Development of Early Stage Diabetes Prediction Model Based on Stacking Approach

Diabetes is a disease that may pose direct or indirect risks in terms of human health. Early diagnosis can minimize the potential harm of this disease to the body and reduce the probability of death. For this reason, laboratory tests are performed on diabetic patients. The analysis of these tests enables the diagnosis of diabetes. The aim of this study is so quickly diagnose diabetes by using data obtained from patients with machine learning methods. In order to diagnose the disease, k-nearest neighbor (k-NN), logistic regression (LR), random forest (RF) models and the stacking meta model which is created by combining these three models were used. The dataset used in the research includes test samples taken from 520 people. The dataset has 17 features, including 16 input features and 1 output feature. As a result of the classification through this dataset, different classification results were obtained from the models. The classification success of the models LR, k-NN, RF and stacking were found to be 91.3%, 91.7%, 97.9% and 99.6%, respectively. F-score, precision and recall performance metrics were utilized for a detailed analysis of the models\u27 classification results. The obtained results revealed that the stacking model has a sufficient level to be used as a decision support system in the early diagnosis of diabetes

FEATURE EXTRACTION AND RECOGNITION ON TRAFFIC SIGN IMAGES

FEATURE EXTRACTION AND RECOGNITION ON TRAFFIC SIGN IMAGESAbstractIt is vital that the traffic signs used to ensure the order of the traffic are perceived by the drivers. Traffic signs have international standards that allow the driver to learn about the road and the environment while driving. Traffic sign recognition systems have recently started to be used in vehicles in order to improve traffic safety. Machine learning methods are used in the field of image recognition. Deep learning methods increase the classification success by extracting the hidden and interesting features in the image. Images contain many features and this situation can affect success in classification problems. It can also reveal the need for high-capacity hardware. In order to solve these problems, convolutional neural networks can be used to extract meaningful features from the image. In this study, we created a dataset containing 1500 images of 14 different traffic signs that are frequently used on Turkey highways. The features of the images in this dataset were extracted using convolutional neural networks from deep learning architectures. The 1000 features obtained were classified using the Random Forest method from machine learning algorithms. 93.7% success was achieved as a result of this classification process.Keywords: Classification, Convolution neural network, Feature extraction, Random forest, Traffic signsTRAFİK İŞARETİ GÖRÜNTÜLERİNDE ÖZELLİK ÇIKARMA VE TANIMAÖzetTrafiğin düzenini sağlamak amacıyla kullanılan trafik levhalarını sürücülerin algılaması hayati önem taşımaktadır. Sürüş esnasında sürücünün yol ve çevre hakkında bilgi edinebilmesini sağlayan trafik levhaları uluslararası standartlara sahiptir. Trafik levhası tanıma sistemleri son zamanlarda trafik güvenliğini arttırmak amacıyla araçlarda kullanılmaya başlamıştır. Makine öğrenmesi yöntemleri görüntü tanıma alanında kullanılmaktadır.  Derin öğrenme yöntemleri, görüntüde yer alan gizli ve ilginç özellikleri çıkarak sınıflandırma başarısını arttırmaktadır. Görüntüler çok sayıda özellik içermektedir ve bu durum sınıflandırma problemlerinde başarıyı etkileyebilmektedir. Ayrıca yüksek kapasiteli donanım gereksinimini de ortaya çıkarabilmektedir. Bu sorunların çözülebilmesi için görüntüden anlamlı özelliklerin çıkarılmasında konvolüsyonel sinir ağları kullanılabilmektedir. Bu çalışmada Türkiye’deki karayollarında sıklıkla kullanılan 14 farklı trafik levhasına ait 1500 görüntü içeren bir veriseti tarafımızca oluşturulmuştur. Bu veriseti kullanılarak derin öğrenme mimarilerinden konvolüsyonel sinir ağları kullanılarak görüntülerin özellikleri çıkarılmıştır. Elde edilen 1000 özellik makine öğrenmesi algoritmalarından Random Forest yöntemi kullanılarak sınıflandırılmıştır. Bu sınıflandırma işlemi sonucunda %93.7 başarı elde edilmiştir.Anahtar Kelimeler: Konvolüsyonel sinir ağları, Özellik çıkarma, Random forest, Sınıflandırma, Trafik işaretleri

Hepatit B enfeksiyonu olan hastalarda yaşanılan zorlukların değerlendirilmesi

Objectives: Although stigma is well defined in people with a chronic disease or condition, it has not been studied much in individuals infected with hepatitis B virus (HBV). The study is one of the first descriptive individual studies conducted on this subject in our country. Our aim in this study was to evaluate the stigma experiences and concerns of individuals living with HBV, their sharing of their illness with the environment, and the state of being affected by their social relationships. Materials and Methods: Patients with hepatitis B surface antigen positivity who were admitted to the infectious diseases outpatient clinic were surveyed through face-to-face interviews. Epidemiological data, stigma experiences and anxiety states, people with whom they shared their illness, the reasons for not sharing, the impairment of social relations were questioned. Results: It was found that 19.5% of 390 individuals infected with HBV who participated in our study were "exposed" to stigma in various ways, and 27.4% were "worried" about experiencing this condition. In research, 19.9% of women, 41.4% of university graduates, and 34.8% of divorced or widowers were found to experience higher stigma (p=0.002, p=0.02 and p<0.001, respectively). It was determined that 56.7% of the participants did not share their illnesses, and this need increased with stigma experiences and anxiety. It was found that individuals mostly shared their disease status with their first-degree relatives (p<0.001). Conclusion: The fact that individuals infected with HBV experience different forms of stigma or experience anxiety suggests that there is a need to investigate these conditions and develop treatment interventions

Radiographic and histopathologic evaluation of radiolucent lesions involving impacted teeth: A multicenter study

Objective: The purpose of this retrospective study was to evaluate the radiographic and histopathologic features of the pathologic lesions associated with an impacted tooth in the maxilla and mandible of patients who were admitted to three different university hospitals located in different cities. Materials and Methods: One hundred one patients (36 females and 65 males) aged between 8 and 67 and who have radiolucent lesions associated with the impacted teeth were included in this study. Data related to the age and gender of the patients, and the findings of cone-beam computed tomography, and histopathologic diagnosis of the lesions were recorded and analyzed. Results: Majority of the lesions were in the posterior region of the mandible (62.4%), related to the mandibular third molars (59.4%), and were diagnosed as a dentigerous cyst. The most common features of the lesions were unilocular radiolucency (91.1%), well-circumscribed (90.1%), and expansive (85.1%). A statistically significant relationship was found between the migration of the impacted tooth/teeth related to the lesion (p<0.05) and the expansion of the lesion (p<0.01) according to gender. A statistically significant relationship was found between the migration of the impacted tooth/teeth related to the lesion (p<0.05) and the histopathological diagnosis of the lesion (p<0.01) according to age groups. Conclusion: Knowing all of the clinical, radiological and histopathological features of the lesions provide the surgeon to reach the correct diagnosis. Thus, the doctors achieve high success in treatment with the right treatment plan.Amaç: Bu retrospektif çalışmanın amacı, farklı şehirlerde bulunan üç farklı üniversite hastanesine başvuran hastaların üst ve alt çenesindeki gömülü diş ile ilişkili patolojik lezyonların radyografik ve histopatolojik özelliklerini değerlendirmektir. Gereç ve Yöntemler: Bu çalışmaya gömülü diş ile ilişkili radyolüsent lezyonu olan 8-67 yaş aralığındaki 101 hasta (36 kadın ve 65 erkek) dahil edildi. Hastaların yaşı ve cinsiyeti, lezyonların konik-ışınlı bilgisayarlı tomografi bulguları ve histopatolojik tanısı ile ilgili veriler kaydedildi ve analiz edildi. Bulgular: Lezyonların çoğu mandibula posterior bölgede (%62,4), mandibular üçüncü molar dişlerle (%59,4) ilişkiliydi ve dentigeröz kist tanısı aldı. Lezyonlarda en fazla görülen özellikler uniloküler radyolüsensi (%91,1), iyi sınırlı (%90,1) ve ekspansif (%85,1) olması idi. Cinsiyete göre lezyon ile ilişkili gömülü diş/dişlerin migrasyonu (p<0,05) ve lezyonun ekspansiyonu (p<0,01) arasında istatiksel olarak anlamlı bir ilişki bulundu. Yaş gruplarına göre lezyon ile ilişkili diş/dişlerin migrasyonu (p<0,05) ve lezyonun histopatolojik tanısı (p<0,01) arasında istatiksel olarak anlamlı bir ilişki bulundu. Sonuç: Lezyonların tüm klinik, radyolojik ve histopatolojik özelliklerinin bilinmesi cerrahın doğru tanıya ulaşmasını sağlar. Böylece hekimler doğru tedavi planı ile tedavide yüksek başarı elde ederler

The genetic history of the Southern Arc: a bridge between West Asia and Europe

By sequencing 727 ancient individuals from the Southern Arc (Anatolia and its neighbors in Southeastern Europe and West Asia) over 10,000 years, we contextualize its Chalcolithic period and Bronze Age (about 5000 to 1000 BCE), when extensive gene flow entangled it with the Eurasian steppe. Two streams of migration transmitted Caucasus and Anatolian/Levantine ancestry northward, and the Yamnaya pastoralists, formed on the steppe, then spread southward into the Balkans and across the Caucasus into Armenia, where they left numerous patrilineal descendants. Anatolia was transformed by intra–West Asian gene flow, with negligible impact of the later Yamnaya migrations. This contrasts with all other regions where Indo-European languages were spoken, suggesting that the homeland of the Indo-Anatolian language family was in West Asia, with only secondary dispersals of non-Anatolian Indo-Europeans from the steppe

Developıng A Software Program Based On Java For Fault Tree Analysıs And Its Applıcatıon To Lpg, Cruıd Oıl And Dıesel Tanks

Petrokimya endüstrisinin üretim ve depolama proseslerinde, insan ölümüne, çevre ve işletmelere ciddi boyutta zarara yol açan ve maddi olarak ciddi kayıplara sebep olabilen büyük endüstriyel kaza anlamında birçok kaza meydana gelmektedir. İlgili direktifler ve mevzuat kapsamında büyük endüstriyel kazaların önlenmesi ve etkilerinin azaltılması amacıyla büyük endüstriyel kaza tehlikelerinin belirlenmesi ve bu tehlikelerden kaynaklanabilecek risklerin değerlendirilmesi gerekmektedir. Bu tez çalışmasında, büyük endüstriyel kaza meydana gelme potansiyeli olan ve petrokimya endüstrisinden seçilen bir akaryakıt depolama tesisinde LPG, Ham Petrol ve Dizel depolama tanklarında meydana gelebilecek sırasıyla BLEVE, Tam Yüzey Yangını ve Havuz Yangını olaylarının kök nedenlerinin belirlenmesi amaçlanmıştır. Bu çalışmada, Hata Ağacı Analizi (HAA) yöntemi kullanılarak risk değerlendirmesi yapılmıştır. Seçilen üç tank için, tepe olay olarak sırasıyla BLEVE, Tam Yüzey Yangını ve Havuz Yangını olayları belirlenmiştir. Bu tepe olaylarına sebep olabilecek tüm alt nedenler belirlenmiş ve sayısal mantık kapıları olan VE/VEYA kapıları ile bağlantı kurularak kök nedenler tespit edilmiştir. Java tabanlı bir program geliştirilmiştir ve LPG, Ham Petrol ve Dizel tankları için uygulanmıştır. Program ile belirlenen tepe olayları ve her bir tepe olayına sebep olan kök nedenler belirlenerek hata ağacı oluşturulduktan sonra, program veri tabanına girilen kök nedenlerin frekans verileri kullanılarak Boolean Matematiği ile her bir tepe olayının frekansı (en yüksek önlem seviyesi) hesaplanmıştır. Her bir tepe olayının kabul edilebilir risk seviyesinde olduğunun kontrolü yapılmıştır. Risk seviyesinin daha düşük olabilmesi için belirlenen nedenler doğrultusunda büyük endüstriyel kazaları önlemeye ve etkilerini en aza indirmeye ilişkin bu sektördeki işletmelere teknik ve idari önlemler sunulmuştur.In the processes of production and storage in petrochemical industry, many accidents called as major industrial accidents, which can lead to human death, economic loss and a great damage to environment occur. For the purpose of preventing major industrial accidents and reducing their effects within relevant regulations and legislation, it is required to determine the hazards of major industrial accidents and assess the risks possibly caused by these hazards. In the thesis study, it is aimed to determine the root causes of BLEVE in LPG, full surface fire in crude oil and pool fire in diesel storage tanks. In this study, risk assessment has been performed by using Fault Tree Analysis. For the selected three tanks, the events of BLEVE, full surface fire and pool fire have been determined as the top event respectively. All sub-causes leading to the top events mentioned above have been determined and root causes have been identified by means of logical structure of AND and OR gates. A software program based on JAVA has been developed for the LPG, Crude Oil and Diesel tanks in the study. After construction of fault tree via the software program by determining root causes leading to each of top events, each of the top events frequency (the highest level of precaution) has been calculated with Boolean Mathematics by using frequency data of root causes processed into the program database. The checkup with respect to the fact that each of the top events is at acceptable risk level is being conducted. In accordance with the determined causes in order to have a lower risk level, technical and administrative measures to hinder major industrial accidents and to reduce the effects have been proposed to the businesses in the relevant sector

Calculation of average fluorescence yields of N shell for elements 38 &#8804; Z &#8804; 101

Bu tez çalışmasında, 38 &#8804; Z &#8804; 101 arası tüm elementler için N tabakasına ait ortalama flöresans verimler (&#982;N) ve 6 keV'da toplam N tabakası X-ışını flöresans tesir kesitleri ( xN &#963; ) teorik olarak hesaplanmıştır. McGuire'nin [1] vermiş olduğu 38 &#8804; Z &#8804; 103 arası 25 element için N tabakasına ait alttabaka flöresans verimler (&#969;i), süper Coster-Kronig geçiş ihtimaliyetleri (Sij) kullanılarak 38 &#8804; Z &#8804; 101 arası tüm elementleri için en küçük kareler metoduyla alttabaka flöresans verimler (&#969;N1&#969;N7) ve süper Coster- Kronig geçiş ihtimaliyetlerinin (Sij) fit değerleri hesaplandı. Bu fit değerlerinden istifade ile N tabakası etkin alttabaka flöresans verimler (&#957;i) hesaplandı. N tabakasına ait ortalama flöresans verimler (&#982;N) türetildi. Ayrıca, 6 keV'da toplam N tabakası X-ışını flöresans tesir kesitleri ( xN &#963; ) teorik olarak hesaplandı. Küçük atom numaralı elementlerde Auger geçiş ihtimaliyeti yüksek, büyük atom numaralı elementlerde ise karakteristik X- ışını yayınlama ihtimaliyeti yüksek olduğundan, atom numarası (Z) arttıkça, N tabakası için ortalama flöresans verimler artmaktadır. N tabakasına ait ortalama flöresans verim ve toplam N tabakası X- ışını flöresans tesir kesit değerlerinde artan Z ile bir artış (&#8764;%2.00-13.50) olduğu gözlendi. Literatürde, N tabakası ve daha üst tabakalara ait teorik ve deneysel veriye rastlanmadığı için, N tabakasına ait ortalama flöresans verim (&#982;N) ve 6 keV'da toplam N tabakası X-ışını flöresans tesir kesitleri ( xN &#963; ) için bir karşılaştırma yapılamamıştır.In this thesis, N shell average fluorescence yields (&#982;N) and total N shell X-ray fluorescence cross-sections ( xN &#963; ) at 6 keV have been calculated theoretically for the elements 38 &#8804; Z &#8804; 101. Fit values of N subshell fluorescence yields (&#969;N1&#969;N7) and super Coster-Kronig transitions probabilities (Sij) for elements 38 &#8804; Z &#8804; 101 have been calculated by using the method of least squares from the N shell fluorescence yields (&#969;i) and super Coster-Kronig transitions probabilities (Sij) which were given by McGuire [1]. The effective subshell fluorescence yields (&#957;i) for N shell have been calculated by using these fit values. The average N shell fluorescence yields (&#982;N) have been derived from the effective fluorescence yields and total N shell X-ray fluorescence cross-sections ( xN &#963; ) at 6 keV have been calculated theoretically. As the atomic number (Z) increases, average fluorescence yields increase for N shell because Auger transition probability increases for low atomic number and the probability of characteristic X-ray emission for high Z. The results show an increase (&#8764;2.00%-13.50%) in N shell average fluorescence yield (&#982;N) and total N shell X-ray fluorescence cross-sections ( xN &#963; ) with increasing Z. There was no the theoretical and experimental data for N shell and higher Shell in literature. Therefore, there was no comparison for average fluorescence yield (&#982;N) and total X-ray fluorescence cross-sections ( xN &#963; ) at 6 keV for N shell

EKSTRÜDE EDİLMİŞ ÜRÜNLERDE AKRİLAMİD İÇERİĞİNİN AZALTILMASI

EKSTRÜDE EDİLMİŞ ÜRÜNLERDE AKRİLAMİD İÇERİĞİNİN AZALTILMAS

3D Reconstruction of Underwater Scenes from Uncalibrated Video Sequences

Underwater is a complex structured environment because of inhomogeneous light absorption and light scattering by the environment. These factors make 3D reconstruction in underwater more challenging. In the literature, only calibrated cameras and predefined camera motions are allowed for 3D reconstruction. In this study, 3D representation of underwater scenes are reconstructed from uncalibrated video sequences.3D reconstruction consists Of the following stages: Image enhancement, feature defection and matching, fundamental matrix estimation, auto-calibration, recovery of extrinsic parameters, rectification, stereo matching and dense reconstruction. In this paper, various algorithms are implemented for each stage and the best performing ones for underwater applications are defined. As a result, a single camera based automatic 3D modelling system, which is reads, to be used in underwater applications, is developed