-qubit controlled unitary quantum gates with quantum channel(World Scientific Publishing, 2021) Şahin, Engin; Yılmaz, İhsanQuantum computers are very efficient in terms of speed and security. Decoherence and architectural complexity restrict the control of sensitive quantum information as the number of qubits in a quantum computer increases. Therefore, it is more convenient to make a device with multiple quantum processors with small number of qubits instead of making a device with a large number of qubits quantum processors. The implementation of controlled unitary gates is a problem in such nonlocal systems. The methods in the literature for this problem use entangled qubit pairs, classical communication channels and classical bits. The existing methods perform some unitary operations on the target state for reconstruction after sending information through classical communication channels and applying quantum measurements on control states. In this study, a generalized method for nonlocal implementation of multi-qubit controlled unitary quantum gates with quantum channel is proposed. The proposed method can implement any controlled gate on the control and target qubits that are far from each other in terms of location. The method does not require classical channels and classical bits, any extra unitary operation for reconstruction. The proposed method is both more secure and uses less resources for operations than the other hybrid methods in the literature. Comparisons with existing studies are given in terms of required entangled qubit pairs, classical channels and bits, extra unitary operations for reconstruction the target state, and the advantages of the proposed method are revealed.Öğe Kalp Hastaları İçin Bulut Bilişim Temelli Erken Uyarı Sistemi(Çanakkale Onsekiz Mart Üniversitesi, 2016-12) Eryılmaz, Ömer; Kahraman, İsmail; Şahin, MustafaDünyada ve ülkemizde ölümle sonuçlanan vakaların başında kardiyak aciller yer almaktadır. Kardiyak acillerde zamanında ve etkin acil bakım uygulayabilmek çok önemlidir. Kalp hastalıkları en kısa sürede tanımlanabilmeli, tedaviye yönelik çalışmalar baş- latılmalı ve en yakın sağlık merkezi ile koordinasyon sağlanıp sevkiyatı yapılmalıdır. Bu çalışmada kalp rahatsızlığı olan kişilerin uzaktan takibi için hasta, doktor ve sağlık merkezini kapsayan akıllı bir bilgi sistemi oluşturulmuş ve oluşabilecek herhangi bir anomali durumunda hasta, yakınları, doktor ve ilgili sağlık merkezlerinin erken uyarı amaçlı bilgilendirilmesi sağlanmıştır. Bu kapsamda kalp hastalıklarının tanımlanması sürecinde yoğun kullanılan parametrelerin ölçümü için donanımsal bileşenler EKG cihazı, nabız ölçer ve tansiyon ölçer gibi cihazlardır. Bu bağlamda mevcut çalışmada nabız ölçer cihazı donanımsal ve yazılımsal olarak gerçekleştirilmiştir. Hasta kayıtları için esneklik, ölçeklenebilirlik, performans/fiyat avantajları yanında mobil ve web ortamını tek merkezden kullanmak ve yönetmek için avantajlar sağlayan merkezi bulut sistemi kullanılmıştır. Hastadan elde edilen nabız verileri bulut sistemine hastanın geçmiş kayıtları olarak kayıt edilmiş ve analiz edilmiştir. Bu analizler sonucunda acil durumların otomatik olarak sistem tarafından tespiti ve ilgili yerlere bildirimi gerçekleştirilmiştir.
